Our Papers
IBM Research papers about FactSheets and AI Governance
- FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity, Arnold, M., Bellamy, R.K.E., Hind, M., Houde, S., Mehta, S., Mojsilović, A. ,Nair, R., Natesan Ramamurthy, K., Reimer, D., Olteanu, A., Piorkowski, D., Tsay, J. and Varshney, K. R., IBM Journal of Research and Development, 63(4/5), July-Sept, 2019, first posted on arXiv, Aug, 2018
- Experiences with Improving the Transparency of AI Models and Services, Hind, M., Houde, S., Martino, J., Mojsilovic, A., Piorkowski, D., Richards, J., Varshney, K.R., ACM CHI'20 Conference on Human Factors in Computing Systems, Late-Breaking Work, Apr, 2020, first posted on arXiv, Nov, 2019
- A Human-Centered Methodology for Creating AI FactSheets, Richards, J., Piorkowski, D., Hind, M., Houde, S., Mojsilović, A., Varshney, K., Bulletin of the Technical Committee on Data Engineering, December, 2021, arXiv, June, 2020
- Towards evaluating and eliciting high-quality documentation for intelligent systems, Piorkowski, D., González, D., Richards, J., Houde, S., arXiv, Nov, 2020
- How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study, Piorkowski, D., Park, S., Wang, A.Y., Wang, D., Muller, M., Portnoy, F., arXiv, Jan, 2021
- The Sanction of Authority: Promoting Public Trust in AI, Knowles, B., Richards, J., arXiv, Jan, 2021
- Accountable Federated Machine Learning in Government: Engineering and Management Insights, Balta, D., Sellami, M., Kuhn, P., Schöpp, U., Buchinger, M., Baracaldo, N., Anwar, A., Ludwig, H., Sinn, M., Purcell, M., Altakrouri, B., International Conference on Electronic Participation, Aug, 2021
- Evaluating a Methodology for Increasing AI Transparency: A Case Study, Piorkowski, D., Richards, J., Hind, M., arXiv, Jan, 2022
- Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach, Baracaldo, N., Anwar, A., Purcell, M., Rawat, A., Sinn, M., Altakrouri, B., Balta, D., Sellami, M., Kuhn, P., Schopp, U., Buchinger, M., arXiv, Feb, 2022
- Quantitative AI Risk Assessments: Opportunities and Challenges, Piorkowski, D., Hind, M., Richards, J., arXiv, Jan, 2023