@conference{552,
  author = {Ruvarashe Madzime and Tommie Meyer and Louise Leenen},
  title = {An Override-aware Classifier for Transparent AI},
  abstract = {<p>Many real-world decisions follow rules that hold in general but allow exceptions, such as "birds usually fly, unless they are penguins." Most interpretable classifiers struggle to capture this pattern, leading to explanations that feel less aligned with human reasoning. This paper introduces the Defeasible Horn Classifier with Exceptions (DHCE), a symbolic model that makes this reasoning structure explicit. Each rule combines a default with its linked exceptions, so predictions can be explained step by step without relying on post-hoc tools. DHCE is learned using Answer Set Programming, which searches for globally optimal rule sets while balancing accuracy and simplicity. The resulting models consist of ranked Horn rules that provide full traceability: users can see both why a decision applies and why it may be overridden. We evaluate DHCE on standard classification benchmarks and find that it matches or outperforms leading interpretable models, a performance level that prior work shows to be competitive with classical machine learning classifiers. By making prediction decisions inherently retractable, DHCE delivers accuracy alongside explanations that mirror how people reason, making it suited for domains where understanding why a rule no longer applies is as important as the prediction itself.</p>},
  year = {2025},
  journal = {Proceedings of the Southern African Conference for Artificial Intelligence Research (SACAIR 2025), Volume II},
  volume = {II},
  chapter = {349-360},
  month = {2025},
  address = {Cape Town, South Africa},
  url = {https://2025.sacair.org.za/online-proceedings/Papers/paper_17.pdf},
}