Visiting Research Scholar – isk273(at) nyu.edu – Personal Website
Ian is a Visiting Scholar at NYU R/AI. He graduated from NYU with an M.S. in Computer Science in 2022 and is currently a PhD student at Cornell Tech. His research is focused on building and evaluating computing technologies that aim to improve the lives of marginalized and underserved populations. In particular, he is interested in community and in-home healthcare, mental health, climate resilience, and responsible artificial intelligence. Outside of academic activities, he is an avid Muay Thai practitioner, enjoys reading, and loves to cook — plus he has been known to eat.
Selected publications
Introducing contextual transparency for automated decision systems
Mona Sloane, Ian Solano-Kamaiko, Jun Yuan, Aritra Dasgupta, and Julia Stoyanovich
@article{NMI_2023,author={Sloane, Mona and Solano-Kamaiko, Ian and Yuan, Jun and Dasgupta, Aritra and Stoyanovich, Julia},title={Introducing contextual transparency for automated decision systems},journal={Nature Machine Intelligence},volume={5},year={2023},pages={187-–195},doi={https://doi.org/10.1038/s42256-023-00623-7},keywords={journal,explainability,policy,hiring},}
It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy
Andrew Bell, Ian Solano-Kamaiko, Oded Nov, and Julia Stoyanovich
In Proceedings of the 5th Annual ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
@inproceedings{DBLP:conf/fat/BellSNS22,author={Bell, Andrew and Solano{-}Kamaiko, Ian and Nov, Oded and Stoyanovich, Julia},title={It's Just Not That Simple: An Empirical Study of the Accuracy-Explainability
Trade-off in Machine Learning for Public Policy},booktitle={Proceedings of the 5th Annual {ACM} Conference on Fairness, Accountability, and
Transparency, {FAccT}},pages={248--266},publisher={{ACM}},year={2022},doi={10.1145/3531146.3533090},timestamp={Wed, 22 Jun 2022 10:20:30 +0200},bibsource={dblp computer science bibliography, https://dblp.org},keywords={explainability, transparency, policy,governance},}
