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This study addresses fairness in machine learning without sensitive attributes. We introduce methods to bound and control equalized odds violations, improving fairness audits for AI systems.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning Ethics

Background:

  • Machine learning models are increasingly used in high-stakes decisions, necessitating fairness audits.
  • Access to sensitive attributes (e.g., demographics) is often unavailable for these audits.
  • The equalized odds (EOD) definition is a key metric for assessing fairness violations.

Purpose of the Study:

  • To develop methods for auditing and controlling fairness violations in machine learning models when sensitive attributes are absent.
  • To provide computable bounds for equalized odds violations.
  • To introduce a post-processing method for provably controlling worst-case EOD.

Main Methods:

  • Derivation of tight and computable upper bounds for EOD violation in the absence of sensitive attributes.
  • Development of a novel post-processing correction technique to control worst-case EOD.
  • Analysis of the optimality of controlling EOD with respect to predicted sensitive attributes.

Main Results:

  • Established precise upper bounds that reflect the worst-case EOD violation.
  • Demonstrated a provably effective post-processing method for controlling worst-case EOD.
  • Characterized conditions under which controlling EOD via predicted attributes is optimal.

Conclusions:

  • The study provides theoretical guarantees and practical methods for ensuring fairness in machine learning without sensitive data.
  • Results are applicable under milder assumptions than prior work.
  • Experiments on synthetic and real datasets validate the proposed methods.