Fairness Without Demographics in Repeated Loss Minimization (1806.08010v2)

Abstract: Machine learning models (e.g., speech recognizers) are usually trained to minimize average loss, which results in representation disparity---minority groups (e.g., non-native speakers) contribute less to the training objective and thus tend to suffer higher loss. Worse, as model accuracy affects user retention, a minority group can shrink over time. In this paper, we first show that the status quo of empirical risk minimization (ERM) amplifies representation disparity over time, which can even make initially fair models unfair. To mitigate this, we develop an approach based on distributionally robust optimization (DRO), which minimizes the worst case risk over all distributions close to the empirical distribution. We prove that this approach controls the risk of the minority group at each time step, in the spirit of Rawlsian distributive justice, while remaining oblivious to the identity of the groups. We demonstrate that DRO prevents disparity amplification on examples where ERM fails, and show improvements in minority group user satisfaction in a real-world text autocomplete task.

Fairness Without Demographics in Repeated Loss Minimization

The paper "Fairness Without Demographics in Repeated Loss Minimization" explores a novel approach to achieving fairness in machine learning models without the explicit use of demographic data. This work is conducted by researchers from Stanford University, including Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. The authors propose an innovative alignment of fairness in machine learning, potentially circumventing the ethical and privacy concerns associated with collecting sensitive demographic information.

Methodology and Approach

The authors introduce a framework that addresses fairness by focusing on repeated loss minimization techniques. This strategy shifts the standard practice from a demographically-aware to a demographically-agnostic paradigm. The methodology hinges on adjusting model performance iteratively to minimize disparities between different, albeit non-demographic, subgroups derived from the data distribution.

Central to the authors’ approach is the optimization process, where the loss function is modified to reduce biases without any explicit demographic labels. This reliance on indirect indicators aims to preclude the need for sensitive data, thereby preserving individual privacy and reducing potential bias introduced by institutional assumptions about demographic categories.

Experiments and Results

Empirical evaluations were conducted to substantiate the potential of this demographic-agnostic framework. The experiments demonstrated that the proposed approach effectively mitigates bias in repeated decision-making scenarios. Crucially, the authors report numerical results indicating successful minimization of loss disparities, aligning the model's effectiveness across different groups. These findings highlight that fairness can be achieved even in the absence of demographic data, offering a valuable contribution to the ongoing dialogue around bias in machine learning systems.

Implications and Future Directions

The implications of this research are manifold. Practically, the ability to achieve fairness without demographic information opens avenues for deploying models in environments where privacy concerns or legal constraints make demographic data collection impractical. Theoretically, the results encourage reevaluation of fairness constructs in machine learning, suggesting new directions that familiarize with distributional fairness concepts.

Future developments sparked by this research could examine further refining the accuracy of subgroup delineations in the absence of demographic data, potentially employing advanced clustering techniques or adversarial learning for enhanced subgroup identification. Additionally, the exploration of applicability across various domains, such as healthcare or finance, where demographic neutrality could yield substantial benefits, presents a promising avenue for extending this work.

In conclusion, "Fairness Without Demographics in Repeated Loss Minimization" presents a compelling case for reconsidering traditional fairness paradigms. By demonstrating successful bias mitigation without relying on demographic data, the authors contribute to more ethical and privacy-conscious machine learning practices, paving the way for future innovations in AI fairness methodologies.