Recent years have witnessed the widespread use of artificial intelligence (AI) algorithms and machine learning (ML) models. Despite their tremendous success, a number of vital problems like ML model brittleness, their fairness, and the lack of interpretability warrant the need for the active developments in explainable artificial intelligence (XAI) and formal ML model verification. The two major lines of work in XAI include feature selection methods, e.g. Anchors, and feature attribution techniques, e.g. LIME and SHAP. Despite their promise, most of the existing feature selection and attribution approaches are susceptible to a range of critical issues, including explanation unsoundness and out-of-distribution sampling. A recent formal approach to XAI (FXAI) although serving as an alternative to the above and free of these issues suffers from a few other limitations. For instance and besides the scalability limitation, the formal approach is unable to tackle the feature attribution problem. Additionally, a formal explanation despite being formally sound is typically quite large, which hampers its applicability in practical settings. Motivated by the above, this paper proposes a way to apply the apparatus of formal XAI to the case of feature attribution based on formal explanation enumeration. Formal feature attribution (FFA) is argued to be advantageous over the existing methods, both formal and non-formal. Given the practical complexity of the problem, the paper then proposes an efficient technique for approximating exact FFA. Finally, it offers experimental evidence of the effectiveness of the proposed approximate FFA in comparison to the existing feature attribution algorithms not only in terms of feature importance and but also in terms of their relative order.

翻译：近年来，人工智能算法与机器学习模型得到了广泛应用。尽管取得了巨大成功，但机器学习模型的脆弱性、公平性问题以及可解释性缺失等关键挑战，亟需可解释人工智能与形式化机器学习模型验证领域的积极发展。可解释人工智能的两大主要研究方向包括特征选择方法（如Anchors）和特征归因技术（如LIME与SHAP）。然而，现有的大多数特征选择与归因方法存在解释不严谨、分布外采样等一系列关键问题。尽管新兴的形式化可解释人工智能（FXAI）方法可规避上述缺陷，但其自身仍存在若干局限性。例如除可扩展性限制外，形式化方法无法解决特征归因问题；此外，形式化解释虽具有严谨性，但通常规模过大，限制了其实用性。基于此，本文提出一种通过枚举形式化解释实现形式化XAI在特征归因任务中的应用框架。论证表明，形式化特征归因在方法论上优于现有形式化与非形式化方法。鉴于实际问题的计算复杂度，本文进一步提出精确FFA的高效近似技术。最后，通过实验证明所提出的近似FFA方法在特征重要性排序及其相对顺序方面均优于现有特征归因算法。