Seminar with Hima Lakkaraju: Does Model Understanding Improve Human Decision Making?
Professor Hima Lakkaraju of Harvard University joined us on April 11, 2022, for “Does Model Understanding Improve Human Decision Making?”
Abstract
As machine learning (ML) models are increasingly being employed to make consequential decisions in high-stakes settings such as finance, healthcare, and hiring, it becomes important to ensure that these models are actually beneficial to human decision makers.
To this end, recent research in ML has focused on developing techniques which aim to explain complex models to domain experts/decision makers so that they can determine if, when, and how much to rely on the predictions of these models.
In this talk, I will give a brief overview of the state-of-the-art in explaining ML models, and then present some of our recent research on understanding the impact of explaining the rationale behind model predictions to decision makers.
More specifically, I will discuss two user studies that we carried out with domain experts in healthcare (e.g., doctors) and hiring (e.g., recruiters) settings where we analyzed the impact of explaining the rationale behind model predictions on the accuracy and the discriminatory biases in the decision making process.
Professor Hima Lakkaraju of Harvard University joined us on April 11, 2022, for “Does Model Understanding Improve Human Decision Making?”
Abstract
As machine learning (ML) models are increasingly being employed to make consequential decisions in high-stakes settings such as finance, healthcare, and hiring, it becomes important to ensure that these models are actually beneficial to human decision makers.
To this end, recent research in ML has focused on developing techniques which aim to explain complex models to domain experts/decision makers so that they can determine if, when, and how much to rely on the predictions of these models.
In this talk, I will give a brief overview of the state-of-the-art in explaining ML models, and then present some of our recent research on understanding the impact of explaining the rationale behind model predictions to decision makers.
More specifically, I will discuss two user studies that we carried out with domain experts in healthcare (e.g., doctors) and hiring (e.g., recruiters) settings where we analyzed the impact of explaining the rationale behind model predictions on the accuracy and the discriminatory biases in the decision making process.
