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.
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
Who is this DataHour for?
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
About the Speaker
Participate in discussion
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