I'm involved with the Fairlearn project, so once I figure out what's necessary from a company policy-side, my plan is to incorporate these methods into Fairlearn one day.

Sure! I'm always happy to chat about this.

> Ethics teams are only useful if they are actively incorporated with and listened by engineering and business teams

I'm an ML fairness specialist who works on a responsible AI team, and in my experience, the best way to do this is to operate a fully-fledged product team whose "customers" are other teams in the company.

For example, I built an internal Python library that other teams can use to perform fairness audits of recommendation systems, so they can compute and report these fairness metrics alongside traditional rec. system performance metrics during the model training process. Now when the Digital Service Act goes into effect, and we are required to produce yearly algorithmic risk assessments of recommender systems, we already have a lot of this tech infrastructure in place.

One of my areas of interest is the intersection of machine learning and social work:

• Eric Rice has used ML to improve homelessness matching for youth in LA to improve racialized outcomes, design better HIV intervention strategies, and many other work
• Desmond Patton has used NLP to understand how gang-related violence is incubated on social media (tensions build on social media and eventually result in real life drive-by shootings).
• In general, Harnessing the power of technology for good is one of the 13 Grand Challenges for Social Work, so there's a lot of emerging work on using data to have more empirically driven social work practices.

Maybe InterpretML? It's developed and maintained by Microsoft Research and consolidates a lot of different explainability methods, including SHAP, into a consistent API.

Stanford has a Computational Policy Lab that focuses on using ML and data to measure the impact of policy changes. Carnegie Mellon has a joint PhD program in Machine Learning and Public Policy. There's also a research conference ACM EAAMO about combing algorithmic theory, economics, and public policy together.

My personal research interests in combining social work with machine learning to design better social work interventions, and there has been work in this space to use NLP to intervene on gang shootings and optimizing homelessness services for youth in LA.

Pretty sure third row, third col is Andrej Karpathy