Abstract
AbstractWith increasing use of artificial intelligence (AI) in high-stakes contexts, a race for “trustworthy AI” is under way. However, Dorsch and Deroy (Philosophy & Technology 37, 62, 2024) recently argued that regardless of its feasibility, morally trustworthy AI is unnecessary: We should merely rely on rather than trust AI, and carefully calibrate our reliance using the reliability scores which are often available. This short commentary on Dorsch and Deroy engages with the claim that morally trustworthy AI is unnecessary and argues that since there are important limits to how good calibration based on reliability scores can be, some residual roles for trustworthy AI (if feasible) are still possible.
Funder
RISE Research Institutes of Sweden
Publisher
Springer Science and Business Media LLC
Reference18 articles.
1. Abbasian, M., Khatibi, E., Azimi, I., Oniani, D., Abad, Shakeri Hossein, Z., Thieme, A., Sriram, R., Yang, Z., Wang, Y., Lin. B, et al. (2024). Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI. NPJ Digital Medicine, 7(1), 82. https://doi.org/10.1038/s41746-024-01074-z
2. Barclay, I., Abramson, W. (2021). Identifying roles, requirements and responsibilities in trustworthy AI systems. Association for Computing Machinery, Inc, pp. 264–271. https://doi.org/10.1145/3460418.3479344
3. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
4. Cavazos, J. G., Phillips, P. J., Castillo, C. D., & O’Toole, A. J. (2020). Accuracy comparison across face recognition algorithms: Where are we on measuring race bias? IEEE Transactions on Biometrics, Behavior, and Identity Science.https://doi.org/10.1109/TBIOM.2020.3027269
5. Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21, 1–13. https://doi.org/10.1186/s12864-019-6413-7