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 :

AI for science – talk recordings now available to watch

  15 Jul 2026
Watch the invited talks from the day on YouTube.

AAAI presidential panel – factuality and trustworthiness

  14 Jul 2026
Watch the latest panel discussion in the series based on the Future of AI research report from AAAI.

The secret to human ‘brilliance’ that AI just can’t match

  13 Jul 2026
New research reveals how people learn social conventions with minimal data – and why that sets us apart from LLMs.

Pre-training isn’t bitter enough

  10 Jul 2026
Given an unlabeled data stream, and a small set of verifiable downstream examples, can we use those examples during continued pre-training?

Interview with Thi Kieu Khanh Ho: Time-series anomaly detection

  09 Jul 2026
How can we teach AI systems to recognize when something unusual or abnormal is happening in complex, real-world data streams, without relying on large amounts of labeled examples?

#RoboCup2026 social media round-up

  08 Jul 2026
Find out what the teams got up to at this year's RoboCup extravaganza in Incheon.

#RoboCup2026 – humanoid league knockout stages

  06 Jul 2026
Find out who won the small, middle and large divisions in Incheon.



AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence