DataHour: Bias/Fairness Detection and Remediation in ML Models

DataHour: Bias/Fairness Detection and Remediation in ML Models

08 May 202412:05pm - 08 May 202413:05pm

DataHour: Bias/Fairness Detection and Remediation in ML Models

About the Event

Bias in machine learning models is a significant source of model risk. Biased models can lead to unfair outcomes that adversely affect certain groups and result in misaligned incentives. This raises ethical concerns and, in domains like finance, also poses regulatory and compliance risks. Therefore, it is crucial for models to be fair and transparent.

The presentation introduces the concept of model risk and emphasizes the importance of considering bias and fairness to mitigate model risk. In the second part of the presentation, we discuss fairness metrics and methods for identifying biased models. The third part of the presentation explains techniques for mitigating and remedying model bias. The presentation aims to provide concrete examples through notebooks and will also cover tools for diagnosing biased models.

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Who is this DataHour for?

  1. Best articles get published on Analytics Vidhya’s Blog Space
  2. Best articles get published on Analytics Vidhya’s Blog Space
  3. Best articles get published on Analytics Vidhya’s Blog Space

About the Speaker

Srimugunthan Dhandapani

Srimugunthan Dhandapani

Data Science Lead at WellsFargo

Srimugunthan brings over 12 years of experience in data analytics, specializing in prediction models, recommender systems, and more. With a background in computer science and an Executive MBA in product management, he's well-equipped to guide us through the complexities of bias in ML models. You can reach her on LinkedIn.

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