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Approaches to Fairness and XAI

Posted Jan 17, 2023 | Views 89
# Explainable Artificial Intelligence
# Fiddler AI
# Decision-making Behaviors
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SPEAKER
Murtuza Shergadwala
Murtuza Shergadwala
Murtuza Shergadwala
Data Scientist 2 @ Fiddler AI

Dr. Murtuza Shergadwala is a data scientist at Fiddler AI. His background is in human-machine interaction and design decision-making. He received his Ph.D. from Purdue University in Mechanical Engineering. Prior to Fiddler, he was a postdoc at the Games User Interaction and Intelligence Lab at UC Santa Cruz where he focused on using Bayesian approaches for modeling cognition and investigating the theory of mind. He’s super passionate about fairness in AI for underrepresented communities.

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Dr. Murtuza Shergadwala is a data scientist at Fiddler AI. His background is in human-machine interaction and design decision-making. He received his Ph.D. from Purdue University in Mechanical Engineering. Prior to Fiddler, he was a postdoc at the Games User Interaction and Intelligence Lab at UC Santa Cruz where he focused on using Bayesian approaches for modeling cognition and investigating the theory of mind. He’s super passionate about fairness in AI for underrepresented communities.

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SUMMARY

The field of Explainable Artificial Intelligence (XAI) is continuously evolving, with an increasing focus on providing model-centric explanations in a human-centric manner. However, better frameworks and training for users are needed to fully utilize the potential of XAI tools.

Additionally, there is a discrepancy in the approach to fairness in XAI, with the industry approaching it from a regulatory standpoint, while academia is engaging in more discussion and research on the topic.

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