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Evaluating Explainable AI Methods as Intentional Distortions: Separating Successful Idealizations from Deceptive Explanations


Core Concepts
Explainable AI (xAI) methods should be evaluated as intentional distortions or "idealizations" of black-box models, rather than as faithful explanations. The SIDEs framework provides a systematic approach to separate successful idealizations from deceptive explanations.
Abstract
The paper introduces the SIDEs (Separating Idealization from Deceptive Explanations) framework for evaluating explainable AI (xAI) methods. It argues that xAI methods should be viewed as engaging in "idealization" - the intentional distortion of a complex system to highlight relevant features - rather than as providing faithful explanations of black-box models. The SIDEs framework consists of four key phases: Purpose: Identifying the specific purpose(s) the xAI method is aiming to serve, such as epistemic understanding or ethical recourse. Idealization Practices: Describing the set of scientific methods and practices the xAI method uses to distort or simplify the black-box model, and justifying the legitimacy of these practices. Ideals and Rules: Evaluating whether the idealization practices embody the appropriate norms and values, and can be operationalized into concrete rules or tests that the xAI method must satisfy. User-Facing Explanations: Ensuring the way the idealized xAI model is presented to end-users aligns with the purpose and does not mislead. The paper illustrates the application of the SIDEs framework through a qualitative analysis of feature importance methods (e.g. LIME, SHAP) and counterfactual explanation methods. It finds that these leading xAI techniques often fail to meet the standards for successful idealization, suggesting the need for new idealization practices tailored to the unique requirements of xAI. The SIDEs framework provides a systematic way for xAI researchers to evaluate their methods, move beyond simplistic notions of "faithfulness", and develop new idealization practices that can balance the various purposes xAI is meant to serve.
Stats
"The ideal gas law dating back to 1834, along with its simpler cousin Boyle's law from the 1660s, are still used to explain how gases behave." "Real gases do not behave ideally. Particles are assumed not to interact (even though they do), and the actual relationship between pressure and volume is more complicated than either law lets on." "Rudin (2019) goes so far as to say that we should stop using black-box models altogether in high-stakes cases because xAI explanations 'must be wrong'." "Lakkaraju et al. (2021) found the best performing feature importance method only approached 85% agreement with the black-box model, with LIME often scoring lower." "Slack et al. (2020) were able to create explanations that hid the most salient feature for classification for SHAP and LIME." "Ghorbani et al. (2019) found such methods were highly sensitive to small changes input data."
Quotes
"Idealizations–the intentional distortions introduced to scientific theories and models–are commonplace in the natural sciences and are seen as a successful scientific tool." "It cannot simply be that the Dutch nitrogen model contains falsehoods vis a vis an idealization, or a lack of fidelity to the phenomena, since the ideal gas law does the same." "Rudin (2019) goes so far as to say that we should stop using black-box models altogether in high-stakes cases because xAI explanations 'must be wrong'."

Key Insights Distilled From

by Emily Sulliv... at arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16534.pdf
SIDEs: Separating Idealization from Deceptive Explanations in xAI

Deeper Inquiries

How can the SIDEs framework be extended to evaluate idealization practices in other areas of AI beyond explainable AI, such as in the development of machine learning models themselves

The SIDEs framework can be extended to evaluate idealization practices in other areas of AI, such as in the development of machine learning models themselves, by adapting the framework to fit the specific context and requirements of those areas. Purpose Alignment: In the context of developing machine learning models, the purpose of idealization may differ from that in xAI. Researchers would need to identify the specific purposes of idealizations in model development, such as improving accuracy, reducing computational complexity, or enhancing generalization. Idealization Practices: Researchers would need to define the idealization practices relevant to model development, considering factors like feature selection, data preprocessing, and model architecture design. Justification for these practices would be crucial in ensuring that they align with the intended purposes. Ideals and Rules: The ideals and rules phase would involve establishing the norms and values governing idealization practices in model development. This could include criteria for feature selection, regularization techniques, and hyperparameter tuning. Evaluating how well these practices embody the desired ideals and rules would be essential. User-Facing Explanations: While user-facing explanations may not be as relevant in the context of model development, the concept of conveying the idealizations made in the model development process to stakeholders or end-users could still be applicable. This could involve communicating the trade-offs and limitations of the model in a transparent and understandable manner. By applying the SIDEs framework to evaluate idealization practices in machine learning model development, researchers can ensure that the idealizations made align with the intended purposes, are justified by the underlying norms and values, and are communicated effectively to relevant stakeholders.

What are the potential ethical implications of viewing xAI methods as intentional distortions rather than faithful explanations, and how should this inform the deployment of xAI systems

Viewing xAI methods as intentional distortions rather than faithful explanations can have significant ethical implications that need to be carefully considered in the deployment of xAI systems. Transparency and Trust: Presenting xAI methods as intentional distortions may raise concerns about transparency and trust. Users may question the reliability and credibility of the explanations provided by xAI systems if they perceive them as intentionally distorted. Bias and Fairness: Intentional distortions in xAI methods could potentially amplify biases or unfairness in decision-making processes. If these distortions are not properly managed or disclosed, they could lead to discriminatory outcomes and harm to individuals or groups. Accountability and Responsibility: Treating xAI methods as intentional distortions shifts the focus to the ethical responsibility of developers and deployers. They must ensure that the distortions serve a legitimate purpose and do not lead to unintended consequences or harm. Regulatory Compliance: Ethical implications of intentional distortions in xAI methods may also have implications for regulatory compliance. Ensuring that xAI systems adhere to ethical standards and legal requirements becomes crucial in light of these distortions. In light of these implications, the deployment of xAI systems should involve thorough ethical assessments, transparency in communicating the nature of the distortions, and mechanisms for accountability and oversight to mitigate potential risks and ensure ethical use of xAI technologies.

Given the challenges in satisfying the ideals and rules of existing idealization practices like minimalist idealization, what novel idealization practices might be better suited for the unique requirements of explainable AI

Given the challenges in satisfying the ideals and rules of existing idealization practices like minimalist idealization, exploring novel idealization practices better suited for the unique requirements of explainable AI is essential for advancing the field. Contextual Idealization: Developing context-specific idealization practices that cater to the specific needs and constraints of explainable AI systems. This could involve tailoring idealizations to different types of models, datasets, or use cases to optimize performance and interpretability. Dynamic Idealization: Introducing dynamic idealization practices that adapt to changing data distributions, model behaviors, or user requirements. This could involve incorporating feedback loops or reinforcement learning mechanisms to continuously refine and adjust idealizations. Interpretable Idealization: Focusing on idealization practices that prioritize interpretability and transparency in the decision-making process. This could involve using techniques like symbolic reasoning, rule-based systems, or causal inference to create more interpretable models. Ethical Idealization: Incorporating ethical considerations into idealization practices to ensure that distortions introduced by xAI methods align with ethical principles and values. This could involve developing frameworks for ethical idealization that prioritize fairness, accountability, and transparency. By exploring and implementing novel idealization practices tailored to the unique requirements of explainable AI, researchers can address the challenges of existing idealization frameworks and enhance the effectiveness and ethical use of xAI systems.
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