4th AAAI/ACM Conference on AI, Ethics, and Society
4th AAAI/ACM Conference on AI, Ethics, and Society
One of the top CS and interdisciplinary conferences on AI ethics, co-chaired by PI Professor Seth Lazar, featuring papers by Research Fellows Claire Benn and Pamela Robinson. Conference website here.
Over the last few years, the world has awoken to the power that we have vested—often without thought or care—in the people and systems that collect, aggregate, analyse, and act on our data. At the same time, AI systems promise new ways to empower individuals and collectives to change society from the bottom up. International organisations, governments, universities, corporations, and philanthropists have recognised the urgent need to bring all of our intellectual tools to bear on charting a course through this uncertain new territory. Earlier iterations of this conference and others have seen the first fruits of these calls to action, as programs for research have been set out in many fields relevant to AI, Ethics, and Society.
The early days of shaking us awake are done: we now know, well, that we are increasingly reliant on AI systems that are radically changing the world around us, for better and worse. The next step is to chart a course forward, both by deepening our diagnosis of where we are now, and by developing new goals, models, and technical and regulatory systems to shape the future of AI and society toward how we collectively intend our societies to look.
To achieve these twin objectives—a richer understanding of where we are now, and technical and socio-technical paths forward—we must draw on insights from across disciplines. AIES is convened each year by program co-chairs from Computer Science, Law and Policy, the Social Sciences, and Philosophy. Our goal is to encourage talented scholars in these and related fields to submit their best work related to the morality, law, and political economy of data and AI. Papers should be tailored for a multi-disciplinary audience without sacrificing excellence. In addition to the community of scholars who have participated in these discussions from the outset, we want to explicitly welcome disciplinary experts who are newer to this topic, and see ways to break new ground in their own fields by thinking about data and AI.
The proceedings of AIES 21, edited by Lazar et al, have now been published here.