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[…] mechanisms transparent, access controls, monitoring, documentation, testing, and debugging. “Privacy or security harms in machine learning do not necessarily require direct access to underlying data or source code,” said Andrew Burt, Immuta Chief Privacy Officer and Legal Engineer. “We explore how creators of any machine learning system can limit the risk of unintended leakage […]

[…] explaining approaches to data protection. Unlike traditional software, in machine learning systems privacy or security harms do not necessarily require direct access to underlying data or source code. The whitepaper presents a layered approach to data protection in machine learning, including recommending techniques such as noise injection, inserting intermediaries between training data and the […]

[…] of their decisions, as use cases run the gamut of useful and creative applications as well as those which might be concerning. OpenAI, a group which actively promotes cooperativeness in AI research, designed a fake text generator they felt was so good, they decided the risks it posed made it too dangerous to release. […]

[…] and Innovation Foundation to undertake both projects. The Alliance will support data sharing efforts under way, help address and mitigate challenges that create barriers to sharing and promote practices that encourage more data sharing between industry and academic researchers. So far, more than 25 prominent companies are participating in the Alliance’s activities. In its […]

[…] not only to go beyond existing laws in addressing consumer privacy concerns, but also to help shape evolving legislative efforts. We are glad to see that the NAI is taking major steps in the right direction in order to continue to be on the front lines of protecting consumer privacy and promoting responsible business practices.

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[…] which FPF released last October. Most of what has been implemented is machine learning – algorithms that can evaluate their own output and make adjustments to their code without human involvement. Machine learning is used in image recognition, facial recognition, sensory inputs for autonomous vehicles, and many other tasks. I like the definition of […]

[…] actionable resources for different audiences. The Student Privacy Pledge has been one of our most successful projects. Co-founded with SIIA, the Pledge is a Federal Trade Commission-enforceable code of conduct for edtech vendors. Now with nearly 330 companies as signatories, the Pledge was designed to both raise awareness of best practices and facilitate their […]

[…] must respect the user’s permission settings and not attempt to manipulate, trick, or force people to consent to unnecessary data access. (5.1.1) New language in the Developer Code of Conduct states: “Customer trust is the cornerstone of the App Store’s success. Apps should never prey on users or attempt to rip-off customers, trick them […]

[…] safeguards, as it considers options to improve school safety; Support efforts to better educate and communicate with stakeholders regarding existing legal authorities that permit data sharing to promote health and safety within a framework that mitigates privacy risks to students; and Call for neutral, expert analysis of empirical data regarding the nature, extent, and […]