New tool makes AI decision-making explainable and contestable
Researchers have developed a method to explain what changes would alter an AI system's conclusions in argumentation-based reasoning. The breakthrough matters for regulated industries—finance, healthcare, law—where stakeholders increasingly demand to understand and challenge algorithmic decisions before they're implemented.
Originaltitel: Strength change explanations in quantitative argumentation
<p>In order to make argumentation-based inference contestable, it is crucial to explain what changes can achieve a desired (instead of the contested) inference result. To this end, we introduce <em>strength change explanations</em> for quantitative (bipolar) argumentation graphs. Strength change explanations describe changes to the initial strengths of a subset of the arguments in a given graph that can achieve a desired ordering based on the final strengths of some (potentially different) subset of arguments. We show that the existing notions of <em>inverse</em> and <em>counterfactual</em> problems can be reduced to strength change explanations. We also prove basic soundness and completeness properties of our strength change explanations, and demonstrate their existence and non-existence in some special cases. By applying a heuristic search, we demonstrate that we can often successfully find strength change explanations for layered graphs that are common in typical application scenarios; still, limitations remain for settings where we do not provide guarantees for the presence (or absence) of explanations.</p>