MLOps.community

Approaches to Fairness and XAI // Murtuza Shergadwala // MLOps Podcast #142

Jan 24, 2023

39:15

Creator website

Highlights

AI Chapters

Episode notes

The Evolution of Explainability in Machine Learning

05:16

The Human-Centered Approach to Machine Learning Model Management

03:23

ML Monitoring - Is FIDLER the Only ML Model Monitoring Tool?

01:55

Introduction

4min

What Is Your Background? And How Did You Become a Mechanical Engineer?

4min

Is There a Connection Between Cognitive Bias and Mechanical Engineering?

4min

The Human Centric Perspective on Modern Monitoring Systems

2min

Designing a Dashboard - Who's the User?

1min

The Evolution of Explainability in Machine Learning

5min

The Human-Centered Approach to Machine Learning Model Management

3min

ML Monitoring - Is FIDLER the Only ML Model Monitoring Tool?

2min

The Three Pillars in ML Monitoring

3min

10.

Detecting Outliers and Alerts

4min

11.

What Kind of Alerts Do You Have?

4min

12.

Is There a One Solution Fits It All?

3min

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.

// 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