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、公平性缺失及可解释性不足等关键问题，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研究方向包括特征选择方法（如Anchors）和特征归因技术（如LIME和SHAP）。尽管前景广阔，现有特征选择与归因方法普遍存在解释不严谨、分布外采样等严重缺陷。近期提出的形式化可解释人工智能方法虽可规避上述问题，但仍面临若干局限：除可扩展性限制外，该方法无法解决特征归因问题；且形式化解释虽具备严格逻辑正确性，但其规模通常过大，限制了实际应用场景的可行性。基于此，本文提出通过形式化解释枚举机制，将形式化可解释人工智能框架应用于特征归因问题。论证表明，形式特征归因相较于现有形式化与非形式化方法具有显著优势。针对该问题的实际计算复杂度，本文进一步提出高效近似精确形式特征归因的技术方案。最终通过实验证明，所提出的近似形式特征归因方法不仅在特征重要性指标上优于现有特征归因算法，在特征相对排序性能方面同样表现卓越。