3 research outputs found
Steps Towards Value-Aligned Systems
Algorithmic (including AI/ML) decision-making artifacts are an established
and growing part of our decision-making ecosystem. They are indispensable tools
for managing the flood of information needed to make effective decisions in a
complex world. The current literature is full of examples of how individual
artifacts violate societal norms and expectations (e.g. violations of fairness,
privacy, or safety norms). Against this backdrop, this discussion highlights an
under-emphasized perspective in the literature on assessing value misalignment
in AI-equipped sociotechnical systems. The research on value misalignment has a
strong focus on the behavior of individual tech artifacts. This discussion
argues for a more structured systems-level approach for assessing
value-alignment in sociotechnical systems. We rely primarily on the research on
fairness to make our arguments more concrete. And we use the opportunity to
highlight how adopting a system perspective improves our ability to explain and
address value misalignments better. Our discussion ends with an exploration of
priority questions that demand attention if we are to assure the value
alignment of whole systems, not just individual artifacts.Comment: Original version appeared in Proceedings of the 2020 AAAI ACM
Conference on AI, Ethics, and Society (AIES '20), February 7-8, 2020, New
York, NY, USA. 5 pages, 2 figures. Corrected some typos in this versio
Personal Universes: A Solution to the Multi-Agent Value Alignment Problem
AI Safety researchers attempting to align values of highly capable intelligent systems with those of humanity face a number of challenges including personal value extraction, multi-agent value merger and finally in-silico encoding. State-of-the-art research in value alignment shows difficulties in every stage in this process, but merger of incompatible preferences is a particularly difficult challenge to overcome. In this paper we assume that the value extraction problem will be solved and propose a possible way to implement an AI solution which optimally aligns with individual preferences of each user. We conclude by analyzing benefits and limitations of the proposed approach