On April 6, the Maryland Senate concurred with House amendments to SB 541, the Maryland Online Data Privacy Act (MODPA), sending the bill to Governor Moore for signature. If enacted, MODPA could be a paradigm-shifting addition to the state privacy law landscape. While recent state comprehensive privacy laws generally have added to the existing landscape in an iterative fashion by making adjustments to the popular Washington Privacy Act (WPA) framework, MODPA is a significant departure from the status quo. Infused with elements derived from the 2022 proposed federal privacy bill, the American Data Privacy and Protection Act of 2022 (ADPPA), MODPA includes novel provisions concerning data minimization, civil rights, and more. In light of these significant substantive differences, there is an argument that MODPA should be regarded as a distinct third model for state comprehensive privacy laws.
In this blog post, we highlight 10 things to know about MODPA that set Maryland apart in the state privacy law landscape.
1. Novel Data Minimization Rules Create Potential Tension with Purpose Limitation Rule
MODPA’s approach to data minimization—default limitations on the ability to collect personal data—sets Maryland apart in the state privacy landscape. Prior to MODPA, state privacy laws typically restricted the collection and use of personal data to what is adequate, relevant, and reasonably necessary in relation to the disclosed purposes for which the data is processed. California, in its regulations, follows a different rule that provides that purposes for which personal information is collected or processed must be consistent with individuals’ reasonable expectations and that collection and processing must be limited to what is reasonably necessary and proportionate to achieve a disclosed purpose.
MODPA establishes a new data minimization framework that places default limitations on both the collection and the processing of personal data. Influenced by the ADPPA, MODPA provides that a controller shall “limit the collection of personal data to what is reasonably necessary and proportionate to provide or maintain a specific product or service requested by the consumer to whom the data pertains.” This is a substantive limit on the purposes for which a controller may collect personal data. When it comes to processing more broadly, however, MODPA includes the standard purpose limitation rule seen in a majority of the states—unless a controller obtains consent, the controller shall not “process personal data for a purpose that is neither reasonably necessary to, nor compatible with, the disclosed purposes for which the personal data is processed, as disclosed to the consumer.”
The distinct standards for “collection” and “processing” create a potential tension between these rules, given that “process” is defined to include “collecting,” which could be read to mean that a controller can collect personal data when not reasonably necessary if the controller obtains consent.
With respect to sensitive data (which, as discussed below, is defined broadly), MODPA again establishes new substantive limits that differ from those in other states. Under MODPA, controllers are prohibited from collecting, processing, or sharing sensitive data except where the collection or processing is “strictly necessary to provide or maintain a specific product or service requested by the consumer to whom the personal data pertains.” This is different from the states’ existing approaches—California allows individuals to opt-out of unnecessary sensitive data processing, whereas most other states require opt-in consent for sensitive data processing.
This new data minimization paradigm has at least three significant ambiguities:
What are the criteria for assessing when collection, processing, and sharing are ‘reasonably’ or ‘strictly’ necessary?
What does it mean to provide or maintain a product or service?
What does it mean for a product or service to be ‘specifically requested’ by a consumer?
The answers to these questions will have significant impact on businesses, especially with respect to back-end data uses that are not apparent in a business-customer relationship, such as product improvement and the launch of new products and features.
This new paradigm also increases the importance of exceptions and limitations to the law, given that controllers will now face stronger limits on the purposes for which they can collect or process personal data. Section 14–4612, for example, preserves controllers’ and processors’ ability to collect, use, or retain personal data for certain internal uses, such as identifying and repairing technical errors or performing internal operations that are either (1) “reasonably aligned with” the consumer’s reasonable expectations or can be “reasonably anticipated based on the consumer’s existing relationship with the controller,” or (2) compatible with processing data in furtherance providing a specifically requested product or service or performance of a contract. Even if a controller or process is relying on an exception to justify a processing activity, however, that processing must still be both “reasonably necessary and proportionate” to the excepted purpose and “adequate, relevant, and limited to what is necessary in relation to the specific purpose listed.”
In adopting these data minimization provisions, Maryland has forged a new path in state privacy law. This approach could provide significant protections for individuals by limiting the collection and use of personal data to purposes that more closely align with reasonable expectations. On the other hand, this approach could foreclose certain socially beneficial and low-risk processing activities that are ancillary to the business-consumer relationship. As stakeholders wait to see the full impact of this approach develop over time, all eyes will be on other state legislatures currently considering similar such standards.
2. Prohibitions against Selling Sensitive Data, Targeted Ads to Minors, and Selling Minors’ Personal Data
MODPA’s strong data minimization rules are supplemented by additional prohibitions on specific processing activities, including:
selling sensitive data (defined broadly to include exchanges for non-monetary valuable consideration);
processing the personal data of an individual for the purpose of targeted advertising if the controller knew or should have known that the individual is under the age of 18; and
selling the personal data of an individual if the controller knew or should have known that the individual is under the age of 18.
These are flat prohibitions with no specific opt-in consent alternatives. The “should have known” standard for minors’ data also differs from the “wilfully disregards” standard included in other state laws and could arguably be interpreted as requiring age-gating of online products and services, as explored by Husch Blackwell’s David Stauss. These prohibitions are still subject to the exceptions to MODPA found in Section 14–4612, such as the performance of a contract to which a consumer is a party.
3. Novel Civil Rights Protection Applicable to Processing Publicly Available Data
State privacy laws typically prohibit controllers from processing personal data in violation of state or federal laws that prohibit unlawful discrimination. MODPA incorporates an additional civil rights protection derived from the ADPPA that prohibits controllers from collecting, processing, or transferring personal data or publicly available data in a manner that unlawfully discriminatesin or otherwise unlawfully makes unavailable the equal enjoyment of goods or services on the basis of race, color, religion, national origin, sex, sexual orientation, gender identity, or disability,” subject to limited exceptions (including self-testing to prevent or mitigate unlawful discrimination and diversifying an applicant or customer pool). One thing to note is that this provision uses the undefined term “publicly available data” rather than the defined term “publicly available information.” Assuming the drafters meant publicly available information, including processing of that data in this provision is notable given that publicly available information is generally outside the scope of the bill and other state privacy laws. Another notable aspect of this prohibition is that it only prohibits unlawful discrimination, which is potentially a higher threshold than other potential standards, such as all discrimination or unjustified differential treatment.
4. Heightened Protections for Consumer Health Data
2023 was notable for a rise in consumer health privacy laws, including the enactment of the Washington My Health My Data Act (WMHMDA) and the Nevada Consumer Health Data Privacy Law. Connecticut also introduced a novel requirement in 2023 when it passed SB 3, which amended the state’s nascent comprehensive privacy law to include expanded protections for “consumer health data” above and beyond what was already covered by its definition of sensitive data. MODPA incorporates Connecticut-style protections for consumer health data, which it defines as “personal data that a controller uses to identify a consumer’s physical or mental health status” and which includes data related to “gender-affirming treatment or reproductive or sexual health care. Unlike CT SB 3, however, it appears that under MODPA a person must meet the applicability thresholds of the Act to be subject to these provisions. Additionally, because consumer health data is included in the definition of sensitive data, the minimization rule limiting the collecting, processing, or sharing of sensitive data to what is “strictly necessary” to provide or maintain a product of service applies to consumer health data as well. This could mean that MODPA creates stricter requirements for the use of most health information than WMHMDA, which has an opt-in consent alternative to its “necessary” health data processing standard. For more on WMHMDA’s necessity standard, see this recent analysis from Hintze Law’s Kate Black and Felicity Slater and FPF’s Jordan Wrigley and Niharika Vattikonda.
5. Data Protection Assessments May Have Narrower Applicability but Broader Scope
Like most state privacy laws, MODPA will require a controller to conduct and document a data protection assessment (DPA) for each of their processing activities that “present a heightened risk of harm to a consumer.” MODPA’s requirements for conducting a DPA, however, contain a number of unique provisions that could require covered entities to rework their internal strategies for conducting assessments:
Exclusive: Like many states based on the WPA framework, MODPA requires a DPA for processing personal data for targeted advertising, sale of personal data, processing sensitive data, and processing personal data for profiling that presents a reasonably foreseeable risk of certain enumerated harms. However, in contrast to those other states, MODPA provides that heightened risk of harm “means” those activities rather than “includes” those activities. MODPA thus has an exclusive rather than inclusive standard for when a DPA is required, and therefore, the scope of when a DPA is required could be narrower than under other laws.
