Working with Beliefs: AI Transparency in the Enterprise

By Ajay Chander, Ramya Srinivasan, Suhas E. Chelian, Jun Wang, Kanji Uchino. 2018

43 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “• Accessibility: A minor subset of the reviewed contributions argues for explainability as the property that allows end users to get more involved in the process of improving and developing a certain ML model [37,86] .”
Explainable AI: The New 42? (2018, background): “An eﬀective explanation helps the explainer cross a cognitive valley , allowing them to update their understanding and beliefs [4].”
Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI (2022, background): “This reflects real world constraints of the diversity of goals, workflows, and backgrounds of individual stakeholders participating in the life cycles of datasets and artificial intelligence (AI) systems [10, 12, 13].”
Evaluating Explanations by Cognitive Value (2018, unknown): “There are at least four distinct kinds of users [3, 4].”
Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “The explainable techniques allow the researchers or end-users to get involved in improving the performance of the algorithm and to trust the prediction results [44].”