Research & Ethics

Data-driven and evidence-based social policy innovation can help governments serve communities better, smarter, and faster. Integrated Data Systems (IDS) use data that government agencies routinely collect in the normal course of delivering public services to shape local policy and practice. They can use data to evaluate the effectiveness of new initiatives or bridge gaps between public services and community providers.

In the world of consumer privacy, including the Internet of Things (IoT), mobile data, and advertising technologies (“Ad Tech”), it can often be difficult to measure real-world impact and conceptualize individual harms and benefits. Fortunately, academic researchers are increasingly focusing on these issues, leading to impressive scholarship from institutions such as the Princeton Center for Information Technology Policy (CITP), Carnegie Mellon University School of Computer Science, UC Berkeley School of Information, and many others, including non-profits and think tanks.

Analysis of personal data can be used to improve services, advance research, and combat discrimination. However, such analysis can also create valid concerns about differential treatment of individuals or harmful impacts on vulnerable communities. These concerns can be amplified when automated decision-making uses sensitive data (such as race, gender, or familial status), impacts protected classes, or affects individuals’ eligibility for housing, employment, or other core services. When seeking to identify harms, it is important to appreciate the context of interactions between individuals, companies, and governments—including the benefits provided by automated decision-making frameworks, and the fallibility of human decision-making.

We look forward to an exciting program of thought leadership, including academic guest speakers and the authors of this year’s PPPM scholarship (announced & awarded in November), who will engage with policymakers in a discussion of academic ideas with practical real-world impact.

Today, the Future of Privacy Forum (FPF) and the Ohio State University’s Program on Data and Governance are holding a discussion of ethics, privacy and practical research reviews in corporate settings. This timely event, which follows the White House’s call to develop strong data ethics frameworks, convened corporate and academic leaders to discuss how to integrate ethical and privacy considerations into innovative data projects and research.

As the volume of consumer data grows, an increasing number of decisions previously made by humans are now made by algorithms. Many thought leaders have called for algorithmic transparency to ensure that these decisions aren’t leading to unfair or discriminatory outcomes, but algorithmic transparency is tricky to implement. Last December, FTC Commissioner Julie Brill acknowledged […]

Kicking off a great event this morning, Beyond IRBs: Designing Ethical Review Processes for Big Data Research, with over 60 of the country’s top academics and industry researchers. Thoughtful Key Note remarks from Professor Ryan Calo, from the University of Washington School of Law, have framed the conversation today around how best to create incentives in […]

Each year, FPF invites privacy scholars and authors to submit articles and papers to be considered by members of our Advisory Board, with an aim toward showcasing those articles that should inform any conversation about privacy among policymakers in Congress, as well as at the Federal Trade Commission and in other government agencies. For our […]

Few would deny that technology and social media are changing the way we interact. People today can stay in touch with friends on Facebook, share vacation photos on Instagram, follow trends on Twitter, grow their networks on LinkedIn, and explore communities on Reddit. And people are staying connected wherever they go. The Pew Research Center […]

For all its hype, discussions about Big Data often still devolve into debates about buzzwords and concepts like business intelligence, data analytics, and machine learning. Hidden in each of these terms are important privacy and ethical considerations. A recent article by Kirsten Martin in MIS Quarterly Executive attempts to bring these considerations to the surface by moving past framing […]