Algorithms: Under MODPA, a controller shall conduct DPAs for processing activities that present a heightened risk of harm, “including an assessment for each algorithm that is used.” This requirement is novel and, if read strictly (a definition of “algorithm” is not provided), could require covered organizations to conduct hundreds or thousands of assessments.
Necessity & Proportionality: MODPA contains a novel DPA provision that requires controllers to consider “the necessity and proportionality of processing in relation to the stated purpose of the processing.” This requirement ties back to the general data minimization rule that collection of personal data must be “reasonably necessary and proportionate to provide or maintain a specific product or service requested.”
6. Broad and Divergent Definitions
MODPA’s definitions contain a number of unique and divergent definitions compared to other state privacy laws, including—
Biometric Data: The definition of biometric data in MODPA is broad, encompassing data that can be used to uniquely identify a consumer’s identity. This differs from most state privacy laws which instead limit biometric data to include only data that are, or are intended to be, used to identify an individual.
Decisions that Produce Legal or Similarly Significant Effects: MODPA follows the majority of states in allowing individuals to opt out of solely automated profiling in furtherance of decisions that produce legal or similarly significant effects, but MODPA does not include decisions relating to insurance in that definition.
De-identified data: MODPA cross-references the Maryland Genetic Information Privacy Act to define de-identified data. Although that definition is substantially similar to the language found in a majority of state comprehensive privacy laws, it is not identical because it does not address data that can reasonably be used to infer information about or otherwise be linked to a device that may be linked to an identified or identifiable consumer.
Publicly Available Information: MODPA incorporates Utah’s three-part definition of publicly available information, which, in contrast to narrower definitions in states like Connecticut or Delaware, includes information obtained from a person to whom the consumer disclosed the information if the consumer did not restrict that information to a specific audience. Although this broader definition generally exempts more data from coverage under the bill than under other laws, publicly available information is still subject to MODPA’s novel civil rights protection highlighted above. Publicly available information does not include biometric data collection by a business without a consumer’s knowledge.
Sale of Personal Data: MODPA broadens the definition of sale to explicitly include exchanges of personal data to third parties by processors and affiliates of controllers or processors.
Sensitive Data: MODPA’s definition of sensitive data includes many elements seen in laws enacted in recent years (such as data revealing sex life, sexual orientation, or status as transgender or nonbinary). It is also broader than other states’ definitions in a few ways.
In contrast to Connecticut, sensitive data includes data revealing consumer health data (rather than “is” consumer health data).
Sensitive data includes biometric data which, as specified above, is broader than in other state laws.
Sensitive data includes personal data “of a consumer that the controller knows or has reason to know is a child.” This differs from “known child” language seen in other states.
MODPA will apply to persons that either (1) control or process the personal data of at least 35,000 consumers during a calendar year, excluding data processed solely for the purpose of completing a payment transaction, or (2) control or process the personal data of at least 10,000 individuals and derive more than 20% of gross revenue from the sale of personal data. These thresholds are uniquely low relative to Maryland’s population of 6.2 million. For comparison, Colorado has a similar population of 5.9 million but sets thresholds of 100K and 25K, whereas Delaware has similar thresholds of 35K and 10K but a total population of only 1 million.
In addition to the low applicability thresholds, MODPA includes notable entity-level and data-level exemptions. MODPA includes an entity-level exemption for financial institutions and affiliates (and data) subject to GLBA. Additionally, although nonprofits are generally subject to MODPA, there is a specific exemption for non-profits that process or share personal data solely for the purpose of assisting either law enforcement in investigating insurance crime or fraud or “first responders in responding to catastrophic events.” MODPA includes data-level exemptions for data subject to HIPAA, FCRA, FERPA, and personal data collected by or on behalf of a person subject to Maryland’s Insurance article “in furtherance of the business of insurance.”
8. No Fraud Exception for Complying with Opt-out Requests
The Act provides relatively standard consumer rights of access, correction, deletion, portability, and to opt-out of targeted advertising, sales of personal data, and solely automated profiling in furtherance of decisions with legal or similarly significant effects. Unlike other state laws, however, MODPA does not give controllers an explicit right to reject opt-out requests that are suspected to be fraudulent.
9. Enforcement is Vested in the Attorney General, but Other Remedies Provided by Law Are Not Foreclosed
Violations of MODPA are tied to the Maryland Consumer Protection Act, and the Act specifically denies private enforcement under Md. Code Com. Law § 13-408, leaving enforcement solely with the Division of Consumer Protection of the Office of the Attorney General. However, the Act specifies that “[t]his section does not prevent a consumer from pursuing any other remedy provided by law.” This language differs from that seen in other states, some of which say that nothing in the law shall be construed as providing the basis for a private right of action for violations of that law “or any other law.” This provision thus could be interpreted as allowing individuals to bring private suits for violations under other causes of action. Similar concerns were raised by industry members when New Jersey enacted S332 in January.
10. Notice Required for Third-Party Use Inconsistent with Past Promises
MODPA contains a novel provision requiring that “[i]f a third party uses or shares a consumer’s information in a manner inconsistent with the promises made to the consumer at the time of collection . . . , the third party shall provide an affected consumer with notice of the new or changed practice before implementing the new or changed practice,” so as to allow a consumer to exercise their rights under the Act. The scope of this provision is ambiguous as the Act neither defines information nor specifies when a third party’s use or sharing of information is inconsistent with promises made to an individual. Additionally, the notice provision does not specify any requirements with respect to consent (such as allowing an individual to revoke previously given consent).
Conclusion
MODPA could portend a paradigm shift in state privacy laws if policymakers in other states follow suit and venture towards rules that impose default limitations on companies’ ability to collect and use personal data. Much will depend on how MODPA’s novel provisions are interpreted. As David Stauss identified in his analysis of MODPA, the Maryland Attorney General has inherent, permissive rulemaking authority with respect to unfair or deceptive trade practices, so it is possible that clarifying regulations could be issued to guide compliance.
On April 6, Maryland became the second state to pass an Age-Appropriate Design Code when the Maryland Senate concurred with House amendments to SB 571. That bill, if enacted by the Governor, will take effect on October 1, 2024, a year before MODPA would take effect. Stay tuned for FPF’s forthcoming analysis of the Maryland Age-Appropriate Design Code Act.
China’s Interim Measures for the Management of Generative AI Services: A Comparison Between the Final and Draft Versions of the Text
Authors: Yirong Sun and Jingxian Zeng
Edited by Josh Lee Kok Thong (FPF) and Sakshi Shivhare (FPF)
The following is a guest post to the FPF blog by Yirong Sun, research fellow at the New York University School of Law Guarini Institute for Global Legal Studies at NYU School of Law: Global Law & Tech and Jingxian Zeng, research fellow at the University of Hong Kong Philip K. H. Wong Centre for Chinese Law. The guest blog reflects the opinion of the authors only. Guest blog posts do not necessarily reflect the views of FPF.
On August 15, 2023, the Interim Measures for the Management of Generative AI Services (Measures) – China’s first binding regulation on generative AI – came into force. The Interim Measures were jointly issued by the Cyberspace Administration of China (CAC), along with six other agencies, on July 10, 2023, following a public consultation on an earlier draft of the Measures that concluded in May 2023.
This blog post is a follow-up to an earlier guest blog post, “Unveiling China’s Generative AI Regulation” published by the Future of Privacy Forum (FPF) on June 23, 2023, that analyzed the earlier draft of the Measures. This post compares the final version of the regulation with the earlier draft version and highlights key provisions.
Notable changes in the final version of the Measures include:
A shift in institutional dynamics, with the CAC playing a less prominent role;
Clarification of the Measures’ applicability and scope;
Introduction of responsibilities for users;
Introduction of additional responsibilities for providers, such as taking effective measures to improve the quality of training data, signing service agreements with registered users, and promptly addressing illegal content;
Assignment of responsibilities to government agencies to strengthen the management of generative AI services; and
Introduction of a transparency requirement for generative AI services, in addition to the existing responsibilities for providers to increase the accuracy and reliability of generated content.
