Auditing Black-box Models for Indirect Influence (1602.07043v2)

Abstract: Data-trained predictive models see widespread use, but for the most part they are used as black boxes which output a prediction or score. It is therefore hard to acquire a deeper understanding of model behavior, and in particular how different features influence the model prediction. This is important when interpreting the behavior of complex models, or asserting that certain problematic attributes (like race or gender) are not unduly influencing decisions. In this paper, we present a technique for auditing black-box models, which lets us study the extent to which existing models take advantage of particular features in the dataset, without knowing how the models work. Our work focuses on the problem of indirect influence: how some features might indirectly influence outcomes via other, related features. As a result, we can find attribute influences even in cases where, upon further direct examination of the model, the attribute is not referred to by the model at all. Our approach does not require the black-box model to be retrained. This is important if (for example) the model is only accessible via an API, and contrasts our work with other methods that investigate feature influence like feature selection. We present experimental evidence for the effectiveness of our procedure using a variety of publicly available datasets and models. We also validate our procedure using techniques from interpretable learning and feature selection, as well as against other black-box auditing procedures.

• The paper proposes an obscuring method and Gradient Feature Auditing to quantify indirect feature influence without retraining models.
• It systematically diminishes feature predictability to reveal both proxy and direct effects on model performance.
• Experimental results across diverse datasets confirm that the approach enhances fairness auditing by uncovering hidden biases.

Overview of "Auditing Black-box Models for Indirect Influence"

The paper "Auditing Black-box Models for Indirect Influence" provides a comprehensive approach to identifying and quantifying both direct and indirect influences of features in machine learning models, particularly focusing on those treated as black boxes. The motivation behind investigating indirect influence arises from sophisticated models where decision-making processes can unintentionally depend on proxy features, even when direct usage of certain attributes is prohibited or unwanted.

In essence, while numerous methodologies exist in the field of direct influence auditing - which measures the change in model performance when features are directly perturbed - the approach outlined in this paper provides a mechanism to audit the more nuanced problem of indirect influence. This is critical in applications where ethical concerns, such as bias and fairness in automated decision-making, are paramount. For example, how certain attributes like race or gender, although not explicitly used, may still influence model outcomes via associated proxy attributes like zip codes in a racially segregated area.

Methodology

The technique presented in the paper revolves around the concept of obscuring features in a dataset such that their predictive power is minimized relative to other features. This is accomplished through a detailed procedure:

1. Feature Obscuring: The feature of interest is obscured using a systematic process that modifies its distribution without retraining the model. This involves altering the feature to ensure it can no longer be effectively predicted from other remaining features.
2. Gradient Feature Auditing (GFA): This procedure computes the influence of each feature by observing the drop in accuracy when the model is evaluated on the obscured dataset. The gradual obscuring allows for a spectrum of influence measurement, indicating how partially removing the signal affects model performance.
3. Validation and Consistency: Through experiments on various datasets and models, including decision trees, SVMs, and neural networks, the paper validates the effectiveness of the obscuring method. It also explores model consistency to evaluate feature importance in cases where models might be suboptimal or exhibit noise.

Experimental Insights

The paper’s experimental section employs multiple datasets, including synthetic, Adult Income from the UCI repository, and recidivism prediction data, to demonstrate the utility of indirect influence auditing. Results consistently show that GFA can identify both direct and indirect proxies of influence, leading to an increased understanding of feature importance beyond simple correlation measures.

Notably, the analysis reveals that traditional direct influence measures, such as those from works by Henelius et al. and Datta et al., might not fully capture the composite impact of proxy variables. It emphasizes the importance of evaluating feature interactions within the context of the model and data interplay, illustrating scenarios where indirect relationships meaningfully alter decision outcomes.

Theoretical Underpinnings

The authors draw connections between their obscuring method and classical statistical tests like ANOVA, illustrating how the proposed approach generalizes some pre-existing statistical procedures. Such grounding adds theoretical robustness to the method, implying that the resulting F-test statistics post-obscuring fail to distinguish between distribution conditions, thus validating the null hypothesis that the obscured feature distributions are identical.

Practical Implications and Future Work

The novel approach to understanding indirect influence has practical implications in sensitive application domains where ensuring unbiased automated decision-making is crucial. For example, legal frameworks necessitate transparency and fairness in hiring decisions, financial lending, or criminal justice outcomes, and auditing for indirect feature influence can provide vital insights.

Future work could explore optimization of the obscuring process for efficiency, potential extensions to handle non-numeric data more effectively, and integration with ongoing developments in fairness auditing and interpretable machine learning to foster more transparent AI systems. Additionally, investigating synergy or comparison with other feature selection and audit techniques could yield a more unified framework for model auditing.

In conclusion, "Auditing Black-box Models for Indirect Influence" reinforces the importance of examining not just explicit model features but also the implicit, indirect pathways through which features may influence predictive outcomes. This work advances beyond traditional auditing methods and offers a substantive contribution to the auditing and interpretability literature in machine learning.