// 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.

// MLOps Jobs board  

https://mlops.pallet.xyz/jobs

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

https://murtuzashergadwala.wixsite.com/murtuza

https://www.fiddler.ai/blog/detecting-intersectional-unfairness-in-ai-part-1

--------------- ✌️Connect With Us ✌️ -------------

Join our slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Abi on LinkedIn: https://www.linkedin.com/in/abiaryan/

Connect with Murtuza on LinkedIn: https://www.linkedin.com/in/murtuza-shergadwala/

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