Rethinking Privacy in the AI Era: Policy Provocations for a Data-Centric World

Paper by Jennifer King, Caroline Meinhardt: “In this paper, we present a series of arguments and predictions about how existing and future privacy and data protection regulation will impact the development and deployment of AI systems.

➜ Data is the foundation of all AI systems. Going forward, AI development will continue to increase developers’ hunger for training data, fueling an even greater race for data acquisition than we have already seen in past decades.

➜ Largely unrestrained data collection poses unique risks to privacy that extend beyond the individual level—they aggregate to pose societal-level harms that cannot be addressed through the exercise of individual data rights alone.

➜ While existing and proposed privacy legislation, grounded in the globally accepted Fair Information Practices (FIPs), implicitly regulate AI development, they are not sufficient to address the data acquisition race as well as the resulting individual and systemic privacy harms.

➜ Even legislation that contains explicit provisions on algorithmic decision-making and other forms of AI does not provide the data governance measures needed to meaningfully regulate the data used in AI systems.

➜ We present three suggestions for how to mitigate the risks to data privacy posed by the development and adoption of AI:

1. Denormalize data collection by default by shifting away from opt-out to opt-in data collection. Data collectors must facilitate true data minimization through “privacy by default” strategies and adopt technical standards and infrastructure for meaningful consent mechanisms.

2. Focus on the AI data supply chain to improve privacy and data protection. Ensuring dataset transparency and accountability across the entire life cycle must be a focus of any regulatory system that addresses data privacy.

3. Flip the script on the creation and management of personal data. Policymakers should support the development of new governance mechanisms and technical infrastructure (e.g., data intermediaries and data permissioning infrastructure) to support and automate the exercise of individual data rights and preferences…(More)”.