Interpretable machine learning applications: Part 5

제공자:
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

 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
Beginner초급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

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

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

자주 묻는 질문

자주 묻는 질문

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.