Description for Interpretable ml applications: Part 5
Aequitas Tool Overview: Explore the Aequitas Tool's capabilities for identifying and quantifying bias in machine learning prediction models.
Case Study on Recidivism: Utilize the COMPAS dataset to investigate the potential effects of biases on outcomes and to make predictions regarding recidivism.
Bias and Fairness Measurement Technique: Investigate statistical methods for evaluating bias and fairness in machine learning models, regardless of the specific algorithms used.
Career Advancement: Acquire skills that are advantageous to not only data scientists and ML developers, but also to policymakers and decision makers who prioritize equitable AI applications.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by Coursera Project Network
Duration: 1.5 hours at your own pace
Schedule: Hands-on learning
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