MLOps Coffee Sessions #142 with Murtuza Shergadwala, Approaches to Fairness and XAI, co-hosted by Abi Aryan. This episode is sponsored by Fiddler AI.
// Abstract
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.
// Bio
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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// MLOps Swag/Merch
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// Related Links
https://murtuzashergadwala.wixsite.com/murtuza
https://www.fiddler.ai/blog/detecting-intersectional-unfairness-in-ai-part-1
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Timestamps:
[00:00] Moto's preferred coffee
[00:35] Introduction to Murtuza Shergadwala
[01:06] Takeaways
[04:30] Huge shout-out to Fiddler AI for sponsoring this episode!
[05:00] Don't forget to like, comment, and subscribe. Give us a rating
[06:10] Moto's background and transition to Human-centric AI
[10:52] Decision-making behaviors of engineering designers in design contests
[15:10] Gaining insights from data decisions
[18:00] Defining latent variables
[20:32] Designer's perspective on building systems
[23:14] XAI as a movement
[27:47] Selling regulations and bridging the gap
[32:18] Data integrity towards detecting outliers, alerting, and data drifts
[34:32] Dealing with alerts and alert fatigue
[37:31] Approaches and their limitations
[39:10] Alert-level systems
[42:19] Alerts putting into practice
[45:30] Creative alerts
[47:02] One solution fits all?
[50:08] Wrap up
