Despite recent efforts to make AI systems more transparent, a general lack of trust in such systems still discourages people and organizations from using or adopting them. In this article, we first present our approach for evaluating the trustworthiness of AI solutions from the perspectives of end-user explainability and normative ethics. Then, we illustrate its adoption through a case study involving an AI recommendation system used in a real business setting. The results show that our proposed approach allows for the identification of a wide range of practical issues related to AI systems and further supports the formulation of improvement opportunities and generalized design principles. By linking these identified opportunities to ethical considerations, the overall results show that our approach can support the design and development of trustworthy AI solutions and ethically-aligned business improvement.
Semantically consistent understanding of data/content in information systems is a key issue in collaborative work settings. Even simple data -such as timestamps -can be a source of misunderstandings both in data entry and reporting tasks. Healthcare information systems generate timestamps during data entry situations. Timestamped data are used for various purposes, such as efficiency calculations, organizational development, and research. However, timestamps are often generated ambiguously in different ways: they are a source of semantic interpretation errors. In this paper we illustrate the phenomena of timestamp ambiguity in medical settings. We suggest that User Interaction Metadata (UIM) could reduce the problems caused by ambiguous and erroneous timestamps. UIM may reveal hidden heterogeneity in data interpretation. Additionally, it can be used to identify user behavior patterns that are currently unrecognizable from raw transaction data.
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