Vulnerability of Partial Dependence Plots to Adversarial Attacks: Concealing Discriminatory Behaviors in Black-Box Models

מושגי ליבה

Adversarial attacks can manipulate partial dependence plots to conceal discriminatory behaviors of black-box models while preserving most of the original model's predictions.

תקציר

The paper proposes an adversarial framework to uncover the vulnerability of permutation-based interpretation methods, particularly partial dependence (PD) plots, for machine learning tasks. The framework modifies the original black-box model to manipulate its predictions for instances in the extrapolation domain, producing deceptive PD plots that can conceal discriminatory behaviors while preserving most of the original model's predictions.
The key insights are:

PD plots can be vulnerable to adversarial attacks that exploit the extrapolation behavior of correlated features and the aggregation of heterogeneous effects during the averaging process.
The adversarial framework maintains the performance of the original black-box model while concealing biases in the predictions on real data, rendering the framework seemingly neutral when interpreting the results through PD plots.
Experiments on real-world insurance and COMPAS datasets demonstrate the effectiveness of the proposed adversarial framework in manipulating PD plots to hide discriminatory behaviors.
The findings raise concerns about the use of permutation-based interpretation methods, as the discriminatory behavior of a predictor can be intentionally hidden by tools such as PD plots.

סטטיסטיקה

The average predicted outcome when the jth column of X is replaced with the value xj is 1/n * sum(f(xj, x^(i)_-j)) for i = 1 to n.
The proportion of permuted data identified as belonging to the extrapolation domain by the classifier c(x) for feature j at value xj is 1/n * sum(c(xj, x^(i)_-j)) for i = 1 to n.
The conditional PD function, capturing the global relationship between the feature and the predicted output considering non-extrapolation data only, is (1/n - n*lambda_j(xj)) * sum(f(xj, x^(i)-j) * (1 - c(xj, x^(i)-j))) for i = 1 to n.

ציטוטים

"Our results show that it is possible to intentionally hide the discriminatory behavior of a predictor and make the black-box model appear neutral through interpretation tools like PD plots while retaining almost all the predictions of the original black-box model."
"Crucially, the first two limitations – the extrapolation behavior of correlated features and the aggregation of heterogeneous effects during the averaging process – are exploited in Section 4 to manipulate PD plot outputs."

תובנות מפתח מזוקקות מ:

Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots

by Xi Xin,Fei H... ב- arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18702.pdf

שאלות מעמיקות

How can the proposed adversarial framework be extended to other interpretation methods beyond partial dependence plots

The proposed adversarial framework can be extended to other interpretation methods beyond partial dependence plots by adapting the methodology to suit the specific characteristics of the alternative interpretation tools. For instance, if we consider Local Interpretable Model-agnostic Explanations (LIME), a popular interpretation method, the framework can be modified to generate adversarial examples that deceive LIME explanations.
To extend the framework to LIME, we would need to adjust the process of generating perturbed instances to align with LIME's methodology. Instead of focusing on manipulating the predictions for instances in the extrapolation domain, the framework would need to generate perturbed instances that are specifically tailored to mislead the local surrogate models created by LIME. By incorporating the unique features of LIME, such as the use of perturbed instances to approximate the behavior of the black-box model locally, the adversarial framework can be customized to target and deceive LIME interpretations effectively.
Similarly, for Shapley Additive Explanations (SHAP) or Accumulated Local Effects (ALE) plots, the framework can be adapted to manipulate the underlying mechanisms of these interpretation methods. By understanding the specific algorithms and principles behind each interpretation method, the adversarial framework can be tailored to generate deceptive outputs that mislead the interpretation results.
In essence, the key to extending the adversarial framework to other interpretation methods lies in understanding the unique characteristics and processes of each method and devising strategies to manipulate them effectively.

What are the potential countermeasures or defenses that can be developed to mitigate the vulnerability of interpretation methods to adversarial attacks

Countermeasures and defenses can be developed to mitigate the vulnerability of interpretation methods to adversarial attacks by implementing the following strategies:

Robustness Testing: Regularly test interpretation methods against adversarial attacks to identify vulnerabilities and weaknesses. By proactively assessing the susceptibility of interpretation tools to manipulation, practitioners can implement targeted defenses to enhance their resilience.

Feature Engineering: Prioritize feature selection and engineering techniques that reduce the impact of correlated features and extrapolation in interpretation methods. By optimizing the input data and reducing interdependencies between features, the effectiveness of adversarial attacks can be minimized.

Ensemble Methods: Utilize ensemble methods that combine multiple interpretation techniques to cross-validate and verify results. By aggregating insights from diverse interpretation methods, practitioners can mitigate the impact of adversarial attacks on individual tools.

Regularization Techniques: Incorporate regularization techniques in the training of interpretation models to prevent overfitting and enhance generalization. By imposing constraints on the model complexity, practitioners can reduce the susceptibility to adversarial manipulation.

Adversarial Training: Implement adversarial training strategies where interpretation models are trained on adversarially perturbed data to improve robustness against attacks. By exposing the models to adversarial examples during training, they can learn to recognize and mitigate deceptive inputs.

Transparency and Documentation: Maintain transparency in the interpretation process and document the limitations and vulnerabilities of the tools used. By openly acknowledging the potential risks of adversarial attacks, practitioners can take proactive measures to address them.

What are the broader implications of the findings on the use of black-box models and interpretation tools in high-stakes decision-making scenarios, such as insurance, healthcare, and criminal justice

The findings on the use of black-box models and interpretation tools in high-stakes decision-making scenarios have significant implications for industries such as insurance, healthcare, and criminal justice:

Risk Assessment and Bias Mitigation: The vulnerability of interpretation methods to adversarial attacks highlights the importance of robust risk assessment and bias mitigation strategies in decision-making processes. Practitioners must be cautious when relying on interpretation tools to ensure fair and accurate outcomes, especially in sensitive domains like insurance and criminal justice.

Regulatory Compliance: Regulators and policymakers need to consider the limitations of interpretation methods when evaluating the use of black-box models in critical applications. Enhanced regulatory guidelines and standards may be necessary to address the vulnerabilities identified in interpretation tools and ensure compliance with ethical and legal requirements.

Trust and Accountability: The findings underscore the need for transparency, accountability, and trust in the deployment of AI systems in high-stakes scenarios. Stakeholders must prioritize explainability and interpretability to maintain confidence in the decision-making processes and uphold ethical standards.

Continuous Monitoring and Evaluation: Continuous monitoring and evaluation of black-box models and interpretation tools are essential to detect and mitigate potential adversarial attacks. Practitioners should implement robust monitoring mechanisms to identify anomalies and inconsistencies in the interpretation results, ensuring the reliability and integrity of the decision-making process.

Interdisciplinary Collaboration: Collaboration between data scientists, domain experts, ethicists, and regulators is crucial to address the complex challenges posed by the use of black-box models and interpretation tools in high-stakes decision-making. By fostering interdisciplinary dialogue and cooperation, stakeholders can develop comprehensive strategies to enhance the transparency and accountability of AI systems in critical applications.