Inspired by the potential of intelligent technologies to improve our lives, I began my M.S. in Computer Science at the University of Pennsylvania (UPenn) in 2020. I took the course CIS 522 Deep Learning, which repeatedly raised the question of how we – as developers – can design more ethical algorithms. Interested in how industry approaches the topic, I reached out to former colleagues and contacts, asking about how their companies manage the ethical implications of AI. Surprisingly, no one had an answer. They weren’t managing adverse outcomes at all because they didn’t know how. The problem was confirmed when I dived into research on the topic: Compared to the technical advancements in AI, the area of technical AI ethics is significantly understudied. Novel, complex autonomous systems are being developed without devoting enough time to their potential negative implications and how developers can mitigate them. Given the increasing use of such systems throughout society, this sparked my interest in contributing to research in AI ethics.
Since then, I have found more questions than answers: How can we translate abstract ethical principles to action-guiding rules for autonomous agents to use in decision situations? Can – and should – autonomous agents learn from human beings what it means to act ethically? How can we implement an ethical intuition into these agents while avoiding complex computations? And how can we design solutions that companies can use easily? With my research, I’m trying to answer some of these questions and make AI ethics understandable, practicable, and accessible.
So far, I have mostly been exposed to technical aspects of AI safety. However, I recently noticed that technical AI safety won’t be sufficient – we need supporting AI governance to ensure the safe deployment of the technology. So, besides my technical research, I’m also committed to improving the collaboration between these fields to ensure that we not only have safe AI technologies but also effective governance frameworks that guide their development and deployment.
Reuel, A., Koren, M., Corso, A., & Kochenderfer, M. (2022). Using Adaptive Stress Testing to Identify Paths to Ethical Dilemmas in Autonomous Systems. Accepted to the AAAI’s Workshop on Trustworthy Autonomous Systems Engineering 2022.
Reuel, A., Peralta, S., Sedoc, J., Sherman, G., & Ungar, L. (2021). Measuring the Language of Self-Disclosure across Corpora. Submitted to the 60th Annual Meeting of the Association for Computational Linguistics.
Python, PyTorch, Scikit Learn, R, Java, AWS