Fairness in Deep Learning: A Computational Perspective (1908.08843v2)

Abstract: Deep learning is increasingly being used in high-stake decision making applications that affect individual lives. However, deep learning models might exhibit algorithmic discrimination behaviors with respect to protected groups, potentially posing negative impacts on individuals and society. Therefore, fairness in deep learning has attracted tremendous attention recently. We provide a review covering recent progresses to tackle algorithmic fairness problems of deep learning from the computational perspective. Specifically, we show that interpretability can serve as a useful ingredient to diagnose the reasons that lead to algorithmic discrimination. We also discuss fairness mitigation approaches categorized according to three stages of deep learning life-cycle, aiming to push forward the area of fairness in deep learning and build genuinely fair and reliable deep learning systems.

Fairness in Deep Learning: A Computational Perspective

The paper "Fairness in Deep Learning: A Computational Perspective" provides a comprehensive overview of algorithmic fairness in deep learning, highlighting the challenges and proposed methodologies to address bias in machine learning models. By focusing on computational aspects, the authors aim to elucidate how biases manifest in data-driven algorithms and propose strategies to mitigate such issues.

Summary of Key Points

1. Algorithmic Discrimination: The paper underscores the tendency of deep learning models to exhibit discriminatory behaviors against protected groups, such as race and gender, which can have significant adverse impacts on individuals and society. This discrimination often stems from biases present in the training data, which are then learned and perhaps amplified by the models.
2. Interpretability as a Diagnostic Tool: One central theme is the role of model interpretability in identifying and mitigating discriminatory behavior in deep learning models. By understanding how models make decisions, researchers can diagnose bias and take necessary corrective actions.
3. Categorization of Fairness Problems:
   • Prediction Outcome Discrimination: Models may produce biased predictions based on input features strongly correlated with sensitive attributes, like race and gender, even if these attributes are not explicitly provided to the model.
   • Prediction Quality Disparity: There exists a disparity in the performance of models across different demographic groups, often due to underrepresentation in the training datasets.
4. Measurement of Fairness: The authors review various metrics used to quantify fairness, including demographic parity, equality of opportunity, and predictive quality parity. These metrics serve as benchmarks for evaluating and improving model fairness.
5. Bias Detection and Mitigation:
   • Discrimination Detection: Techniques including local and global interpretability are discussed as methods to detect whether a model's predictions are influenced by sensitive attributes.
   • Bias Mitigation: Strategies are classified across three stages: pre-processing (modifying data), in-processing (modifying the learning algorithm), and post-processing (adjusting decisions after learning). Each method has its strengths and challenges, and the paper provides examples where these techniques have been applied effectively.
6. Challenges in Ensuring Fairness: The paper details the remaining challenges in achieving fairness, including the lack of consensus on fairness metrics, the trade-off between fairness and model performance, and difficulties in dealing with intersectional biases where multiple protected attributes intersect.

Implications and Future Directions

The implications of this research are significant for both academic exploration and practical applications. As deep learning systems are increasingly deployed in sensitive and high-stakes areas such as hiring, credit scoring, and criminal justice, ensuring these models operate fairly is paramount. The paper encourages more interdisciplinary research to tackle these challenges, involving experts from computer science, statistics, and social sciences.

Future research could focus on developing universally accepted fairness metrics, improving the transparency of machine learning models, and creating benchmark datasets that facilitate comprehensive evaluations of model fairness.

This paper lays the groundwork for ongoing efforts to ensure that machine learning models contribute positively to society by mitigating biases and ensuring fairness in their predictions and decisions.