Vulnerability of Counterfactual Explanations to Data Poisoning Attacks

Core Concepts

Data poisoning can significantly increase the cost of recourse provided by counterfactual explanations, making it harder for individuals to take actionable steps to change an unfavorable outcome.

Abstract

The paper studies the vulnerability of counterfactual explanations, a popular method for providing computational recourse, to data poisoning attacks. The authors formalize different levels of data poisoning (local, sub-group, and global) that aim to increase the cost of recourse.
Key highlights:

Theoretical analysis shows that injecting a training sample on the decision boundary can increase the cost of recourse locally.
The authors propose a data poisoning algorithm that constructs poisonous instances close to the decision boundary to maximize the impact on the cost of recourse.
Empirical evaluation on benchmark datasets, classifiers, and state-of-the-art counterfactual generation methods demonstrates the vulnerability of existing approaches to data poisoning.
Even a small amount of poisonous instances can significantly increase the cost of recourse, both locally and globally.
The increase in cost of recourse is more challenging to achieve for sub-groups due to the overlap in data distributions.
The authors discuss the need for more robust counterfactual generation methods and defense mechanisms against malicious data manipulations.

Stats

The cost of recourse increases by up to 15.24 for the Crime dataset when using a linear SVC classifier and the FACE counterfactual generation method with 40% poisoned samples.
The difference in cost of recourse between protected groups increases by up to 14.32 for the Crime dataset when using a random forest classifier and the FACE counterfactual generation method with 40% poisoned samples.

Quotes

"Data poisoning can be done offline [Lin et al., 2021] or online [Tolpegin et al., 2020]. It only makes small changes to the training data such as changing labels, removing samples, or adding new instances, that are likely to remain unnoticed."
"Since counterfactuals state actionable recommendations that are to be executed in the real-world, manipulated explanations would directly affect the individuals by enforcing more costly actions or hiding some information from them."

Key Insights Distilled From

The Effect of Data Poisoning on Counterfactual Explanations

by Andr... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2402.08290.pdf

Deeper Inquiries

How can we design counterfactual explanation methods that are robust to data poisoning attacks

To design counterfactual explanation methods that are robust to data poisoning attacks, several strategies can be implemented:

Data Augmentation Techniques: By augmenting the training data with diverse and realistic instances, the model can learn to generalize better and be less susceptible to data poisoning attacks.

Outlier Detection: Implementing outlier detection mechanisms during the training phase can help identify and mitigate the impact of poisoned data points.

Regularization: Introducing regularization techniques can help prevent overfitting to poisoned data and promote generalization to unseen instances.

Adversarial Training: Training the model with adversarial examples can enhance its robustness to data poisoning attacks by exposing it to various perturbations during training.

Feature Engineering: Careful feature selection and engineering can help reduce the impact of poisoned data on the model's decision boundaries.

Model Monitoring: Continuously monitoring the model's performance and behavior can help detect anomalies caused by data poisoning and trigger appropriate responses.

What other types of attacks, beyond data poisoning, could threaten the reliability of counterfactual explanations

Beyond data poisoning, other types of attacks that could threaten the reliability of counterfactual explanations include:

Adversarial Attacks: Adversarial attacks involve crafting malicious inputs to deceive the model into making incorrect predictions, which can also impact the counterfactual explanations provided.

Model Inversion Attacks: In model inversion attacks, adversaries attempt to reverse-engineer the model by exploiting the information revealed in the counterfactual explanations, compromising the privacy of the individuals involved.

Membership Inference Attacks: Adversaries may launch membership inference attacks to determine if a specific individual's data was used in the model training, potentially leading to privacy breaches.

Model Manipulation Attacks: Attackers could manipulate the model's decision boundaries by injecting biased or misleading data, affecting the accuracy and fairness of the counterfactual explanations.

Data Poisoning Attacks: Apart from the direct impact on model training, data poisoning attacks can also target the counterfactual explanations, leading to misleading or harmful recommendations.

How can we develop effective defense mechanisms to protect counterfactual explanations from malicious manipulations while preserving their usefulness for providing computational recourse

Developing effective defense mechanisms to protect counterfactual explanations from malicious manipulations while preserving their usefulness for providing computational recourse involves the following strategies:

Input Sanitization: Implementing input validation and sanitization techniques to filter out potentially poisoned data before it affects the model training process.

Anomaly Detection: Utilizing anomaly detection algorithms to identify unusual patterns in the data that may indicate data poisoning attempts.

Explainability Verification: Incorporating explainability verification techniques to ensure that the generated counterfactual explanations align with the model's decision-making process and are not influenced by malicious inputs.

Model Robustness Testing: Conducting robustness testing to evaluate the model's resilience against various attacks, including data poisoning, and refining the counterfactual generation process accordingly.

Privacy Preservation: Implementing privacy-preserving measures to safeguard sensitive information revealed in the counterfactual explanations and prevent unauthorized access.

Adversarial Training: Training the model with adversarial examples specifically designed to mimic data poisoning attacks can enhance its resilience and improve the accuracy of counterfactual explanations in the presence of malicious inputs.