Introduction
The stated purpose of the Measures, a binding administrative regulation within the People’s Republic of China (PRC), is to promote the responsible development and regulate the use of generative AI technology, while safeguarding the PRC’s national interests and citizens’ rights. Notably, the Measures should be read in the context of other Chinese regulations addressing AI and data, including the Cybersecurity Law, the Data Security Law, the Personal Information Protection Law, and the Law on Scientific and Technological Progress.
Central to the Measures is the principle of balancing development and security. The Measures aim to encourage innovation while also addressing potential risks stemming from generative AI technology, including manipulation of public opinion and disseminate sensitive or misleading information at scale. The Measures also:
Address a range of societal concerns, including data breaches, fraudulent activities, privacy violations, and intellectual property infringements,
Provide mechanisms for oversight inspections, the right to file complaints, and penalties for non-compliance, and
Coordinate different stakeholders involved in generative AI.
The next section provides some context on the finalization process of the Measures.
The final Measures were shaped significantly by private and public input
The initial draft of the Measures was released for public consultation on April 11, 2023. Following the conclusion of the consultation period on May 10, 2023, the final version of the Measures received internal approval from the CAC on May 23, 2023, and were subsequently made public on July 10, 2023 before formally coming into force on August 15, 2023.
Several significant changes in the final version of the Measures appear attributable to feedback from various from industry stakeholders and legal experts. These industry stakeholders and legal experts include leading tech and AI companies such as Baidu, Xiaomi, SenseTime, YITU, Megvii, and CloudWalk, as well as research institutes affiliated with authorities such as the MIIT. The stakeholders’ input, including public statements on the draft Measures (which were referred to in FPF’s earlier guest blog) appear to have played a role in influencing the revisions made in the final version of the Measures.
In addition, certain changes may also have been influenced by industry policies and standards at the central and local government levels. In particular, between May 2023 and July 2023, China’s National Information Security Standardization Technical Committee (also known as “TC260”) published two “wishlists” (here and here), outlining 48 upcoming national recommended standards. Among these standards, three were specifically focused on generative AI, with the aim of shaping the enforcement of the requirements specified in the final version of the Measures.
The next few paragraphs highlight changes to the overall contours of the Measures.
A key change in the final Measures is the allocation of regulatory responsibility for generative AI
A major difference between the draft and final versions of the Measures is in the allocation of administrative responsibility for generative AI. The final version of the Measures allowed for greater collaboration amongst public institutions compared to the draft version, with the CAC playing a less prominent role. The other six agencies involved in issuing the final version of the Measures are the National Development and Reform Commission (NDRC); the Ministry of Education; the Ministry of Science and Technology (MoST); the Ministry of Industry and Information Technology (MIIT); the Ministry of Public Security; and the National Radio and Television Administration.
Notably, the task to promote AI advancement amid escalating concerns is to be overseen by authorities other than the CAC, such as MoST, MIIT, and NDRC.
Another significant difference is the inclusion of three pro-business provisions – namely, Articles 3, 5, and 6 – in the final version of the Measures. These Articles provide as follows:
Article 3: “The state is to adhere to the principle of placing equal emphasis on development and security, merging the promotion of innovation with governance in accordance with law; employing effective measures to encourage innovation and development in generative AI, and carrying out tolerant and cautious graded management by category of generative AI services.” [emphasis added]
Article 5: “Encourage the innovative application of generative AI technology in each industry and field, generate exceptional content that is positive, healthy, and uplifting, and explore the optimization of usage scenarios in building an application ecosystem.
Support industry associations, enterprises, education and research institutions, public cultural bodies, and relevant professional bodies, etc. to coordinate in areas such as innovation in generative AI technology, the establishment of data resources, applications, and risk prevention.” [emphasis added]
Article 6: “Encourage independent innovation in basic technologies for generative AI such as algorithms, frameworks, chips, and supporting software platforms, carry out international exchanges and cooperation in an equal and mutually beneficial way, and participate in the formulation of international rules related to generative AI.
Promote the establishment of generative AI infrastructure and public training data resource platforms. Promote collaboration and sharing of algorithm resources, increasing efficiency in the use of computing resources. Promote the orderly opening of public data by type and grade, expanding high-quality public training data resources. Encourage the adoption of safe and reliable chips, software, tools, computational power, and data resources.” [emphasis added]
These provisions impose fewer obligations on generative AI service providers than those in the draft version of the Measures. They emphasize the balance between development and security in generative AI, the promotion of innovation while ensuring compliance with the law, support for the application of AI across industries to generate positive content, and collaboration among various entities. They also emphasize independent innovation in AI technologies, international cooperation, and the establishment of infrastructure for sharing data resources and algorithms.
These shifts may be attributed to the above-mentioned feedback received on the draft version of the Measures from industry stakeholders and legal experts.
This article now turns to changes in specific provisions in the final Measures and their implications.
1. The Measures see significant changes in respect of their domestic and extraterritorial applicability
The Measures narrow the scope of “public” by excluding certain entities and service providers not providing services in PRC
The Measures apply to organizations that provide generative AI services to “the public in the territory of the People’s Republic of China”. While the Measures do not define “generative AI services”, Article 2 clarifies that the Measures apply to services that use models and related technologies to generate text, images, audio, video, and other content.
The Measures appear to address some concerns raised in the previous article about the ambiguity surrounding the undefined term “public”. For example, one of the questions raised in the previous article (in respect of the draft Measures) was whether a service licensed exclusively to a Chinese private entity for internal use would fall within the scope of the Measures, considering scenarios where a generative AI service might be made available only to certain public institutions or customized for individual customers. The Measures appear to partially address this ambiguity by removing certain entities from the scope of “the public”. Specifically, Article 2 now clarifies that the Measures do not apply to certain entities (industrial organizations, enterprises, educational and scientific research institutions, public cultural institutions, and related specialized agencies) if they research, develop, and use generative AI technologies but do not provide generative AI services to the public in the PRC. Further clarification may be found in an expert opinion published on the CAC’s public WeChat account supporting the internal use of generative AI technologies and the vertical supply of generative AI technologies among these entities.
This change also significantly narrows the scope of the Measures compared with other existing Chinese technology regulations. In comparison, the rules on deep synthesis and recommendation algorithms apply to any service that uses generative AI technologies, regardless of whether these services are used by individuals, enterprises or “the public”.
Future AI regulation in China may not share the Measures’ focus on “the public”. For instance, the recent China AI Model Law Proposal, an initiative of the Chinese Academy of Social Sciences (CASS) and a likely precursor to a more comprehensive AI law, does not appear to have such a limitation on its scope.
The Measures now have extraterritorial effect to address foreign provision of generative AI services to PRC users
The Measures also appear to have been tweaked to apply extraterritorially. Specifically, Article 2 provides that the Measures apply to a generative AI service so long as it is accessible to the public in the PRC, regardless of where the service provider is located.
This change appears to have been prompted by users trying to circumvent the application of the Measures on generative AI service providers based overseas. Specifically, to avoid compliance with Chinese regulators, several foreign generative AI service providers have limited access to their services from users in the PRC, such as by requiring foreign phone numbers for registration or requiring international credit cards during subscription. In practice, however, users have been able to access the services of these foreign generative AI service providers by following online tutorials or purchasing foreign-registered accounts on the “black market“. For example, though ChatGPT does not accept registrations from users in China, ChatGPT logins were available for sale on Taobao shortly after its initial release. Such activity has drawn the attention of the Chinese government, which had to take enforcement action against such platforms even before the Measures were formulated.
In practice, CAC is expected to adopt a “technical enforcement” strategy against foreign generative AI services. Article 20 of the Measures empowers the CAC to take action against foreign service providers that do not comply with relevant Chinese regulations, including the Measures. Under this provision, the CAC may notify relevant agencies to take “technical measures and other necessary actions” to block Chinese users’ access to these services. A similar provision is found in the Article 50 of the Cybersecurity Law, which addresses preventing the spread of illegal information outside of the PRC.
