Artificial Intelligence and Work: AAAI 2019 Fall Symposium

A two and one-half day symposium was held 7–9 November 2019 in Washington, DC, USA, to discuss and plan how AI researchers will contribute to research on human work with artificial intelligence. The symposium schedule and papers are available (NB. you must be logged in to the website to see the papers).

Draft Symposium Schedule

Day 1

9:00 Welcome and overview
9:30 Opening talks
11:00 Panel
12:30 Small group discussions of research questions over lunch
2:00 Panel
3:30 Small group project discussions
6:00 Symposium reception

Discussions. The heart of the symposium will be small group discussions on research gaps and plans for future research. Example topics include public acceptance, organizational change management, legal issues, security, education and careers. Attendees will be asked to discuss what is known and what needs to be learned about these topics. Each group will have one or more of the panelists as a convenor and will select one or more members to take notes capturing the discussion. Written products of the groups (e.g., brainstorming notes) will also be captured. On the first days, groups will refine a list of possible transdisciplinary research problems, starting with those that have been identified in other RCN workshops and adding to them. Convenors will be provided with a structured agenda to guide the process of ideating, clustering and ranking research ideas. The lists will be shared as posters at the end of the day, to allow participants to see connections or even switch groups for the second day.

Day 2

9:00 Panel
11:00 Small group project discussions
12:30 Research round tables over lunch
2:00 Overview of NSF activities
2:30 Small group project discussions
4:00 Small group report back and plenary preparation
6:00 Symposium plenary session

Discussions. The breakout groups will continue work on the second day. Discussions on the second day will focus on ways to advance the research (e.g., build shared language and share resources needed for these problems), with a particular focus on what AI researchers have to contribute and where other disciplines can inform AI research. In the afternoon, the work of the breakout groups will be discussed in a plenary session of the symposium before being presented in summary in the overall plenary sessions.

Day 3

9:00 Talk
AI for a Generative Economy: intelligent systems and work in a just and sustainable future
Ron Eglash, Lionel Robert, Audrey Bennett, Kwame Robinson, Michael Lachney and William Babbitt
9:30 Small group discussions of next steps
11:00 Small group discussions of next steps
11:30 Report on plans for next steps and call to arms
12:15 Wrap up and end

Closing discussion. The symposium will close with a group design activity about what additional resources (e.g., website, shared datasets, research protocols, impact cases) participants think would be useful in supporting interdisciplinary convergence research and skills development on the topic of work with AI in the coming years. We will encourage participants to reflect on the potential for developing collaboration spaces to support research as well as what they might be able to contribute directly.

Wrap up. The final activity of the symposium will be a short evaluation of the activities and reflections on what was learned.

Symposium Theme

The symposium theme is Artificial Intelligence and Work. AI technologies are characterized by increased autonomy: an ability to learn and an ability to interact with other systems and with humans. While AI-enabled systems do not possess the generalized intelligence that humans have, they are increasingly capable of performing tasks that have traditionally been the sole purview of humans. For example, machines can now recognize images or speech in particular domains with an ability approaching or surpassing that of humans [e.g., 3]. These abilities enable them to perform useful work, sometimes more accurately than humans, with greater speed and at less cost. (For the purposes of this symposium, we adopt the definition of work from a recent NSF solicitation, NSF 19-541, “mental or physical activity to achieve tangible benefit such as income, profit, or community welfare”). The potential impacts on human work and employment are immense, calling for a coordinated research response. While the topic is touched on in many venues, there is as yet no regular conference on the topic and AAAI Symposia have not yet addressed the topic of AI and human work specifically.

Much of the rhetoric about work and AI focuses on people being put out of work by automation. But this perspective is too simplistic: tasks that can be automated rarely stand in isolation [2] and are defined by context. For example, consider an automated system that diagnoses skin cancer [3]. To be practicable, a system needs to fit the complex work of a medical practice. Someone must order the imaging, image the correct body part using the right lighting, explain the diagnosis to the patient, family members or other doctors (in varied and appropriate ways), bill insurance companies, monitor ongoing performance, anticipate potential malpractice suits and so on. All this surrounding work must adapt to an automated dermatologist and vice versa.

