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Systematizing Audit in Algorithmic Recruitment.

Emre Kazim1, Adriano Soares Koshiyama1, Airlie Hilliard2

  • 1Department of Computer Science, University College London, Gower St, London WC1E 6EA, UK.

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Summary
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This study introduces a systematic algorithm audit framework to ensure artificial intelligence (AI) recruitment tools are used responsibly and fairly. Audits are crucial for governing AI in hiring to prevent algorithmic bias and promote ethical deployment.

Keywords:
accountabilitybiascomplianceexplainabilityfairnessgovernanceprivacyrecruitmentrobustnesstransparency

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Area of Science:

  • Organizational Psychology
  • Artificial Intelligence (AI)
  • Human-Computer Interaction

Background:

  • Business psychologists assess individual differences (intelligence, personality) for workplace applications.
  • AI systems now measure these differences, leading to AI-driven recruitment solutions.
  • Concerns exist regarding algorithmic fairness and responsible deployment of AI in hiring.

Purpose of the Study:

  • To apply a systematic algorithm audit framework to AI-driven recruitment systems.
  • To explore how audits can ensure responsible and fair deployment of these technologies.
  • To address the ethical implications of algorithmic hiring tools.

Main Methods:

  • Systematic algorithm audit framework application.
  • Exploration of audit assessments for AI-driven hiring systems.
  • Identification of risks, audit opportunities, bias measurement, and transparency in algorithms.

Main Results:

  • Outlined sources of risk associated with algorithmic hiring tools.
  • Suggested optimal opportunities for conducting algorithm audits.
  • Recommended methods for measuring algorithmic bias and enhancing transparency.

Conclusions:

  • Algorithmic recruitment systems require robust governance mechanisms.
  • Systematic audits are essential for ensuring fairness and ethical use of AI in hiring.
  • Transparency and bias measurement are critical components of responsible AI deployment.