ΑΙhub.org
 

One Hundred Year Study on Artificial Intelligence (AI100) – a panel discussion at #IJCAI-PRICAI 2020


by
21 January 2021



share this:
IJCAI-PRICAI2020 LOGO

One of the panel discussions at IJCAI-PRICAI 2020 focussed on the One Hundred Year Study on Artificial Intelligence (AI100). The mission of AI100 is to launch a study every five years, over the course of a century, to better track and anticipate how artificial intelligence propagates through society, and how it shapes different aspects of our lives. This IJCAI session brought together some of the people involved in the AI100 initiative to discuss their efforts and the direction of the project.

Taking part in the panel discussion were:

  • Mary L Gray (Microsoft Research & Harvard University)
  • Peter Stone (University of Texas at Austin)
  • David Robinson (Cornell University)
  • Johannes Himmelreich (Syracuse University)
  • Thomas Arnold (Tufts University)
  • Russ Altman (Stanford University)

The goals of the AI100 are “to support a longitudinal study of AI advances on people and society, centering on periodic studies of developments, trends, futures, and potential disruptions associated with the developments in machine intelligence, and formulating assessments, recommendations and guidance on proactive efforts”.

Working on the AI100 project are a standing committee and a study panel. The first study panel report, released in 2016, can be read in full here. This reports provide insights from people who work closely in the field, in part to counter the external perceptions and the hype which surround AI, and to accurately portray what is going on in the field. The intended audience for this report is broad, ranging from AI researchers to the general public, from industry to policy makers.

The second study panel report, expected in late 2021, is now underway. It will be based, in part, on two study-workshops commissioned by the AI100 standing committee, one entitled “Coding Caring” and the other “Prediction in Practice”.

In the first part of the session, the panellists discussed their experiences from the two study workshops. These study groups brought together a whole range of stakeholders, including academics (from different disciplines), start-ups, care-givers, and other practitioners. They also brought in people who had created high-stakes AI applications and found out what it was like to maintain and integrate AI applications as part of a larger system. Their aim was to address conceptual, ethical and political issues via a multidisciplinary approach. Balancing the needs of systems users, customers, start-ups and the public sector is a fiendishly difficult challenge, but one that it is necessary to address.

In the second part of the session, we heard views on the value of the AI100 initiative. AI100 allows a periodic, longitudinal view of how AI is viewed by society, and aims to report realistic hopes and concerns. AI has progressed to the point where we need to be having conversations with practitioners about the implications of deploying AI systems in settings such as care and law. AI researchers should be aware of how their work impacts society.

Find out more about the next report here.



tags:


Lucy Smith is Senior Managing Editor for AIhub.
Lucy Smith is Senior Managing Editor for AIhub.

            AUAI is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

2026 AAAI / ACM SIGAI Doctoral Consortium interviews compilation

  08 Oct 2026
We collate our interviews with the 2026 cohort of doctoral consortium participants.

Machine learning for clinical time-series forecasting: an interview with Mayra Elwes

  07 Oct 2026
Our series hearing from the IJCAI-ECAI doctoral consortium participants continues.

Can you teach yourself to detect AI writing? Maybe

  06 Oct 2026
Before generative AI, we could generally assume that written text had been composed by a human. This is no longer the case. So how can we spot AI-written text?

Interview with William Yijiang Li: vision language models and the physical world

  05 Oct 2026
How effective are vision language models at understanding how the physical world changes over time?

Forthcoming machine learning and AI seminars: October 2026 edition

  02 Oct 2026
A list of free-to-attend AI-related seminars that are scheduled to take place in the next couple of months.

Rebuilding the brain with neuromorphic computing: an interview with Oliver Rhodes

  01 Oct 2026
Neuromorphic computing takes inspiration from biology to build faster, more energy efficient systems.
monthly digest

AIhub monthly digest: September 2026 – tracking animal populations, recommender systems, and an interview with Ken Goldberg

  29 Sep 2026
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.


↑


AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence