How to Measure Human-AI Prediction Accuracy in Explainable AI Systems
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914931535773696 |
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| author | Koujalgi, Sujay Anderson, Andrew Adenuga, Iyadunni Soneji, Shikha Dikkala, Rupika Nader, Teresita Guzman Soccio, Leo Panda, Sourav Das, Rupak Kumar Burnett, Margaret Dodge, Jonathan |
| author_facet | Koujalgi, Sujay Anderson, Andrew Adenuga, Iyadunni Soneji, Shikha Dikkala, Rupika Nader, Teresita Guzman Soccio, Leo Panda, Sourav Das, Rupak Kumar Burnett, Margaret Dodge, Jonathan |
| contents | Assessing an AI system's behavior-particularly in Explainable AI Systems-is sometimes done empirically, by measuring people's abilities to predict the agent's next move-but how to perform such measurements? In empirical studies with humans, an obvious approach is to frame the task as binary (i.e., prediction is either right or wrong), but this does not scale. As output spaces increase, so do floor effects, because the ratio of right answers to wrong answers quickly becomes very small. The crux of the problem is that the binary framing is failing to capture the nuances of the different degrees of "wrongness." To address this, we begin by proposing three mathematical bases upon which to measure "partial wrongness." We then uses these bases to perform two analyses on sequential decision-making domains: the first is an in-lab study with 86 participants on a size-36 action space; the second is a re-analysis of a prior study on a size-4 action space. Other researchers adopting our operationalization of the prediction task and analysis methodology will improve the rigor of user studies conducted with that task, which is particularly important when the domain features a large output space. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00069 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How to Measure Human-AI Prediction Accuracy in Explainable AI Systems Koujalgi, Sujay Anderson, Andrew Adenuga, Iyadunni Soneji, Shikha Dikkala, Rupika Nader, Teresita Guzman Soccio, Leo Panda, Sourav Das, Rupak Kumar Burnett, Margaret Dodge, Jonathan Human-Computer Interaction Artificial Intelligence D.2.8 Assessing an AI system's behavior-particularly in Explainable AI Systems-is sometimes done empirically, by measuring people's abilities to predict the agent's next move-but how to perform such measurements? In empirical studies with humans, an obvious approach is to frame the task as binary (i.e., prediction is either right or wrong), but this does not scale. As output spaces increase, so do floor effects, because the ratio of right answers to wrong answers quickly becomes very small. The crux of the problem is that the binary framing is failing to capture the nuances of the different degrees of "wrongness." To address this, we begin by proposing three mathematical bases upon which to measure "partial wrongness." We then uses these bases to perform two analyses on sequential decision-making domains: the first is an in-lab study with 86 participants on a size-36 action space; the second is a re-analysis of a prior study on a size-4 action space. Other researchers adopting our operationalization of the prediction task and analysis methodology will improve the rigor of user studies conducted with that task, which is particularly important when the domain features a large output space. |
| title | How to Measure Human-AI Prediction Accuracy in Explainable AI Systems |
| topic | Human-Computer Interaction Artificial Intelligence D.2.8 |
| url | https://arxiv.org/abs/2409.00069 |