Certifying and removing disparate impact (1412.3756v3)

Abstract: What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, gender, religious practice) and an explicit description of the process. When the process is implemented using computers, determining disparate impact (and hence bias) is harder. It might not be possible to disclose the process. In addition, even if the process is open, it might be hard to elucidate in a legal setting how the algorithm makes its decisions. Instead of requiring access to the algorithm, we propose making inferences based on the data the algorithm uses. We make four contributions to this problem. First, we link the legal notion of disparate impact to a measure of classification accuracy that while known, has received relatively little attention. Second, we propose a test for disparate impact based on analyzing the information leakage of the protected class from the other data attributes. Third, we describe methods by which data might be made unbiased. Finally, we present empirical evidence supporting the effectiveness of our test for disparate impact and our approach for both masking bias and preserving relevant information in the data. Interestingly, our approach resembles some actual selection practices that have recently received legal scrutiny.

• The paper introduces a systematic approach to certify algorithmic decisions using statistical tests based on the 80% rule.
• The paper presents a novel algorithmic repair mechanism that reduces bias without significantly compromising performance.
• The paper validates its methods with extensive experiments and theoretical proofs to ensure legal compliance and ethical AI.

Certifying and Removing Disparate Impact

The paper Certifying and Removing Disparate Impact by Feldman et al. addresses significant issues in fairness within algorithmic decision-making systems, specifically focusing on the concept of disparate impact. The authors propose a framework to both certify and mitigate disparate impact when it arises due to algorithmic biases.

Key Contributions

The paper makes the following key contributions:

1. Certification of Disparate Impact:
   • The authors introduce a systematic approach to certify whether an algorithm exhibits disparate impact. Utilizing the 80% rule from legal guidelines, they design mathematical formulations to quantify biases. The certification process leverages statistical tests to determine if the decisions disproportionately affect a particular group compared to a reference group.
2. Algorithmic Repair Mechanism:
   • A novel method is proposed to repair the input data or the decision-making process itself. This repair mechanism aims to reduce or eliminate disparate impact without significantly compromising the overall performance of the algorithm. The authors provide detailed algorithms and transformations that achieve this balance.
3. Theoretical Foundations:
   • The paper establishes rigorous theoretical foundations for both the certification and repair processes. It presents formal definitions, lemmas, and theorems that underlie the statistical and algorithmic techniques employed. This theoretical guarantee is crucial for dependability and extends the understanding of fairness in machine learning models.
4. Experimental Validation:
   • Extensive experimental evaluations are conducted on several real-world datasets. The results demonstrate that their methodologies can effectively reduce disparate impact while maintaining reasonable accuracy. Notably, the experiments cover diverse domains, substantiating the generality and applicability of the proposed solutions.

Strong Numerical Results

The experiments underscore the practicality of the methodologies. For example, in scenarios where disparate impact was notably high, the proposed repair algorithm successfully reduced the bias metric below the legal threshold without more than a marginal drop in predictive accuracy. These numerical results validate the efficacy of the proposed solutions in real-world applications.

Implications and Future Work

The practical implications of this research are considerable:

• Legal Compliance: Organizations can use the proposed framework to ensure their algorithms comply with legal standards related to disparate impact, thus mitigating risks associated with biased decision-making practices.
• Ethical Standards: By certifying and repairing disparate impact, the framework promotes ethical standards and fairness in automated systems, aligning with broader societal and regulatory expectations.

The theoretical implications also suggest rich avenues for future research:

• Extended Theoretical Models: Further refinement and generalization of the theoretical models could address more complex forms of bias and intersectional fairness issues.
• Real-Time Applications: Developing real-time certification and repair mechanisms for large-scale, dynamic data streams could be a promising advancement.
• Interdisciplinary Research: Collaborations with legal experts and ethicists could enhance the legal interpretability and robustness of the fairness criteria used.

Conclusion

The paper by Feldman et al. provides a substantial contribution to the field of algorithmic fairness by presenting a robust framework for certifying and mitigating disparate impact. Their approach is both theoretically sound and practically viable, offering significant benefits for ethically aligned AI development. Future work building on this foundation could further enhance fairness and equity in automated decision-making systems.