Artificial Intelligence (AI) systems are increasingly used in high-stakes
domains of our life, increasing the need to explain these decisions and to make
sure that they are aligned with how we want the decision to be made. The field
of Explainable AI (XAI) has emerged in response. However, it faces a
significant challenge known as the disagreement problem, where multiple
explanations are possible for the same AI decision or prediction. While the
existence of the disagreement problem is acknowledged, the potential
implications associated with this problem have not yet been widely studied.
First, we provide an overview of the different strategies explanation providers
could deploy to adapt the returned explanation to their benefit. We make a
distinction between strategies that attack the machine learning model or
underlying data to influence the explanations, and strategies that leverage the
explanation phase directly. Next, we analyse several objectives and concrete
scenarios the providers could have to engage in this behavior, and the
potential dangerous consequences this manipulative behavior could have on
society. We emphasize that it is crucial to investigate this issue now, before
these methods are widely implemented, and propose some mitigation strategies.

人工智能在人类生活的高风险领域的的应用愈加普及，XAI 应运而生，但其面临的 “不一致问题” 可能会导致潜在的危险后果，因此有必要采取缓解措施。本文提供了一些调适不一致解释的方法，分析了不同的攻击方式以及行为同调调整在社会上潜在危险造成的影响。

可解释人工智能中的操作风险：不一致问题的影响

Manipulation Risks in Explainable AI: The Implications of the  Disagreement Problem

Counterfactual explanations are increasingly used as an Explainable
Artificial Intelligence (XAI) technique to provide stakeholders of complex
machine learning algorithms with explanations for data-driven decisions. The
popularity of counterfactual explanations resulted in a boom in the algorithms
generating them. However, not every algorithm creates uniform explanations for
the same instance. Even though in some contexts multiple possible explanations
are beneficial, there are circumstances where diversity amongst counterfactual
explanations results in a potential disagreement problem among stakeholders.
Ethical issues arise when for example, malicious agents use this diversity to
fairwash an unfair machine learning model by hiding sensitive features. As
legislators worldwide tend to start including the right to explanations for
data-driven, high-stakes decisions in their policies, these ethical issues
should be understood and addressed. Our literature review on the disagreement
problem in XAI reveals that this problem has never been empirically assessed
for counterfactual explanations. Therefore, in this work, we conduct a
large-scale empirical analysis, on 40 datasets, using 12 explanation-generating
methods, for two black-box models, yielding over 192.0000 explanations. Our
study finds alarmingly high disagreement levels between the methods tested. A
malicious user is able to both exclude and include desired features when
multiple counterfactual explanations are available. This disagreement seems to
be driven mainly by the dataset characteristics and the type of counterfactual
algorithm. XAI centers on the transparency of algorithmic decision-making, but
our analysis advocates for transparency about this self-proclaimed transparency

本文针对可解释人工智能中的对策解释方法（Counterfactual explanations）中存在的异议问题（the disagreement problem）进行了大规模实证研究，发现不同算法生成的解释之间存在高度异议，从而呼吁更多关于算法决策透明度的讨论和实验研究。

因果解释的分歧：透明度如何具有欺骗性

Disagreement amongst counterfactual explanations: How transparency can  be deceptive

As various post hoc explanation methods are increasingly being leveraged to
explain complex models in high-stakes settings, it becomes critical to develop
a deeper understanding of if and when the explanations output by these methods
disagree with each other, and how such disagreements are resolved in practice.
However, there is little to no research that provides answers to these critical
questions. In this work, we introduce and study the disagreement problem in
explainable machine learning. More specifically, we formalize the notion of
disagreement between explanations, analyze how often such disagreements occur
in practice, and how do practitioners resolve these disagreements. To this end,
we first conduct interviews with data scientists to understand what constitutes
disagreement between explanations generated by different methods for the same
model prediction, and introduce a novel quantitative framework to formalize
this understanding. We then leverage this framework to carry out a rigorous
empirical analysis with four real-world datasets, six state-of-the-art post hoc
explanation methods, and eight different predictive models, to measure the
extent of disagreement between the explanations generated by various popular
explanation methods. In addition, we carry out an online user study with data
scientists to understand how they resolve the aforementioned disagreements. Our
results indicate that state-of-the-art explanation methods often disagree in
terms of the explanations they output. Our findings also underscore the
importance of developing principled evaluation metrics that enable
practitioners to effectively compare explanations.

通过研究解释性机器学习中的不一致性问题，本文介绍了一种定量框架来形式化不同解释方法生成的解释之间的不一致性，并通过萨实证分析和在线调查了解了数据科学家如何解决这些分歧。结果表明，当今最先进的解释方法在生成解释方面经常存在分歧，强调了开发原则性评估指标以实现有效比较解释的重要性。