| Title | Measure Utility, Gain Trust: Practical Advice for XAI Researchers |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Davis, B., Glenski, M., Sealy, W., Arendt, D. |
| Conference Name | 2020 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) |
| Keywords | Computational modeling, computing methodologies, Data models, Deep Learning, HCI design and evaluation methods, human computer interaction (hci), human-centered computing, machine learning, machine learning algorithms, machine learning approaches, Neural networks, pubcrawl, Resiliency, Scalability, Task Analysis, user studies, visual analytics, xai |
| Abstract | Research into the explanation of machine learning models, i.e., explainable AI (XAI), has seen a commensurate exponential growth alongside deep artificial neural networks throughout the past decade. For historical reasons, explanation and trust have been intertwined. However, the focus on trust is too narrow, and has led the research community astray from tried and true empirical methods that produced more defensible scientific knowledge about people and explanations. To address this, we contribute a practical path forward for researchers in the XAI field. We recommend researchers focus on the utility of machine learning explanations instead of trust. We outline five broad use cases where explanations are useful and, for each, we describe pseudo-experiments that rely on objective empirical measurements and falsifiable hypotheses. We believe that this experimental rigor is necessary to contribute to scientific knowledge in the field of XAI. |
| DOI | 10.1109/TREX51495.2020.00005 |
| Citation Key | davis_measure_2020 |
Measure Utility, Gain Trust: Practical Advice for XAI Researchers