Michael Feffer

Bio

Michael is a fifth-year Societal Computing PhD student in the Software and Societal Systems Department at Carnegie Mellon University. Prior to coming to CMU, he graduated from MIT in 2018 with a Bachelor of Science degree in Computer Science and a Master of Engineering (MEng) degree in Electrical Engineering and Computer Science. Industry positions range from full-time software engineering at Mastercard Data & Services for 2.5 years (before starting at CMU) to summer research internships at IBM Research and Spotify (after starting at CMU).

Education

Degrees

Master of Engineering (MEng) in Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), 2018
Bachelor of Science (SB) in Computer Science and Engineering, Massachusetts Institute of Technology (MIT), 2018

Teaching

Teacher's Assistant (TA), Machine Learning, Ethics, and Society, Carnegie Mellon University (CMU), 2023
Teacher's Assistant (TA), Mathematical and Computational Foundations for Machine Learning, Carnegie Mellon University (CMU), 2022
Teacher's Assistant (TA), Intro to Machine Learning, Massachusetts Institute of Technology (MIT), 2018
Lecturer's Assistant (LA), Intro to Machine Learning, Massachusetts Institute of Technology (MIT), 2017

Research

Michael is broadly interested in examining interactions between AI and society to study how to leverage the strengths of AI while avoiding negative outcomes and impacts. Current primary areas of research are:

Algorithmic fairness
Music information retrieval
Participatory machine learning
AI for social good
Ethics and evaluation of generative AI

Publications

Feffer M., Sinha A., Deng W.H., Lipton Z.C., Heidari H. (2024) Red-Teaming for Generative AI: Silver Bullet or Security Theater? AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2024. *Best Paper Award 

Feffer M., Xu R., Sun Y., Yurochkin M. (2024) Prompt Exploration with Prompt Regression. Conference on Language Modeling (COLM), 2024.

Feffer M., Lipton Z.C., Donahue C. (2023) DeepDrake ft. BTS-GAN and TayloRVC: A Survey of Musical Deepfake Models. 2nd Workshop on Human-Centric Music Information Research (HCMIR@ISMIR), 2023. 

Feffer M., Skirpan M., Heidari H., Lipton Z.C. (2023) From Preference Elicitation to Participatory ML: A Critical Survey & Guidelines for Future Research. AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2023. 

Feffer M., Heidari H., Lipton Z.C. (2023) Moral Machine or Tyranny of the Majority? The AAAI Conference on Artificial Intelligence (AAAI), 2023.