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  •  About
    • About the Lab
    • Director’s Note
    • Our Vision
    • Founding Donor
    • Advisory Board
    • Principal Investigators
  • People
    • Associated Faculty
    • Executive Committee
    • Students
    • Program Directorate
  • TrustNet
  •  Projects
  •  Resources
    • Pre-Doctoral Program
    • Internships
    • Early Career Award
    • Trust Lab Grant
    • Trust Lab Fellowship
  •  News
    • Trust Matters
    • Quick Updates
  •  Events
    • Talks
    • Trust Summit
    • TL CTF
    • Schools
    • All Events
  •  Engage

Innovative Learning Material and Evaluation Platform Development for Trustworthy AI

Overview
People
Outcome
Overview

Trustworthy AI involves multi-agent interactions where each agent wants to maximize their individual gains. To understand the collective objectives that are achievable and are mutually ‘best’ for all agents, we need to train our workforce with topics in strategic multi-agent systems that delve into the topics at the interface of economics and computer science. This proposal aims to develop an impactful yet easy-to-follow set of video-learning materials and an associated grading platform to help students and practitioners to self-train on the topics of algorithmic game theory and mechanism design.
Active from 2024
Funding: Trust Lab Grant 2024

People

Swaprava Nath

Outcome
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    Department of Computer Science and Engineering,
    Indian Institute of Technology Bombay,
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