While AI technology is advancing quickly, the human side of the relationship—comprising people, organizations, legal frameworks and social values, to name a few—is evolving much more slowly. The result is a potential mismatch between the technologies and the organizational and individual contexts of use that risks unexpected or undesired consequences. Undesired consequences include deskilling that weakens the ability of workers to manage the system or adapt to future jobs, the creation of overly fragile systems and automation surprises, when machines behave in unexpected ways, e.g., learning anti-social behaviours.

As a result, understanding how AI systems will be used and affect human work is a pressing challenge. How we design organizations, how we design education and how we build systems will all be impacted. The likelihood of this transformed future leads to a series of questions that will anchor the proposed symposium. To name a few:

  • How are contemporary AI systems being deployed and what effects on human work are we seeing?
  • What can be done in the near future to more effectively design and deploy AI, while at the same time improving the knowledge and satisfaction of human workers?
  • Given what we know about work, what are some untapped opportunities or open challenges for AI-based systems?
  • How will technologies currently on the horizon affect work and what are reasonable anticipatory strategies, in particular, related to training and education?
  • What are possible second- and third-order effects on work of AI, both current and on the horizon, and how might these be anticipated and mitigated?
  • How do impacts differ across settings, industries, socio-economic status and geography?

Aim and Goals

To foster our goal of convergence we will bring together researchers who share an interest in work and a deep knowledge of the potentials and limitations of AI. An AAAI symposium will help ensure that the AI research community perspective is fully represented in the activities of the RCN and conversely, to enrich discussions in the AI community with perspectives from other disciplines. The symposium will provide a forum to advance three specific goals.

First, we seek to frame important research problems around work and AI. Roco et al. suggested that, “using forecasting, early signs of change, scenario setting, and other approaches, it is possible to establish a credible vision for what is desired in the longer term for a knowledge and technology field” [4:149].

Second, we want to develop a more integrated language base about these problems and issues that can span disciplines. As the NSF notes, “as disciplines interact, the knowledge, theories, methods, data, research communities and languages are increasingly intermingled or integrated” [1].

Third, we want to define needs for supporting technologies and resources that can promote convergence in the community of scholars interested in this topic.

In achieving these goals, we acknowledge the need for sustained interaction with industrial and policy partners who are an important audience for the research. In particular, we anticipate that the location in Washington, DC will enable attendance by government employees.

Participants. The maximum number of participants will be 40, to balance the desire for a high level of interaction with the desire for diversity among participants. Ideal participants for this symposium will be big picture thinkers interested in addressing the simultaneous consideration of technical, individual, group, organizational and societal issues.

Products. One product of the symposium will be a white paper based on the breakout groups summarizing what we know, priorities for needed research and possible research projects. A second outcome will be an article in a practitioner journal related to one of the topics in the workshop, co-authored by the a subset of the organizers and symposium participants. For example, work by one of the investigators and his collaborators, based on a case study of automation, was recently published in Communications of the ACM [5]. A third possibility is a journal special issue on the topic. Finally, a key success metric will be the formation of productive connections among attendees supporting interdisciplinary research and convergence.


[1] National Science Foundation. (2017). Convergence Research at NSF. Available from

[2] Chui, M., Manyika, J., & Miremadi, M. (2015, November). Four fundamentals of workplace automation. McKinsey Quarterly, 1–9. Available from: functions/digital-mckinsey/our-insights/four-fundamentals-of-workplace-automation

[3] Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. doi: 10.1038/nature21056

[4] Roco, M. C., Bainbridge, W. S., Tonn, B., & Whitesides, G. (Eds.). (2013). Convergence of Knowledge, Technology, and Society: Beyond Convergence of Nano-Bio-Info-Cognitive Technologies. World Technology Evaluation Center, Inc. Available from

[5] Seidel, S., Berente, N., Lindberg, A., Nickerson, J. V., Lyytinen, K. (2019). Autonomous tools and design work: A triple-loop approach to human-machine learning. Communications of the ACM, 62(1), 50-57, doi: 10.1145/3210753

Thursday, November 7, 2019 - 08:00 to Saturday, November 9, 2019 - 15:00