2. The Measures relax providers’ obligations while assigning users with new responsibilities
As elaborated below, the CAC adjusted the balance of obligations between generative AI service providers and users in the final version of the Measures. To recap, Article 22 of the final version of the Measures defines “providers” as companies that offer services using generative AI technologies, including those offered through application programming interfaces (APIs). It also defines “users” as organizations and individuals that use generative AI services to generate content.
The Measures adopt a more relaxed stance on generative AI hallucination
The Measures seek to address hallucinations of generative AI in three ways.
First, the Measures shift focus from outcome-based to conduct-based obligations for providers. Previously, the draft version of the Measures adopted a strict compliance approach, while the final version of the Measures adopted an approach focused on actions taken by generative AI service providers to address hallucinations, a more flexible approach focusing on the duty of conduct. In the draft version of the Measures, Article 7 required providers to ensure the authenticity, accuracy, objectivity and diversity of the data used for pre-training and optimization training. However, the final version of the Measures has softened this stance, expecting providers simply to “take effective measures to improve” the quality of data. This revision recognizes the technical challenges of developing generative AI, including the heavy reliance on data made available on the Internet (which makes ensuring the authenticity, accuracy, objectivity and diversity of the training data practically impossible).
Second, the Measures no longer require generative AI service providers to prevent “illegal content” (which is not defined in Article 14, but is likely to refer to “content that is prohibited by laws and administrative regulations” under Article 4.1) from being re-generated within three months. Instead, Article 14.1 of the Measures merely requires providers to immediately stop the generation of illegal content, cease its transmission, and remove it. The Measures also require generative AI service providers to report the illegal content to the CAC (Article 14).
The Measures relax penalties for generative AI service providers, but mandate other regulatory requirements
The Measures relax penalties for violations, notably removing all references to service termination or fines. Specifically, Article 20.2 of the draft Measures had provided for suspension or termination or generative AI services and the imposition of fines between 10,000 to 100,000 yuan where generative AI service providers refused to cooperate or committed serious violations. However, Article 21 of the Measures merely provides for suspension of services.
The relaxed penalty regime, however, appears to be balanced against the imposition of mandatory security assessment and algorithm filings in certain cases. Article 17 of the Measures requires generative AI service providers providing generative AI services “with public opinion properties or the capacity for social mobilization” to carry out security assessments and file their algorithms based on the requirements set out under the “Provisions on the Management of Algorithmic Recommendations in Internet Information Services” (which regulate algorithmic recommendation systems in, inter alia, social media platforms). This targeted approach thus avoids a blanket requirement for all services to undergo a security assessment based on a presumption of potential influence on the public.
While the practical impact of this added assessment and filing requirement remains unclear, it is notable that by September 4, 2023 (less than a month after the Measures came into force), it was reported that eleven companies had completed algorithmic filings and “received approval” to provide their generative AI services to the public. Given that these filings are usually also tied to a security assessment, his development suggests that the companies had also passed their security assessments. From the report, however, it is unclear whether these companies were required under the Measures to file their generative AI services; some may have voluntarily completed these processes to reduce future compliance risks.
The Measures also adopt narrower, albeit more stringent, inspection requirements. Under Article 19, when subject to “oversight inspections”, generative AI service providers are required to cooperate with the relevant competent authorities and provide details of the source, scale and types of training data, annotation rules and algorithmic mechanisms. They are also required to provide the necessary technical and data support during the inspection. This appears to have been narrowed from its corresponding provision in the draft Measures (specifically, Article 17 of the draft Measures), which also required generative AI service providers to provide details such as “the description of the source, scale, type, quality, etc. of manually annotated data, foundational algorithms and technical systems” on top of those required under Article 19. However, Article 19 introduces greater stringency by explicitly requiring vendors to provide the actual training data and algorithms, as opposed to the draft version under the draft Article 17, which only required descriptions. Article 19 also introduces a section outlining the responsibilities of enforcement authorities and staff in relation to data protection.
The Measures also introduce provisions that impact users of generative AI services
The Measures introduce provisions that impact the balance of obligations between generative AI service providers and their users in three main areas:
1. Use of user input data to profile users: Article 11 contains a notable difference between the final and draft version of the Measures as regards the ability for generative AI service providers to profile users based on their input data. Specifically, while the draft Measures had strictly prohibited providers from profiling users based on their input data and usage patterns, this restriction is noticeably absent in the final Measures. The implication appears to be that generative AI service providers now have greater leeway to utilize users’ data input to profile them.
2. Providers to enter into service agreements with users: The second paragraph of Article 9 requires generative AI service providers to enter “service agreements” with users that clarify their respective rights and obligations. While the introduction of this provision may indicate a stance towards allowing private risk allocation, it is still subject to several limitations. First, this provision should be read in conjunction with the first paragraph of Article 9, which states that providers ultimately “bear responsibility” for producing online content and handling personal information in accordance with the law. Thus, the Measures do not permit providers to fully shift liability to users via service agreements. Second, even when the parties outline their respective rights and obligations, whether they can allocate their rights and obligations fairly and efficiently will depend on various factors, such as the resources available to them and the existence of information asymmetries between parties.
3. Responsibilities of Users: Article 4(1) appears to extend obligations to users to ensure that generative AI services “(u)phold the Core Socialist Values”. This means that users must also refrain from creating or disseminating content that incites subversion, glorifies terrorism, promotes extremism, encourages ethnic discrimination or hatred, and any content that is violent, obscene, pornographic, or contains misleading and harmful information. This provision is significant given that the draft Measures did not initially include the obligations of users.
3. The Measures assign responsibility to generative AI service providers as producers of online information content, although the scope of obligation remains unclear
Under Article 9, the Measures state that generative AI service providers shall bear responsibility as the “producers of online information content (网络信息内容生产者)”. This terminology aligns with the CAC’s 2019 Provisions on the Governance of the Online Information Content Ecosystem (2019 Provisions), in which the CAC outlined an online information content ecosystem consisting of content producers, content service platforms, and service users, each with shared but distinct obligations in relation to content. In its ‘detailed interpretation’ of the 2019 Provisions, the CAC defined content producers as entities (individuals or organizations) that create, reproduce, and publish online content. Service platforms are defined as entities that offer online content dissemination services, while users are individuals who engage with online content services and may express their opinions through posts, replies, messages, or pop-ups.
This allocation of responsibility as online information content producers under the Measures can be contrasted with the position under the draft Measures, which referred to generative AI service providers as “generated content producers (生成内容生产者)”. This designation was legally unclear, as it was a new and undefined term.
However, the legal position following this allocation of responsibility under the Measures is still unclear. Unlike content producers defined under the 2019 Provisions, generative AI service providers have a less direct relationship with the content produced by their generative AI services (given that content generation is not prompted by these service providers, but by their users)
To further complicate matters, Article 9 also imposes “online information security obligations” on generative AI service providers. These obligations are set out in Chapter IV of China’s Cybersecurity Law. This means that the scope of generative AI service providers’ online information security obligations can only be determined by jointly reading the Cybersecurity Law, the Measures, the 2019 Provisions, as well as user agreements between generative AI service providers and their users.
In sum, while there is slightly greater legal clarity on generative AI service providers’ responsibilities as regards content generated by their services, more clarity is needed on the exact scope of these obligations. It may only become clearer when the CAC carries out an investigation under the Measures.
Conclusion: While clearer than before, the precise impact of the Measures will only be fully understood in the context of other regulations and global developments.
Notwithstanding the greater clarity provided in the Measures, their full significance cannot be understood in isolation. Instead, they need to be read closely with existing laws and regulations in China. These include existing regulations introduced by the CAC on recommendation algorithms and deep synthesis services. Nevertheless, the Measures will give the CAC additional regulatory firepower to deal with prominent societal concerns around algorithmic abuses, youth Internet addiction, and issues such as deepfake- related fraud, fake news, and data misuse.
