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The International Institute of Information Technology Bangalore (IIITB) and the Indian Institute of Technology, Jodhpur (IITJ) are organising Ethics in AI Talk Series: A Conversation on Cultural Diversity.
This webinar will summarise research from IIITB’s project on the ‘Regulatory Impact Assessment of the National AI Market Place (NAIM) of India’ and IITJ’s project on ‘Mitigating Bias in Face Recognition Systems’.
Date: 12th August 2021
Time: 4.30 to 5.45 PM
Register here.
|
4.30 PM |
Introduction |
|
4.35 PM |
Presentation 1: Regulatory Impact Assessment of the National AI Market Place (NAIM) of India by V Sridhar, Shrisha Rao - IIITB |
|
4.50 PM |
Presentation 2: Mitigating Bias in Face Recognition Systems, Richa Singh, Mayank Vatsa - IITJ |
|
5.05 PM |
Question and Answer Session |
|
5.25 PM |
Regional perspective by Dr. Melvin Jabar, from De La Salle University, Philippines |
|
5.35 PM |
Closing Remarks by Preeti Syal, Senior Specialist Niti Aayog |
Regulatory Impact Assessment of the National AI Market Place (NAIM) of India
V Sridhar, Shrisha Rao – International Institute of Information Technology Bangalore
As usage of Artificial Intelligence (AI) technologies across industries are increasing, there is a growing need for creating large marketplaces to host and transact good quality data sets to train AI algorithms. Our study analyses the characteristics of such an AI Marketplace (AIM). We build a review and rating system to signal the quality of data sets in the marketplace. Extensive simulations using Agent Based Modeling (ABM) indicate that reputation as a feedback mechanism to build trust between producers and consumers, We also model the effect of varying degrees of regulation on the functioning of the AIM.
Mitigating Bias in Face Recognition Systems
Richa Singh, Mayank Vatsa - Indian Institute of Technology Jodhpur
Automated face recognition has widespread applicability in several scenarios such as access control, surveillance, and even on mobile platforms for social media and marketing. Despite achieving high performance on benchmark datasets, several challenges and limitations of these systems have recently come to light, specifically the biased behaviour of these systems with respect to under-represented groups. We systematically study the effect of bias on face recognition systems, where and how is it manifested. We next design detection and mitigation algorithms to address the effect of bias. Large scale evaluations on diverse databases show that structured data collection and mitigation algorithms can help in improving the performance across different groups.
To attend this webinar, please register here. You will receive the meeting link closer to the event in your registered email address.