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Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification.

Brian d'Alessandro1,2, Cathy O'Neil3, Tom LaGatta4

  • 11 Department of Data Science, Zocdoc , New York, New York.

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Summary
This summary is machine-generated.

Machine learning and big data can worsen societal disparities. Data scientists need guidance to identify and mitigate discrimination within familiar data-mining processes to ensure objective outcomes.

Keywords:
data sciencedisparate impactethics

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Area of Science:

  • Computer Science
  • Sociology
  • Data Science Ethics

Background:

  • Growing awareness of machine learning (ML) exacerbating societal disparities.
  • Lack of concrete guidance for data science practitioners on addressing ML bias.
  • Research on ML bias often originates outside the data science community.

Purpose of the Study:

  • Introduce discrimination issues to data scientists on their own terms.
  • Provide a taxonomy of data-mining practices that can lead to unintended discrimination.
  • Suggest methods to augment development processes for mitigating ML discriminatory potential.

Main Methods:

  • Touring the standard data-mining process.
  • Categorizing common practices with discriminatory potential.
  • Surveying methods for measuring discrimination in ML systems.

Main Results:

  • Identification of specific data-mining practices that can introduce or amplify discrimination.
  • Overview of common metrics used to quantify ML discrimination.
  • Proposed augmentations to existing development workflows.

Conclusions:

  • Data scientists must be intentional in modeling and reducing discriminatory outcomes.
  • Failure to address bias perpetuates systemic discrimination under a guise of objectivity.
  • Proactive mitigation is essential for ethical AI development.