Further, while China’s AI industry contends with the Measures and its implications, they may soon have to contend with another regulation: an overarching comprehensive AI law. In May 2023, China’s State Council discreetly announced plans to draft an AI Law. This was followed by the release of a draft model law by the Chinese Academy of Social Sciences, a state research institute and think tank. Key features of the model law include a balanced approach to development and security through an adjustable ‘negative list,’ the establishment of a National AI Office, adherence to existing technical standards and regulations, and a clearer delineation of responsibilities within the AI value chain. In addition, the proposed rules indicate strong support for innovation through the introduction of preemptive regulatory sandboxes, broad ex post non-enforcement exemptions, and various support measures for AI development, including government-led initiatives to promote AI adoption. In addition, the impact of the Measures will need to be studied alongside international developments, such as the EU AI Act and the UK’s series of AI Safety Summits. Regardless of how these international developments unfold, it is clear that the Measures – and other regulations introduced by the CAC on AI – are helping it build a position of thought leadership globally, as seen from the UK’s invitation to China to its inaugural AI Safety Summit. As governments around the world rush to comprehend rapid generative AI developments, China has certainly left an impression for being the first jurisdiction globally to introduce hard regulations on generative AI.
Two New Apple and Google Platform Privacy Requirements Kicking In Now
Apple’s important mandatory requirements affecting iOS apps are about to kick in, and Google’s new requirements for publishers and advertisers have just gone into effect. Accurately implementing these requirements calls for close cooperation between the legal, privacy, and ad ops teams.
Apple’s Privacy Manifests
At WWDC 2023, Apple announced privacy manifests, signatures for SDKs, and required reason APIs. In early 2024, Apple began requiring a privacy manifest for every new or updated app and every third-party Software Development Kit (SDK) in the Apple App Store. The privacy manifest must include four pieces of information:
The type of data collected by the app or SDK.
How the data collected will be used by the app or the SDK.
Whether the data are linked to the user.
Whether the data are used for tracking, as defined by Apple.
What are Privacy Manifests, and what benefits do they provide?
Privacy Manifests are an important tool for third-party SDK developers and app developers to communicate critical information about their privacy practices with app developers and Apple. Privacy manifests describe in detail their use of data and select system APIs, called “required reason APIs,” which may require collaboration with legal teams to ensure accurate reporting. Data categories include Contact Information, Health and Fitness, Financial Information, Location, Search History, User Content, Purchases, and a category for Other Data Types not covered in one of the defined categories. The data collected in each category should be assigned a defined purpose in the property file. Example purposes include: App Functionality, Analytics, and Third-party Advertising. A defined “other purposes” category exists as a catch-all.
Privacy Manifests provide several benefits once defined. First, they build on App Tracking Transparency (ATT) in that any network requests to any of the tracking domains made when the user has chosen not to be tracked will automatically fail. Building this into the platform ensures that apps or SDKs cannot accidentally violate user consent because it will actually be impossible for the app to complete the network request. App developers who are unaware of the tracking third-party SDKs do may no longer have to worry and can simply state which tracking domains they know they need to use.
Second, privacy manifests allow developers and Apple to know why third-party SDKs and apps are using select system APIs. This is possible because every developer must specify their reason for needing to use these system APIs. Functionally, this reason is specified in a similar manner to data categorization and use described above. Instead of defined data categories and purposes, developers must select a defined reason for using any of the APIs defined in the developer documentation of the privacy manifest feature. These requirements will start being enforced on May 1st.
The goal of the “required reason” API feature may be intended to prevent software fingerprinting, which is a type of tracking that uses differences in preferences, settings, and hardware capabilities to uniquely identify users. Consider the use of an API that returns information on how much space is left on the file system. This could be done to ensure the space available is enough for a large network transfer, but it could also be done as a data point to uniquely identify a device. The former is an acceptable reason that can be specified as such in a privacy manifest, whereas the latter may raise privacy implications or violate platform guidelines.
Third, organizations implementing privacy manifests can generate a Privacy Report by automatically combining the application’s privacy manifest with all of the privacy manifests of the third-party SDKs used by that app. The report is a PDF that describes data and API uses broken down by category (e.g., contact information, health and fitness, etc). It does not replace Apple’s Privacy Nutrition Labels in the App Store, but can be used by organizations as a reference when making those assessments.
Finally, Apple has defined and will maintain a list of third-party SDKs that require a privacy manifest and an application signature. Developers have had to be extremely cautious in adopting new SDKs because they are responsible for all the code in their app as well as the code in third-party SDKs included in their app. The goal of combining privacy manifests with an application signature is to improve the privacy and security of the software supply chain by helping developers determine when data practices have changed and respond appropriately to those changes. For example, developers may choose to update their Privacy Nutrition Label or replace a third-party SDK that no longer has acceptable data practices.
How should developers prepare for this update?
App developers who want to remain in the App Store must prepare a Privacy Manifest. Some aspects of the privacy manifest will be quite straightforward, like uses of data and APIs that are part of the software’s core functionality and clearly fit into the defined categories. Other aspects may not be immediately obvious. Therefore, developers should be proactive in reaching out to the appropriate people within their organization to ensure they provide the most accurate categorization possible. The goal is clear: the privacy manifest should be a comprehensive report on all data used by the application, but it is not prose text, just a categorization of data collection and usage rationale based on the available defined categories and purposes available in the Privacy Manifest specification.
Google’s Consent Mode v2
Google began enforcing changes to its advertising platforms in Europe starting March 2024. These changes require publishers to update to Consent Mode version 2 in either a basic or an advanced configuration.
A brief history and description of Consent Mode and Consent Mode v.2
Consent Mode was released in 2020 as part of Google Tag Manager, a tool available to publishers using Google Advertising services that provides publishers with an optional set of controls for advertising and analytics tags. Consent Mode helps publishers to communicate user consent status to Google such that it can guide future interactions with any person, such as tracking or advertising. Consent Mode works with Consent Management Platforms (CMPs) to provide more options to publishers seeking to comply with European data protection regulations in their advertising technology stack, including advertising and analytics tags for both Google and third parties. Google Ads also supports the IAB’s Transparency and Consent Framework (TCF), and recommends implementing either TCF or Consent Mode to communicate consent, but not both. If both are implemented, Google respects the most conservative setting communicated, and their recommendation to implement only one of these two options is driven primarily by performance considerations.
In late 2023, Google released Consent Mode version 2, an update that was designed to provide more nuance in recording an individual’s preferences as well as in reaction to legal updates in Europe. Specifically, Consent Mode version 2 introduces two new parameters: ad_user_data, which captures consent for personalized advertising, and ad_personalization, which captures consent for remarketing. These parameters do not have an impact on how tags operate on the publisher site and only communicate how user data can be used for advertising to Google.
By way of comparison, the parameters from Consent Mode version 1 are ad_storage, which enables the storage of identifiers for advertising on both web and mobile platforms, and analytics_storage, which enables the storage of identifiers for analytics on both web and mobile platforms. So, one way to think about these changes is to think of the tags from Consent Mode version 1 as qualifiers for which identifiers can be stored and the tags from Consent Mode version 2 as instructions for Google on how to process the data collected.
With the new parameters introduced in Consent Mode version 2, Google also introduced two new configurations: a Basic configuration that prevents any loading of Google’s tags without user consent, and an Advanced configuration that loads Google’s tags prior to user consent but only sends a cookieless ping until user consent is obtained. The Advanced configuration can be customized for each advertiser tag. Sites based on Consent Mode and seeking to ensure that tags are always available to collect information with consent must implement either Basic or Advanced Consent Mode version 2 configuration.
What should publishers using Google advertising services do to comply in response?
First, publishers hosting a site with users in the European Economic Area (EEA) should, at an absolute minimum, implement Consent Mode version 2 in its Basic configuration.
If you have done nothing else, a Basic configuration of Consent Mode is a relatively quick way to ensure that you are not collecting data without user consent.
Second, publishers can create an Advanced configuration with their advertising and marketing team. Advanced configurations are capable of more nuanced privacy controls that may more efficiently achieve advertising goals. This approach can include AI modeling, templates for different consent management platforms, and per-advertiser configuration of tags. The details of a custom configuration are outside the scope of this post, but an Advanced configuration may prove to be the best option available for many publishers.
