Why Is My Classifier Discriminatory? (1805.12002v2)

Abstract: Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy. In sensitive applications such as healthcare or criminal justice, this trade-off is often undesirable as any increase in prediction error could have devastating consequences. In this work, we argue that the fairness of predictions should be evaluated in context of the data, and that unfairness induced by inadequate samples sizes or unmeasured predictive variables should be addressed through data collection, rather than by constraining the model. We decompose cost-based metrics of discrimination into bias, variance, and noise, and propose actions aimed at estimating and reducing each term. Finally, we perform case-studies on prediction of income, mortality, and review ratings, confirming the value of this analysis. We find that data collection is often a means to reduce discrimination without sacrificing accuracy.

• The paper introduces a bias-variance-noise decomposition framework to pinpoint data-related sources of discrimination.
• It demonstrates that enhancing data representativeness can effectively reduce unfair predictions without sacrificing performance.
• Empirical case studies in income, mortality, and review ratings highlight practical strategies for tackling classifier bias.

An Analytical Framework for Evaluating Fairness in Predictive Models

The paper "Why Is My Classifier Discriminatory?" by Irene Y. Chen, Fredrik D. Johansson, and David Sontag addresses a critical issue in machine learning: the fairness of classifiers, especially in high-stakes domains such as healthcare and criminal justice. As predictive models become more widespread in decision-making processes, ensuring their fairness has emerged as a vital concern. The authors propose a novel analytic approach to understanding and mitigating discrimination in classifiers through an in-depth exploration of data and model selection.

Summary of Contributions

The core argument of the paper revolves around the evaluation of fairness in context to model biases arising from data limitations, rather than solely constricting models to enforce fairness. Recognizing that fairness-accuracy trade-offs can be unfeasible, the authors emphasize the significance of data collection in addressing model discrimination without compromising performance.

The paper introduces a theoretical framework by deconstructing cost-based metrics of discrimination into bias, variance, and noise components. The authors suggest a series of strategies to estimate and reduce each component, advocating for data-driven interventions to diminish model discrimination. The paper spans three practical scenarios in predicting income, mortality, and book review ratings, validating the proposed methods.

Analytical Framework

The authors implement a bias-variance-noise decomposition to dissect the sources of discrimination in learning models. This allows them to pinpoint specific reasons—such as inadequate data or an inappropriate choice of model—that cause unfairness in predictions across protected demographic groups. This analytical dissection is essential in understanding how model biases are influenced by data characteristics and not purely by model limitations.

1. Bias is attributed to a model's inability to learn the optimal mapping from inputs to outputs due to its assumptions or expressiveness.
2. Variance arises from model sensitivity to the training dataset, typically due to small sample sizes.
3. Noise involves irreducible uncertainties inherent in the task or data, often necessitating additional predictive features.

Importantly, the framework helps in revealing when it is unjustified or inefficient to enforce fairness through model adjustments alone, especially when substantial group-based prediction errors may result from data collection deficiencies.

Key Results and Implications

Notably, through case studies, it is demonstrated that discrimination in predictive tasks can often be alleviated significantly by acquiring more representative training data. Moreover, empirical findings indicate that traditional fairness-improving restrictions on model flexibility might be redundant or counterproductive when the data-related issues predominate.

In the income prediction case paper, gender-based disparities in income prediction errors diminish noticeably as the sample size grows, corroborating the framework’s validity. The results imply potential for accurately predicting outcomes fair across different demographic segments without reducing prediction performance, challenging the common assumption of a fairness-performance trade-off.

Furthermore, the importance of identifying and addressing subgroups with disparate error rates is highlighted. Such targeted actions can guide future efforts in data augmentation or refinement of variable sets, augmenting model fairness meaningfully.

Future Directions

While this work provides an insightful framework for understanding and mitigating predictive discrimination, it opens avenues for future exploration. The paper implicitly raises questions about broader structural inequalities manifesting in dataset biases, thus encouraging the integration of fairness-aware strategies right from the dataset creation phase. Future research could probe into adaptive data sampling techniques or the development of robust bias mitigation algorithms that function concurrently within the model training process.

Additionally, the interplay between fairness and dynamic, evolving datasets appears to be a compelling domain for further inquiry, as the socio-technical landscapes and their datasets are rarely static. Another aspect meriting further exploration lies in the ethical dimensions of data interventions for fairness, weighing trade-offs between data privacy and fairness.

In conclusion, this paper's contribution is significant in grounding fairness discourse within the computational field of machine learning. By emphasizing data-centric approaches, the authors provide a new lens to researchers aiming to design models that respect societal equity while retaining high efficiency and accuracy in various critical applications.