Leveraging AI Responsibly in Sensemaking for Successful Human AI Workflows

Speaker:  Nitesh Goyal – Stamford, CT, United States
Topic(s):  Human Computer Interaction


My research vision is to enable expert and non-experts to successfully make sense of complex world problems. As a Human-Computer Interaction researcher, I iteratively focus on studying how sensemaking is performed to identify challenges in collaborative data analytics, design tools using computational techniques that overcome these challenges and evaluate my designs using human participants to inform subsequent designs. In particular, I have focussed on three domains: criminal justice, ML Development and Online Harassment. Solving crimes correctly is a critical and life-altering problem where intelligence analysts are constantly struggling against their biases. ML engineering and development requires constantly struggling with fairness and equity issues related to datasets and algorithmic outcomes. Managing online harassment requires emotional and physical labor by targets of harassment while making sense of who is harassing them and why ? There are many similarities across these 3 domains and despite, recurring themes of how AI should be designed responsibly to support these use cases/users, we have barely started to scratch the surface. In this lecture, I introduce the notion of Sensemaking Translucence into biases, fairness and equity related challenges. I then provide examples of how AI can support Sensemaking Translucence. My work finally makes the case that it is important to design from a human centered perspective by leveraging AI to support these Human AI Collaboration workflows.

About this Lecture

Number of Slides:  61
Duration:  45 minutes
Languages Available:  English
Last Updated: 

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