Differential Privacy (DP) is, by now, the most widely adopted formal model of privacy protection used in industry [L23] and government [ABS22] but my sense is that its “semantics”, especially in the presence of correlated data and in the adversarial interactive setting, is still not broadly understood in the community, especially among practitioners. In the … More What Can Differential Privacy Actually Protect?
We have just released another paper on arXiv: https://arxiv.org/abs/2406.17649 Here’s the abstract: We consider the problem of privacy protection in Reinforcement Learning (RL) algorithms that operate over population processes, a practical but understudied setting that includes, for example, the control of epidemics in large populations of dynamically interacting individuals. In this setting, the RL algorithm … More Privacy-Preserving Reinforcement Learning for Population Processes
I have been incredibly frustrated with the lack of quality and content in many responsible AI (RAI) conversations. Almost all the (non-academic) RAI meetings I attended these past 12 months involve the speakers repeating words like fairness, accountability, and transparency basically for the entire duration of the meeting, with everyone nodding furiously in agreement about … More Improving the Quality of the Responsible AI Conversations
There are many different definitions of Artificial Intelligence in the literature, all are suggestive and insightful. However, at the end of the day, I think there is really one simple enough to be understood and formalised rigorously. This is John McCarthy’s original definition of AI from 1955: “the science and engineering of making intelligent machines”. … More A Simple Definition of Artificial Intelligence
Mental Models 4 Life 