2021/05/14 by Cor Steging, Silja Renooij, Steging, Cor +3
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Law #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Software Engineering Research #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.2105.06758
21 pages
arxiv created 2021/05/14 · openalex publication_date 2021/05/14 · arxiv updated 2021/05/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In AI and law, systems that are designed for decision support should be explainable when pursuing justice. In order for these systems to be fair and responsible, they should make correct decisions and make them using a sound and transparent rationale. In this paper, we introduce a knowledge-driven method for model-agnostic rationale evaluation using dedicated test cases, similar to unit-testing in professional software development. We apply this new method in a set of machine learning experiments aimed at extracting known knowledge structures from artificial datasets from fictional and non-fictional legal settings. We show that our method allows us to analyze the rationale of black-box machine learning systems by assessing which rationale elements are learned or not. Furthermore, we show that the rationale can be adjusted using tailor-made training data based on the results of the rationale evaluation.