Interpretable machine learning applications: Part 5
You will be able to use the Aequitas Tool as a tool to measure and detect bias in the outcome of a machine learning prediction model. As a use case, we will be working with the dataset about recidivism, i.e., the likelihood for a former imprisoned person to commit another offence within the first two years, since release from prison. The guided project will be making use of the COMPAS dataset, which already includes predicted as well as actual outcomes.
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- Level Beginner
- Duration 3 hours
- Course by Coursera Project Network
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You will be able to use the Aequitas Tool as a tool to measure and detect bias in the outcome of a machine learning prediction model. As a use case, we will be working with the dataset about recidivism, i.e., the likelihood for a former imprisoned person to commit another offence within the first two years, since release from prison. The guided project will be making use of the COMPAS dataset, which already includes predicted as well as actual outcomes. Given also that this technique is largely based on statistical descriptors for measuring bias and fairness, it is very independent from specific Machine Learning (ML) prediction models. In this sense, the project will boost your career not only as a Data Scientists or ML developer, but also as a policy and decision maker.Modules
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- Assess Your Knowledge
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Labs
- Interpretable machine learning applications: Part 5
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- Project Overview
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Explore the Aequitas Tool to measure and detect bias in machine learning predictions using a recidivism dataset. This beginner-friendly course, led by Coursera, spans 180 minutes and is ideal for data scientists, ML developers, policy makers, and decision makers. Enhance your career with key insights into statistical descriptors for bias and fairness, independent of specific ML models. Available for free.
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