Armanda Lewis | Welcome to the Center for Responsible AI at New York University

Senior Research Scientist – al861(at) nyu.edu –

Armanda is a doctoral candidate at NYU working at the intersection of the learning sciences, AI, and ethical HCI. Her dissertation, overseen by Professors Xavier Ochoa, Todd Gureckis, and He He, explores open questions on using multimodal large language models to model inclusion and facilitate how people can learn to be more inclusive. Her work as a senior research scientist at the Center for R/AI advances research on how people teach and learn about responsible AI topics. She holds an M.S. from the NYU Center for Data Science, a masters in interactive technology from NYU Tisch, and a doctorate in literature and ethics from Columbia University. In her free moments, she likes to spend time with her family, create interactive textile art, and read.

Selected publications

Using Case Studies to Teach Responsible AI to Industry Practitioners

Julia Stoyanovich, Rodrigo Kreis Paula, Armanda Lewis, and Chloe Zheng

CoRR 2024

Cite PDF

@article{DBLP:journals/corr/abs-2407-14686,
  author = {Stoyanovich, Julia and de Paula, Rodrigo Kreis and Lewis, Armanda and Zheng, Chloe},
  title = {Using Case Studies to Teach Responsible {AI} to Industry Practitioners},
  journal = {CoRR},
  volume = {abs/2407.14686},
  year = {2024},
  doi = {10.48550/ARXIV.2407.14686},
  eprinttype = {arXiv},
  eprint = {2407.14686},
  timestamp = {Thu, 22 Aug 2024 15:43:26 +0200},
  biburl = {https://dblp.org/rec/journals/corr/abs-2407-14686.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  keywords = {working,education}
}

Teaching Responsible Data Science

Armanda Lewis, and Julia Stoyanovich

International Journal of Artificial Intelligence in Education (IJAIED) 2022

Cite PDF online resource

@article{LewisStoyanovich21,
  author = {Lewis, Armanda and Stoyanovich, Julia},
  title = {Teaching Responsible Data Science},
  journal = {International Journal of Artificial Intelligence in Education (IJAIED)},
  volume = {32},
  number = {3},
  pages = {783--807},
  year = {2022},
  keywords = {journal,education,rds},
  addendum = {Special Issue: The FATE of AI in Education: Fairness, Accountability, Transparency, and Ethics},
  doi = {https://doi.org/10.1007/s40593-021-00241-7},
  site = {https://dataresponsibly.github.io/rds/},
}