Correcting User Decisions Based on Incorrect Machine Learning Decisions
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916483216441344 |
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| author | Goldberg, Saveli Salnikov, Lev Kaiser, Noor Srivastava, Tushar Pinsky, Eugene |
| author_facet | Goldberg, Saveli Salnikov, Lev Kaiser, Noor Srivastava, Tushar Pinsky, Eugene |
| contents | . It is typically assumed that for the successful use of machine learning algorithms, these algorithms should have a higher accuracy than a human expert. Moreover, if the average accuracy of ML algorithms is lower than that of a human expert, such algorithms should not be considered and are counter-productive. However, this is not always true. We provide strong statistical evidence that shows that even if a human expert is more accurate than a machine, an interaction with such a machine is beneficial when communication with the machine is non-public. The existence of a conflict between the user and ML model, and the private nature of user-AI communication will have the effect of making the user think about their decision and hence increase overall accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10474 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Correcting User Decisions Based on Incorrect Machine Learning Decisions Goldberg, Saveli Salnikov, Lev Kaiser, Noor Srivastava, Tushar Pinsky, Eugene Human-Computer Interaction . It is typically assumed that for the successful use of machine learning algorithms, these algorithms should have a higher accuracy than a human expert. Moreover, if the average accuracy of ML algorithms is lower than that of a human expert, such algorithms should not be considered and are counter-productive. However, this is not always true. We provide strong statistical evidence that shows that even if a human expert is more accurate than a machine, an interaction with such a machine is beneficial when communication with the machine is non-public. The existence of a conflict between the user and ML model, and the private nature of user-AI communication will have the effect of making the user think about their decision and hence increase overall accuracy. |
| title | Correcting User Decisions Based on Incorrect Machine Learning Decisions |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2411.10474 |