Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.17534v1</id>\n    <title>Informative Semi-Factuals for XAI: The Elaborated Explanations that People Prefer</title>\n    <updated>2026-03-18T09:42:46Z</updated>\n    <link href='https://arxiv.org/abs/2603.17534v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.17534v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>Recently, in eXplainable AI (XAI), $\\textit{even if}$ explanations -- so-called semi-factuals -- have emerged as a popular strategy that explains how a predicted outcome $\\textit{can remain the same}$ even when certain input-features are altered. For example, in the commonly-used banking app scenario, a semi-factual explanation could inform customers about better options, other alternatives for their successful application, by saying \"$\\textit{Even if}$ you asked for double the loan amount, you would still be accepted\". Most semi-factuals XAI algorithms focus on finding maximal value-changes to a single key-feature that do $\\textit{not}$ alter the outcome (unlike counterfactual explanations that often find minimal value-changes to several features that alter the outcome). However, no current semi-factual method explains $\\textit{why}$ these extreme value-changes do not alter outcomes; for example, a more informative semi-factual could tell the customer that it is their good credit score that allows them to borrow double their requested loan. In this work, we advance a new algorithm -- the $\\textit{informative semi-factuals}$ (ISF) method -- that generates more elaborated explanations supplementing semi-factuals with information about additional $\\textit{hidden features}$ that influence an automated decision. Experimental results on benchmark datasets show that this ISF method computes semi-factuals that are both informative and of high-quality on key metrics. Furthermore, a user study shows that people prefer these elaborated explanations over the simpler semi-factual explanations generated by current methods.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n    <published>2026-03-18T09:42:46Z</published>\n    <arxiv:primary_category term='cs.AI'/>\n    <author>\n      <name>Saugat Aryal</name>\n    </author>\n    <author>\n      <name>Mark T. Keane</name>\n    </author>\n  </entry>"
}