Interpretable machine learning applications: Part 5

提供方
Coursera Project Network
在此指导项目中,您将:

 Be acquainted with the basics of the Aequitas Tool as a tool to measure and detect bias in the outcome of a machine learning prediction model.

Learn more about a real world case study, i.e., predictions of recidivism (COMPAS dataset), and how the prediction model may have been biased.

Learn a technique, which is largely based on statistical descriptors, for measuring bias and fairness for Machine Learning (ML) prediction models.

Clock1.5 hours
Beginner初级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

您要培养的技能

  • Software Engineering
  • Artificial Intelligence (AI)
  • Data Science

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Setting up the stage

  2. First attempt and stage for detecting bias

  3. Second attempt and stage for detecting bias

  4. Third attempt and stage in detecting bias

  5. Visualisation: Final stage for detecting bias

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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