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Approaches to Fairness and XAI

Posted Jan 17, 2023 | Views 484
# Explainable Artificial Intelligence
# Fiddler AI
# Decision-making Behaviors
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SPEAKERS
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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Demetrios Brinkmann
Demetrios Brinkmann
Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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Abi Aryan
Abi Aryan
Abi Aryan
Machine Learning Engineer @ Independent Consultant

Abi is a machine learning engineer and an independent consultant with over 7 years of experience in the industry using ML research and adapting it to solve real-world engineering challenges for businesses for a wide range of companies ranging from e-commerce, insurance, education and media & entertainment where she is responsible for machine learning infrastructure design and model development, integration and deployment at scale for data analysis, computer vision, audio-speech synthesis as well as natural language processing. She is also currently writing and working in autonomous agents and evaluation frameworks for large language models as a researcher at Bolkay.

Prior to consulting, Abi was a visiting research scholar at UCLA working at the Cognitive Sciences Lab with Dr. Judea Pearl on developing intelligent agents and has authored research papers in AutoML and Reinforcement Learning (later accepted for poster presentation at AAAI 2020) and invited reviewer, area-chair and co-chair on multiple conferences including AABI 2023, PyData NYC ‘22, ACL ‘21, NeurIPS ‘18, PyData LA ‘18.

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Abi is a machine learning engineer and an independent consultant with over 7 years of experience in the industry using ML research and adapting it to solve real-world engineering challenges for businesses for a wide range of companies ranging from e-commerce, insurance, education and media & entertainment where she is responsible for machine learning infrastructure design and model development, integration and deployment at scale for data analysis, computer vision, audio-speech synthesis as well as natural language processing. She is also currently writing and working in autonomous agents and evaluation frameworks for large language models as a researcher at Bolkay.

Prior to consulting, Abi was a visiting research scholar at UCLA working at the Cognitive Sciences Lab with Dr. Judea Pearl on developing intelligent agents and has authored research papers in AutoML and Reinforcement Learning (later accepted for poster presentation at AAAI 2020) and invited reviewer, area-chair and co-chair on multiple conferences including AABI 2023, PyData NYC ‘22, ACL ‘21, NeurIPS ‘18, PyData LA ‘18.

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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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