There is an extensive international conversation around the how to govern the use artificial intelligence. This attention is warranted, as large-scale algorithmic decision-making systems are proliferating across the many industries. Journalists and academics have identified a wide range of problems with these models (see below), but government has done little to protect citizens. Since, AI differs dramatically across its many uses, and no single regulatory intervention makes sense for all AI systems.
Work with a team to find a solution to problematic AI applications. First, choose a problematic instance of AI—you can use the list below or find your own problem. Next, work through possible interventions. Which would work well to alleviate the systemic AI issue?
Interventions: Interpretability, explainability; Protect tech whisteblowers; Algorithmic audits; Ex-ante approval; Public reporting; Problems: Justice systems; Discriminatory healthcare/employment algorithms; College admissions/financing; Credit approval; Critical infrastructure; Safety of autonomous cars;
That the session is based on breakout groups should hopefully make it relatively agnostic to participant size. However, more groups potentially means less time for knowledge sharing at the end of the session.
We're hoping that many efforts and discussions will continue after Mozfest. Share any ideas you already have for how to continue the work from your session.:The Brookings Institution is holding an ongoing international forum on AI governance. This forum has seven participants (US, EU, UK, Canada, Australia, Japan, Singapore) and is actively working on issues on how to govern AI, including regulatory approaches. This session is designed to contribute some technical knowledge to those broader efforts.
What is the goal and/or outcome of your session?:(1) To disseminate a better understanding of problems in AI/ML systems and why they matter, and also to increase practitioner understanding of the tools and processes available to improve them.
(2) To discover/crowdsource new ideas from AI/ML practitioners to better inform AI governance.
I study the implications of artificial intelligence and emerging technologies on society and governance at the Brookings Institution. I teach data science at Georgetown’s McCourt School of Public Policy.