DataHour: Bias and Fairness in NLP
DataHour: Bias and Fairness in NLP
25 Jul 202213:07pm - 25 Jul 202214:07pm
DataHour: Bias and Fairness in NLP
About the Event
Biases are stereotypical/unjust associations a model encodes with respect to protected attributes, or subpar model performance for certain groups. Hence, bias mitigation is the process of lessening the severity of stereotypical/unjust associations and disparate model performance.
Fairness concerns ensuring that a model performs equitably for all groups of people with respect to protected attributes.
This DataHour will cover the background of how biases can occur in NLP systems and why it is important to identify them. The session will also include techniques to identify and mitigate these biases and how fairness can be estimated. The discussion will revolve mostly around "algorithmic bias" that is, the "unjust, unfair, or prejudicial treatment of people related to race, income, sexual orientation, religion, gender, and other characteristics historically associated with discrimination and marginalization, when and where they manifest in algorithmic systems or algorithmically aided decision-making".
Prerequisites: Enthusiasm for learning Data Science and familiarity with ML and NLP.
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- 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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