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Keeping Real World Bias Out of Artificial Intelligence ?Examination of Coder Bias in Data Science Recruitment Solutions?

Author

Listed:
  • Yvette Burton

    (Columbia University School of Professional Studies)

Abstract

Research Question and Objectives: Is there subtle gender bias in the way companies word and code job listings in such fields as engineering and programming? Although the Civil Rights Act effectively bans companies from explicitly requesting workers of a particular gender, the language in these listings may discourage many women from applying.The objectives of the research are to create to foundational constructs leaders can use to address the growing employee competency and business performance gaps created by the impact of lack of gender diversity among data scientist roles, and siloes across enterprise talent strategies. These two objectives include: Integrated Data Scientist and HCM Leadership Development Strategies and AI Leadership Assessment and Development w/ Risk Audits.

Suggested Citation

  • Yvette Burton, 2019. "Keeping Real World Bias Out of Artificial Intelligence ?Examination of Coder Bias in Data Science Recruitment Solutions?," Proceedings of International Academic Conferences 9110624, International Institute of Social and Economic Sciences.
    • Handle: RePEc:sek:iacpro:9110624
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    File URL: https://iises.net/proceedings/iises-international-academic-conference-rome/table-of-content/detail?cid=91&iid=006&rid=10624
    File Function: First version, 2019
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    More about this item

    Keywords

    Coding Bias; Artificial Intelligence; Data Scientists; Leadership Development; Business Performance; Digital Workforce Solutions; Behavioral Analytics; Twenty-First Century Skills Gaps; Human Capital Management; STEM; Enterprise Risk Management.;
    All these keywords.

    JEL classification:

    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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