In this paper we focus on the importance of interpreting the quality of the input of predictive models (potentially a GI, i.e., a Garbage In) to make sense of the reliability of their output (potentially a GO, a Garbage Out) in support of human decision making, especially in critical domains, like medicine. To this aim, we propose a framework where we distinguish between the Gold Standard (or Ground Truth) and the set of annotations from which this is derived, and a set of quality dimensions that help to assess and interpret the AI advice: fineness, trueness, representativeness, conformity, dryness. We then discuss implications for obtaining more informative training sets and for the design of more usable Decision Support Systems.
Cabitza, F., Campagner, A., Ciucci, D. (2019). New Frontiers in Explainable AI: Understanding the GI to Interpret the GO. In International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE 2019) (pp.27-47). Springer Verlag [10.1007/978-3-030-29726-8_3].
New Frontiers in Explainable AI: Understanding the GI to Interpret the GO
Cabitza F.
;Campagner A.;Ciucci D.
2019
Abstract
In this paper we focus on the importance of interpreting the quality of the input of predictive models (potentially a GI, i.e., a Garbage In) to make sense of the reliability of their output (potentially a GO, a Garbage Out) in support of human decision making, especially in critical domains, like medicine. To this aim, we propose a framework where we distinguish between the Gold Standard (or Ground Truth) and the set of annotations from which this is derived, and a set of quality dimensions that help to assess and interpret the AI advice: fineness, trueness, representativeness, conformity, dryness. We then discuss implications for obtaining more informative training sets and for the design of more usable Decision Support Systems.
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