Summary
European data protection requirements and related DPA enforcement and court decisions continue to shape the technology and policy interactions between different stakeholders in the ad tech ecosystem. Obligations that large platforms have under DSA, DMA, and other EU digital strategy developments will continue to drive new platform obligations. Google began enforcing Consent Mode v2 in March, and Apple will start fully enforcing their privacy manifest requirements on May 1st. Both of these features will be implemented by developers, but both of them have legal implications that likely require detailed privacy review.
FPF Submits Comments to the Office of Management and Budget on AI and Privacy Impact Assessments
On April 1, 2024, the Future of Privacy Forum filed comments to the Office of Management and Budget (OMB) in response to the agency’s Request for Information on how privacy impact assessments (PIAs) may mitigate privacy risks exacerbated by AI and other advances in technology. The OMB issued the RFI pursuant to the White House’s Executive Order 14110 on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.
As privacy impact assessments are a well-established means for both public and private entities to assess privacy risks in their services, products, and programs, there is a tremendous opportunity for federal agencies to apply learnings from existing data privacy to the challenges that AI presents as a rapidly evolving technology.
In our submission, FPF provides several recommendations to the OMB, including:
1. Clearly defining the scope of PIAs for AI to explicitly encompass considerations of all risks posed by the processing of personal data, including algorithmic discrimination;
2. Recognizing that risks addressed in a PIA, including discrimination risks, should be complementary to, and neither a replacement nor a repetition of, a comprehensive AI risk assessment or other AI-related assessment; and
3. Ensuring that the scope and substance of a PIA for AI tools account for role-specific responsibilities and capabilities in the AI system lifecycle.
Given that AI can create risks for individuals, communities, and societies, it is imperative to ensure that organizations perform a risk analysis on their use of AI tools, especially when such tools are used to make consequential decisions.
“Whether conducted by the public sector, private companies, or other entities, privacy impact assessments can play an important role in evaluating and mitigating certain risks associated with technology. As the federal government now looks to determine the usefulness of privacy impact assessments for responsible AI governance and development, FPF looks forward to continuing to provide insights to policymakers and companies alike as they grapple with the unique privacy challenges associated with the use of AI tools and other emerging technologies.”
– Anne J. Flanagan, FPF Vice President for Artificial Intelligence
FPF Celebrates 15 Years! Spring Social Marks Board Transition as Data Protection Leaders Toast to FPF’s Success
Leaders in Data Protection Take Center Stage at FPF’s Spring Social
The week started with FPF’s 15th Anniversary Spring Social, where FPF CEO Jules Polonetsky thanked FPF’s Board Chair and Founder Chris Wolf, who served for 15 years, and welcomed FPF’s new Board Chair, Alan Raul. Three leading data protection regulators lauded FPF’s effectiveness in supporting their work, in the U.S. and globally. Remarks were delivered by Denise Wong (Deputy Commissioner at the Personal Data Protection Commission of Singapore), Wojciech Wiewiorowski (European Data Protection Supervisor), and Rebecca Kelly Slaughter (Commissioner at the Federal Trade Commission).
FPF’s Board, Advisory Board, and supporters were joined at the event by senior White House staff, leaders at the Federal Trade Commission, Commerce Department, House and Senate staff, state legislators and enforcement agency staff, and representatives of more than a dozen data protection authorities globally.
FPF Activities during IAPP GPS Engage Stakeholders, Launch India Focus, and Highlight Staff Experts
As in years past, FPF took part in the 2024 IAPP Global Privacy Summit in Washington, D.C., which brings together thousands of privacy pros and, most notably, some of FPF’s closest stakeholders to host a week of exciting events while FPF experts participated in GPS panel sessions. And an ‘I Love Privacy’ thank you to those who visited FPF’s booth for our latest expert resources on everything from youth privacy to ad tech to AI!
FPF Hosts India Roundtable
FPF hosted an all-star group of thought leaders on India’s data protection and digital policy landscape at its Washington, D.C., office. The roundtable featured Rahul Matthan, Partner at Trilegal in Bengaluru, Monika Tomczak-Gorlikowska, Chief Privacy Officer at Prosus, and FPF’s Senior Fellow for India Malavika Raghavan. This unique member event showcased our experts as they discussed in detail the state of play of digital policymaking in India, focusing on the next stages of implementation of the new Digital Personal Data Protection Act (DPDPA). FPF has expanded our on the ground work in India and is working with our APAC Council members to plan future activities.
FPF also hosted leadership breakfasts and lunches for our senior stakeholders, as well as informal discussions and receptions with our legislative and health teams.
FPF Experts at GPS Workshops and Panel Sessions
Meanwhile, FPF staff experts participated in nine GPS workshops and sessions, including:
FPF’s Keir Lamont, Tatiana Rice, and Jordan Francis hosted a workshop on ‘The State of U.S. Privacy Law,’ where participants were brought up to speed on the latest developments in U.S. state privacy law. At the same time, expert panelists identified nuances and differences in the recently passed data privacy laws.
Bailey Sanchez and Jim Siegl, CIPT, CIPM participated in a workshop on ‘The State of Play: Compliance with Kids and Teens Privacy Law’ where attendees learned about the core obligations of recently passed laws, tips for maintaining an advertising program that aligns with kids advertising requirements, an overview of age assurance, and an understanding of the kids and teens privacy policy landscape.
Bailey Sanchez took part in ‘The State of Play: An Overview of Kids and Teens Privacy in the U.S.’ which covered recent and expected developments in children’s privacy and online safety legislation in the United States, including considerations that go into crafting legislation at the state level, what the FTC’s priorities are in this space, how companies are working with policymakers to pass legislation that will have positive outcomes for kids, and civil society’s role. Bailey was joined by Senator James Maroney from Connecticut’s 14th District and leading experts from the FTC, Google, and Hogan Lovells.
Stacey Gray moderated ‘How to Evaluate Novel Advertising Solutions With Privacy Enhancing Technologies,’ which featured an FPF discussion draft of a detailed rubric that experts, advocates, and policymakers can use to objectively compare different novel advertising systems, from browser-based APIs to data-clean rooms. The expert panel explored the rubric’s elements and how privacy professionals can use it to conduct informed evaluations of emerging advertising systems.
Keir Lamont took part on the panel ‘Federal Privacy Legislation: Obstacles and Opportunities” to discuss the state of legislative efforts in Congress, breaking down the substance of current proposals and the political and policy disagreements that have become barriers to enactment. Panelists also covered the need for federal privacy legislation, the relationship between current proposals and existing laws, the potential impact on emerging technologies such as artificial intelligence, and the debates over preemption, enforcement mechanisms, and other topics.
Zoe Strickland at ‘The Role that Consumer Consent Plays in the Future of Trusted Commerce’ participated in a discussion on the evolving landscape of consumer trust in commerce, specifically around the crucial theme of consumer consent, and will explore its implications not only for consumers but also for businesses and regulatory bodies.
Aaron Massey sat on an expert panel at ‘Mad Men and the Metaverse: Opportunities and Challenges in Immersive Advertising’ where panelists presented a provocative, future-oriented discussion of the possibilities of immersive advertising tempered by lessons learned from the past and the current advertising policy and compliance landscape.
FPF CEO Jules Polonetsky moderated ‘PETS: How Can We Drive Progress? National Strategies and Regulator Perspectives’ which convened leaders of national PETs strategies, leading regulators and experts to assess the barriers and potential for rapid adoption of PETs across a range of use cases.
Malavika Raghavan participated in ‘The Monsoon Has Arrived. India’s New Digital Personal Data Protection Act (DPDPA),’ focused on the new Digital Personal Data Protection Act for India that was adopted in August 2023. Speakers discussed the status of the implementation of the DPDPA as of April 2024, how this new act impacts the global privacy agendas, when it is supposed to enter full effect, and what stages of operational implementation are.
We hope you enjoyed this year’s IAPP Global Privacy Summit as much as we did! If you missed us at our booth, visit FPF.org for all our reports, publications, and infographics. Follow us on Twitter/X, and LinkedIn, and subscribe to our newsletter for the latest.
