Bounding boxes are often used to communicate automatic object detection results to humans, aiding humans in a multitude of tasks. We investigate the relationship between bounding box localization errors and human task performance. We use observer performance studies on a visual multi-object counting task to measure both human trust and performance with different levels of bounding box accuracy. The results show that localization errors have no significant impact on human accuracy or trust in the system. Recall and precision errors impact both human performance and trust, suggesting that optimizing algorithms based on the F1 score is more beneficial in human-computer tasks. Lastly, the paper offers an improvement on bounding boxes in multi-object counting tasks with center dots, showing improved performance and better resilience to localization inaccuracy.

Clinical decision support systems is a collection name for a lot of Artificial Intelligence systems used in healthcare. These systems are designed to help health workers make decisions faster and make the healthcare environment as a whole more efficient. Decisions made by these systems often weigh heavy on the ethical side giving advice on what kind of care a patient receives. The ethics of these decision mean it is even more important for these systems to act fair and unbiased towards all patients. This however is not always the case though. This paper will explain and dissect the issues of unfairness within clinical decision support systems and will compare and adapt different socio-technical design methodologies to form a advice on designing for fair clinical decision support algorithms.