Using Machine Learning for High Stakes Decision Making

For our third event, we will be hearing from Hima Lakkaraju, an incoming assistant professor at Harvard University with appointments in the Business School and Department of Computer Science! Hima will speak to us about novel computational frameworks that are capable of making high stakes decisions in areas like law, healthcare, and public policy. The abstract for the talk can be found below.

We are being generously hosted by Jobcase for this event!

Agenda
6:00-6:15pm -- Event starts with introductions and updates

6:15-7:00pm -- Hima Lakkaraju: Using Machine Learning for High Stakes Decision Making

7:00-7:30pm -- Q+A time

7:30-8:00pm -- Snacks and drinks and socializing

Abstract: Domains such as law, healthcare, and public policy often involve highly consequential decisions which are predominantly made by human decision-makers. The growing availability of data pertaining to such decisions offers an unprecedented opportunity to develop machine learning models which can aid human decision-makers in making better decisions. However, the applicability of machine learning to the aforementioned domains is limited by certain fundamental challenges:

1. The data is selectively labeled i.e., we only observe the outcomes of the decisions made by human decision-makers and not the counterfactuals.
2. The data is prone to a variety of selection biases and confounding effects.
3. The successful adoption of the models that we develop depends on how well decision-makers can understand and trust their functionality, however, most of the existing machine learning models are primarily optimized for predictive accuracy and are not very interpretable.

In this talk, I will describe novel computational frameworks which address the aforementioned challenges, thus, paving the way for large-scale deployment of machine learning models to address problems of significant societal impact.