Consumer Acceptance, Transparency, and Unique Privacy Considerations at the Forefront of FPF’s Discussion on Privacy and Vehicle Safety Systems
On March 21, the Future of Privacy Forum (FPF) hosted a conversation on “Driving the Conversation on Privacy and Vehicle Safety Systems” to discuss the future of certain technologies in vehicles. The panel discussion was moderated by Adonne Washington, FPF Policy Counsel for Data, Mobility, and Location, and included Hilary Cain (Senior Vice President for Policy at the Alliance for Automotive Innovation), Kristin Kingsley (Director of Program Development and Outreach at the Automotive Coalition for Traffic Safety), and William Wallace (Associate Director of Safety Policy at Consumer Reports). The event followed the launch of FPF’s new report, “Vehicle Safety Systems: Privacy Risks and Recommendations,” which focuses on Advanced Driver Assistance Systems (ADAS), Driver Monitoring Systems (DMS), and Impairment Detection Technologies.
FPF CEO Jules Polonetsky provided welcome remarks ahead of the panel discussion and highlighted how personal data and privacy are and will be implicated in new automotive technologies. In framing the discussion, he explained how many of these technologies, particularly impairment detection technologies, are so early in their development that there is more room for industry, civil society, regulators, policymakers, and other stakeholders to build out a consensus-driven framework that will guide their implementation and serve as a model for other emerging safety technologies.
Washington set the stage for FPF’s report and the panel discussion, noting the January 2024 Advanced Notice of Proposed Rulemaking (ANPRM) on Advanced Impaired Driving Prevention Technologies from the National Highway Traffic Safety Administration (NHTSA). The ANPRM comes out of the Bipartisan Infrastructure Law passed in 2021. FPF continues to investigate the privacy implications of impairment detection systems and other driver safety systems, such as those used for lane-keeping assist functions or to detect passengers in a vehicle. You can find FPF’s analysis and more on Data, Mobility, and Location on the website.
Most of the day’s discussion focused on the landscape of new driver safety automotive technologies, including driver impairment technologies, such as Alcohol Detection Systems, and the challenges needed to gain consumer acceptance of these technologies. Panelists highlighted the unique privacy challenges vehicles pose and the transparency and consent mechanisms needed to ensure individuals can exercise control over their data. Unlike more personal electronics, cars are typically owned by one person and operated by others. Cain noted that privacy is often considered in the context of the vehicle owners, but this model does not address the privacy of passengers or other drivers, including individuals who become owners later in the vehicle’s lifetime, such as through the secondary market.
Kingsley, who works with NHTSA through a public-private partnership on cooperative research to create the Driver Alcohol Detection System for Safety (DADSS), said that the project has been focused on privacy since its earliest stages, noting that “the only way we’re going to be able to deploy broadly is with consumer acceptance, and privacy is at the forefront of that.” Even though NHTSA has limited authority to regulate data privacy, she said that any NHTSA rule has to be practicable and is closely tied to considerations around individual privacy.
Cain underscored the importance of consumer trust in the technology rollout and highlighted the report’s findings that consumer trust in the automotive industry is still ahead of other industries. In light of subsequent news coverage about vehicle data collection practices, Cain affirmed the automotive industry’s commitment to the Alliance for Automotive Innovation’s 2014 Consumer Privacy Protection Principles, which require heightened protection for biometric data and driver behavior data, including information about driver impairment. Cain also noted that since the rollout of the Consumer Privacy Protection Principles, several states have enacted comprehensive state privacy laws and vehicle-specific privacy laws, which could be integrated into the principles. However, she said, “what we are begging for is a federal privacy law that would cover our industry along with every industry in the United States.”
All three panelists emphasized that the technologies to determine driver impairment are still nascent and unfamiliar to consumers. Wallace, discussing a camera-based approach to detecting driver impairment, noted that while consumers may generally like their vehicles, “the idea of having an in-cabin camera is very new, and we don’t fully know what the reaction will be.” However, he also argued that the potential to save ten thousand lives per year requires the industry to think about how best to implement the technology rather than whether to implement it at all. Wallace reiterated the importance of addressing automotive safety and consumer privacy, saying, “we are always looking at this through both lenses.”
To read the full report and the consumer survey findings, visit the FPF website, and be sure to watch Adonne Washington’s LinkedIn Live chat with CEO Jules Polonetsky on FPF’s YouTube Channel.
Alan Raul, Founder of Sidley Austin’s Privacy and Cybersecurity Law Practice Elected FPF’s New Board President
FPF Founder Christopher Wolf and Board Chair steps down after 15 years of service
FPF is pleased to announce Alan Raul, former Vice Chairman of the Privacy and Civil Liberties Oversight Board, has been elected to serve as President and Chair of the organization’s Board of Directors. Raul succeeds Christopher Wolf, founding Board President and founder of FPF, who is stepping down after a foundational and impactful tenure spanning 15 years.
Wolf, a pioneer in Internet and privacy law, is Senior Counsel Emeritus of Hogan Lovells’ top-ranked Privacy and Cybersecurity practice. As a leading attorney with the firm, he co-founded and led the development of the practice for over a decade, advising and shaping the thinking of Internet free speech, hate speech, and the parameters of government access to stored information. Wolf will continue as a member of FPF’s Board of Directors throughout this year before stepping down.
“In 2008, when I founded the Future of Privacy Forum, our vision was that it would be a place where we could advance the responsible use of data while respecting individual privacy,” Wolf said. “We believed that if dedicated technologists, policymakers, industry groups, and advocates focused on advancing privacy in a manner that businesses can achieve, we could strike a balance between consumer privacy and personalization that enables greater innovation for all.”
FPF flourished under Wolf’s guidance, becoming instrumental in steering collaborative and innovative efforts to address the complexity of the data-driven world. The organization regularly publishes substantive policy papers and reports tracking and analyzing data protection developments in different jurisdictions worldwide. Since launching, FPF has expanded its offices to Europe, Tel Aviv, and the Asia Pacific region and convened numerous international events, including the Brussels Privacy Symposium, now in its 7th year and first annual Japan Privacy Symposium.
Wolf’s dedication has not only set a high benchmark for leadership but also has helped regulators, policymakers, and staff at data protection authorities better understand the technologies at the forefront of data protection law. FPF will honor and celebrate Wolf’s contributions to the privacy sector and FPF during his tenure at their 2024 Advisory Board Meeting’s Opening Night Reception on June 5.
“In my experience in leading privacy and cybersecurity law and research, I’ve come to recognize the qualities that make a dedicated privacy trailblazer,” Wolf said. “Alan Raul shares my commitment to fostering a thriving, diverse privacy landscape that advances responsible data practices and technological innovation. His values align with the needs of FPF, and I am confident he will work tirelessly with integrity and dedication to build on the successes of recent years and take on new challenges.”
Raul has served on FPF’s board for eight years and is the founder and, for 25 years, the leader of Sidley Austin LLP’s highly-ranked Privacy and Cybersecurity Law practice. He is currently Senior Counsel at Sidley. Raul brings his breadth of knowledge in global data protection and compliance programs, cybersecurity, artificial intelligence, national security, and Internet law. He is also currently a member of the Technology Litigation Advisory Committee of the U.S. Chamber of Commerce Litigation Center. Raul is also a Lecturer in Law at Harvard Law School, where he teaches Digital Governance and Cybersecurity.
“I’m thrilled to take on this role and continue working to advance responsible data practices and safeguard individual privacy rights,” Raul said. “By leveraging my experience in advising global compliance programs and navigating complex regulatory landscapes, I hope I can contribute meaningful insights to the Board of Directors and effectively guide the direction of FPF’s work as we continue to grow globally as well as meet the new challenges and opportunities in the era of Artificial Intelligence.”
Olivier Sylvain and George Little also join FPF’s Board of Directors as two new members to serve. Sylvain is a Professor of Law at Fordham University and a Senior Policy Research Fellow at Columbia University’s Knight First Amendment Institute, where his research has focused on information and communications law and policy. Sylvain served as Senior Advisor to the Chair of the Federal Trade Commission from 2021 to 2023. Little is a partner at the Brunswick Group specializing in crisis communications, cybersecurity, reputational, and public affairs matters. Little co-chairs the firm’s Global Cybersecurity, Data & Privacy Practice, pulling from his experience working in the highest levels of the national security and defense community and the private sector.
Sylvain and Little join the ranks of recently named board members, including Tom Moore, recently retired as AT&T’s chief privacy officer; Jane Horvath, partner at Gibson, Dunn & Crutcher, LLP and former Chief Privacy Officer of Apple; and Theodore Christakis, Professor of International, European and Digital Law at University Grenoble Alpes (France), Director of the Centre for International Security and European Law (CESICE), and Director of Research for Europe with the Cross-Border Data Forum. FPF’s distinguished new Directors join other privacy luminaries on our Board of Directors – namely, Anita Allen, Debra Berlin, Danielle Citron, Mary Culnan, David Hoffman, Agnes Bundy Scanlan, and Dale Skivington.
“It’s been a pleasure getting to work with Chris Wolf and seeing the vision we had for FPF as a hub for privacy education and research develop over the years and grow into the leading institution it is today,” said Jules Polonetsky, CEO of FPF. “I am confident in Alan’s ability to lead the board to greater heights and continue informing the organization’s future work.”
Composed of leaders from industry, academia, and civil society, the input of FPF’s Board of Directors ensures that FPF’s work is expert-driven and independent of any stakeholders.
About Future of Privacy Forum (FPF) The Future of Privacy Forum (FPF) is a global non-profit organization that brings together academics, civil society, government officials, and industry to evaluate the societal, policy, and legal implications of data use, identify the risks and develop appropriate protections. FPF believes technology and data can benefit society and improve lives if the right laws, policies, and rules are in place. FPF has offices in Washington D.C., Brussels, Singapore, and Tel Aviv. Follow FPF on X and LinkedIn.
Examining Novel Advertising Solutions: A Proposed Risk-Utility Framework
The digital advertising industry is in the midst of a sea change. Around the world, privacy regulators have become far more critical of mainstream advertising business models. Both lawmakers and enforcers of existing laws are now more focused on strengthening individual privacy rights and specifically preventing many of the harms associated with the use of personal information in advertising. Meanwhile, large platforms such as Apple, Google, and Microsoft have taken significant steps in recent years to limit access to advertising-related data about their users through efforts like App Tracking Transparency (ATT), Intelligent Tracking Prevention (ITP), and an ongoing process to deprecate third party cookies in Google Chrome. Each change has ripple effects throughout the economy, changing the way advertisers do business and often impacting other social values.
In reaction to these regulatory and platform pressures, businesses are actively seeking new tools and solutions to maintain identity and addressability, or to provide greater privacy safeguards, ideally (in their view) doing so while sustaining as much business utility as possible. Many solutions involve privacy-enhancing technologies (PETs), while others involve a significant shift in business models, such as a return to contextual advertising, the use of solely first-party data, or a shift to client-side processing.
The goal of this Risk-Utility Framework and its associated Background (“Advertising in the Age of Data Protection”) is to provide a comprehensive rubric for navigating the many tradeoffs inherent in the evolving digital advertising landscape and the technology it is built upon. We do not assign values to each aspect of utility, risk, or social impact, but rather aim to holistically identify the many factors relevant for a policymaker or privacy leader to evaluate the impact of a given digital advertising proposal, solution, or system.
FPF Statement on Vice President Harris’ announcement on the OMB Policy to Advance Governance, Innovation, and Risk Management in Federal Agencies’ Use of Artificial Intelligence
Following the groundbreaking White House Executive Order on AI last fall, which outlined ambitious goals to promote the safe, secure, and trustworthy use and development of AI systems, Vice President Harris has today announced the publication by the Office of Management and Budget of a binding memorandum on “Advancing Governance, Innovation, and Risk Management for Agency Use of Artificial Intelligence,” which indicates the diligent efforts of agencies toward achieving this objective. This commitment is further highlighted by the National Telecommunications and Information Administration (NTIA) publication earlier this week of the“Artificial Intelligence Accountability Policy Report,” which details mechanisms to support the creation and adoption of trustworthy AI.
Although the OMB memorandum primarily focuses on the government’s use of AI, its influence on the private sector will be significant. This is due to not only the requirements for U.S. government vendors and procurement, but also how this framework will create broadly applicable norms and standards for conducting impact assessments, mitigating bias, providing rights to individuals affected by AI systems that impact their rights and safety, and assessing data quality and data privacy in these systems.
“This is a pivotal moment for the development of AI standards when the public sector has a crucial role to play in setting norms for the assessment and procurement of AI systems. We are particularly enthused by the renewed commitment to bring clarity to the development of AI in the public sector and its national utilization. At FPF, we eagerly anticipate contributing to this crucial work through our evidence-based research on Artificial Intelligence.”
– Anne J. Flanagan, FPF Vice President for Artificial Intelligence
Youth Privacy in Immersive Technologies: Regulatory Guidance, Lessons Learned, and Remaining Uncertainties
As young people adopt immersive technologies like extended reality (XR) and virtual world applications, companies are expanding their presence in digital spaces, launching brand experiences, advertisements, and digital products. While virtual worlds may in some ways resemble traditional social media and gaming experiences, they may also collect more data and raise potential manipulation risks, particularly for vulnerable and impressionable young people.
This policy brief analyzes recent regulatory and self-regulatory actions and guidance related to youth privacy, safety, and advertising in immersive spaces, pulling out key lessons for organizations building experiences in virtual worlds.
Recent FTC Enforcement Actions and Guidance
The Federal Trade Commission (FTC) has shown a strong interest in using its consumer protection authority to bring enforcement actions against a wide range of digital companies for alleged “unfair and deceptive” practices, rule violations, and other unlawful conduct. The Commission has also issued several policy statements and guidance documents relevant to organizations building immersive technologies, touching on issues such as biometric data and advertising to children. It is clear the agency is thinking seriously about how its authority could apply in emerging sectors like AI, and organizations working on immersive technologies should take heed. Lessons from recent FTC privacy cases and guidance include:
The FTC interprets the Children’s Online Privacy Protection Act (COPPA)’s definition of “personal information” broadly, including data types that immersive technologies commonly collect, like eye tracking.
Immersive application providers must comply with COPPA if their application is “directed to children” or if there is “actual knowledge” children are accessing it.
Organizations should provide privacy policies and notices in a format appropriate for and consistent with the design elements of immersive experiences.
Organizations should take additional steps to be transparent about advertising practices.
Self-Regulatory Cases and Safe Harbor Guidance
Self-regulatory bodies also have an essential role in ensuring privacy and safety in child-directed applications and providing guidance to companies operating in the space. For example, organizations designated as COPPA Safe Harbors can guide companies toward compliant, developmentally appropriate, and privacy-protecting practices. Lessons from recent self-regulatory cases and Safe Harbor guidance include:
Advertising disclosures in immersive environments should be designed to be as clear and conspicuous as possible and provided in an age-appropriate manner.
Platforms that allow advertisements to children should ensure that developers, brands, and content creators have the necessary tools and guidance to clearly and conspicuously disclose the presence of advertising to children.
Privacy by design and by default demonstrate to regulatory and self-regulatory bodies that an organization takes youth privacy seriously.
Privacy and advertising practices for teens should take into account the unique considerations relevant to teen privacy and safety, compared to child and adult guidance.
Organizations with a robust privacy culture that demonstrate good faith efforts to follow the law are more likely to be given the benefit of the doubt.
Remaining Areas of Uncertainty
Because immersive technologies are relatively new and evolve rapidly, much of the existing regulatory and self-regulatory guidance is pulled from other contexts. Therefore, questions remain about how regulations apply in immersive environments and how to operationalize best practices. These questions include:
How age-appropriate design principles will best fit into an immersive technology context, such as how best to ensure strong default privacy settings for underage users; the best methods for clarity and transparency regarding data practices notices and advertising disclosures; and whether an immersive experience should require unique, additional safeguards.
What novel data collection and analysis methods in the immersive technology space will require discerning data practices surrounding its safeguarding and use, such as what kinds of inferences are appropriate to make from body-based data or to what extent avatars not derived from a child’s data are considered personal information.
How immersive technologyimpacts children and teens; more research is needed to understand whether certain kinds of experiences and privacy practices are harmful for children and teens, if there are unique risks to children’s privacy and mental health, and how organizations, parents, schools, and other stakeholders can address potential issues.
