AI2030 Responsible AI Tools Marketplace

Key Takeaways

Surrogate models may hide discriminatory patterns: The research demonstrates that even when an AI system systematically discriminates against a specific group, a simpler "surrogate" decision tree used to explain it can bury the discriminatory question deep within the tree structure, making it difficult for humans to notice the unfair behavior.
Explainability tools can be manipulated: The findings show that operators of AI systems could potentially push discriminatory decision rules to lower levels of explanation trees "as low as wanted," suggesting that the transparency provided by surrogate models is not guaranteed and could be deliberately obscured.
Current XAI approaches have blind spots: The paper challenges the assumption that surrogate decision trees would naturally surface discriminatory patterns at early, visible levels—revealing that this common expectation about how explainability tools work may be fundamentally flawed.
Fairness requires active verification: The research implies that simply using a simpler model to explain a complex AI system is insufficient for ensuring fairness; organizations need additional mechanisms to actively verify that discriminatory patterns aren't being hidden within explanation structures.
Technical safeguards needed for responsible AI: The work highlights that explainable AI methods require careful scrutiny and validation to prevent them from becoming tools that obscure rather than reveal problematic algorithmic decision-making in high-stakes applications.

Abstract

In recent years, the number of new applications for highly complex AI systems has risen significantly. Algorithmic decision-making systems (ADMs) are one of such applications, where an AI system replaces the decision-making process of a human expert. As one approach to ensure fairness and transparency of such systems, explainable AI (XAI) has become more important. One variant to achieve explainability are surrogate models, i.e., the idea to train a new simpler machine learning model based on the input-output-relationship of a black box model. The simpler machine learning model could, for example, be a decision tree, which is thought to be intuitively understandable by humans. However, there is not much insight into how well the surrogate model approximates the black box. Our main assumption is that a good surrogate model approach should be able to bring such a discriminating behavior to the attention of humans; prior to our research we assumed that a surrogate decision tree would identify such a pattern on one of its first levels. However, in this article we show that even if the discriminated subgroup - while otherwise being the same in all categories - does not get a single positive decision from the black box ADM system, the corresponding question of group membership can be pushed down onto a level as low as wanted by the operator of the system. We then generalize this finding to pinpoint the exact level of the tree on which the discriminating question is asked and show that in a more realistic scenario, where discrimination only occurs to some fraction of the disadvantaged group, it is even more feasible to hide such discrimination. Our approach can be generalized easily to other surrogate models.

24 pages, 7 figures

Key Takeaways

Surrogate models may hide discriminatory patterns: The research demonstrates that even when an AI system systematically discriminates against a specific group, a simpler "surrogate" decision tree used to explain it can bury the discriminatory question deep within the tree structure, making it difficult for humans to notice the unfair behavior.
Explainability tools can be manipulated: The findings show that operators of AI systems could potentially push discriminatory decision rules to lower levels of explanation trees "as low as wanted," suggesting that the transparency provided by surrogate models is not guaranteed and could be deliberately obscured.
Current XAI approaches have blind spots: The paper challenges the assumption that surrogate decision trees would naturally surface discriminatory patterns at early, visible levels—revealing that this common expectation about how explainability tools work may be fundamentally flawed.
Fairness requires active verification: The research implies that simply using a simpler model to explain a complex AI system is insufficient for ensuring fairness; organizations need additional mechanisms to actively verify that discriminatory patterns aren't being hidden within explanation structures.
Technical safeguards needed for responsible AI: The work highlights that explainable AI methods require careful scrutiny and validation to prevent them from becoming tools that obscure rather than reveal problematic algorithmic decision-making in high-stakes applications.

Abstract

24 pages, 7 figures

Hacking a surrogate model approach to XAI

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links

Hacking a surrogate model approach to XAI

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links