Author:
Vasquez Jonathan,Gitiaux Xavier,Rangwala Huzefa
Publisher
Springer Nature Switzerland
Reference26 articles.
1. Adkins, D., et al.: Method cards for prescriptive machine-learning transparency. In: 2022 IEEE/ACM 1st International Conference on AI Engineering-Software Engineering for AI (CAIN), pp. 90–100. IEEE (2022)
2. Adkins, D., et al.: Prescriptive and descriptive approaches to machine-learning transparency. In: CHI Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–9 (2022)
3. Asuncion, A., Newman, D.: UCI machine learning repository (2007)
4. Bao, M., et al.: It’s compaslicated: the messy relationship between rai datasets and algorithmic fairness benchmarks. arXiv preprint arXiv:2106.05498 (2021)
5. Bellamy, R.K.E., et al.: AI Fairness 360: an extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias (2018)