Tag: DelibDemoc

Co-designing AI ethics in education

(Acknowledgements: DALL•E)

2024 here we come… The current frenzy around artificial intelligence in education was triggered just over a year ago by the explosive arrival of ChatGPT, which made the power of the most mature large language model ever developed, freely available to the masses via an engaging conversational user interface. Every level of the educational sector then spent 2023 grappling with the implications, and I’ve shared my small pieces of that puzzle in other blog posts. (For those interested, R&D in “AIED” is not new, dating back ~40 years depending on how you count.*)

The tech is advancing at a dizzying pace which can leave us disoriented, and few anticipate that 2024 will be any different. But a consistent challenge faced by every school, college and university, is to build and sustain trust that AI will be used responsibly. Easier said than done:

  • Local ethics. There are endless lists of AI ethics principles that would seem on first inspection to make sense everywhere (“fairness”, “accountability”, “transparency”, etc…) — but translation work is needed. There will be local sensitivities around how these are implemented. What do qualities like “trust” and “responsible” mean to teachers, students, parents, leaders, educational authorities? There will be commonalities for sure that translate across contexts, but building trust means taking your people on the journey, so that they can internalise what these ideas mean, bring abstract principles to life in their own language and metaphors, and tell user stories they can inhabit.
  • High quality deliberation. Moreover, the issues are complex. How do we convene informed, respectful dialogue between diverse stakeholders? Calling a ‘town hall’ for all interested risks being superficial (there’s no time to grapple with the complexities; contributions are misinformed), tokenistic (those in power have already made the decisions), or attracting only the most confident or strident voices. A brainstorming workshop provides more space to go deep, but often doesn’t involve any learning, participants may not represent the true diversity of the community, and while hugely generative of ideas, may fail to converge on tangible outcomes that actually make a difference.

Agreeing on what WE consider to be acceptable practice in OUR context can provide a sense of orientation and safety amid the turbulence — if they are then implemented of course.

In late 2021, here at UTS we set out to grapple with this, and designed the EdTech Ethics forum for the university community with these concerns in mind. We put out an initial report documenting the process and preliminary feedback in the immediate aftermath, but then did the key work of interviewing participants, analysing their feedback, followed by contributing to the university’s governance processes as it developed and published its AI Operations Policy and Procedures.

So I’m delighted to share a forthcoming journal paper documenting how we ran this, what the participants thought, and the tangible outcomes. The paper acknowledges the many people who made this possible, but special thanks to my co-authors Teresa Swist and Kal Gulson at Sydney University Education Futures Studio, who joined the project as external participants to UTS, and conducted the interviews. This work on Deliberative Democracy intersects with our collaboration around Technical Democracy. Chad Foulkes from Liminal by Design was an awesome workshop session designer and facilitator, under the tricky lockdown conditions. And to Chris Riedy and Nivek Thompson (UTS Institute for Sustainable Futures) whose guidance and teaching on Leading Deliberative Democracy and Doing Deliberative Democracy started me down this road (highly recommended online micro credentials!).

Swist, T., Buckingham Shum, S. & Gulson, K. N. (2024). Co-producing AIED Ethics Under Lockdown: An Empirical Study of Deliberative Democracy in Action. International Journal of Artificial Intelligence in Education Published online: 27 Feb. 2024. https://doi.org/10.1007/s40593-023-00380-z

Abstract: It is widely documented that higher education institutional responses to the COVID-19 pandemic accelerated not only the adoption of educational technologies, but also associated socio-technical controversies. Critically, while these cloud-based platforms are capturing huge datasets, and generating new kinds of learning analytics, there are few strongly theorised, empirically validated processes for institutions to consult their communities about the ethics of this data-intensive, increasingly algorithmically-powered infrastructure. Conceptual and empirical contributions to this challenge are made in this paper, as we focus on the under-theorised and under-investigated phase required for ethics implementation, namely, joint agreement on ethical principles. We foreground the potential of ethical co-production through Deliberative Democracy (DD), which emerged in response to the crisis in confidence in how typical democratic systems engage citizens in decision making. This is tested empirically in the context of a university-wide DD consultation, conducted under pandemic lockdown conditions, co-producing a set of ethical principles to govern Analytics/AI-enabled Educational Technology (AAI-EdTech). Evaluation of this process takes the form of interviews conducted with students, educators, and leaders. Findings highlight that this methodology facilitated a unique and structured co-production process, enabling a range of higher education stakeholders to integrate their situated knowledge through dialogue. The DD process and product cultivated commitment and trust among the participants, informing a new university AI governance policy. The concluding discussion reflects on DD as an exemplar of ethical co-production, identifying new research avenues to advance this work. To our knowledge, this is the first application of DD for AI ethics, as is its use as an organisational sensemaking process in education.

Your thoughts welcomed in LinkedIn…

* Histories of AIED:

Doroudi, S. (2023). The Intertwined Histories of Artificial Intelligence and Education. International Journal of Artificial Intelligence in Education, 33(4), 885-928. https://doi.org/10.1007/s40593-022-00313-2

Pham, S. T. H., & Sampson, P. M. (2022). The development of artificial intelligence in education: A review in context. Journal of Computer Assisted Learning, 38(5), 1408–1421. https://doi.org/10.1111/jcal.12687

Woolf, B. P. (2015). AI and education: Celebrating 30 years of marriage. In C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), Artificial intelligence in education: 17th international conference, AIED 2015, Madrid, Spain, June 22–26, 2015. Proceedings (pp. 38–47). Springer International Publishing. https://doi.org/10.1007/978-3-319-19773-9

Deliberative Democracy for EdTech Ethics

What principles should govern UTS’ use of analytics and artificial intelligence to improve teaching and learning for all, while minimising the possibility of harmful outcomes?

This was the challenge we set a team of 20 people – students, casual tutors and full-time academics. And 5 intensive workshops later, they had delivered their response! A draft set of ethical principles to govern the use of these fast-changing technologies in UTS. How did we manage this? Below is the executive summary from the report on the EdTech Ethics website.

Executive Summary

This report has been written to document a novel community consultation process, using the principles and methods of Deliberative Democracy to consult with the UTS community on the following brief:

What principles should govern UTS use of analytics and artificial intelligence to improve teaching and learning for all, while minimising the possibility of harmful outcomes?

We’re sharing this to assist colleagues in UTS and beyond who are seeking more participatory models for community deliberation, with (in this case) specific application to the responsible use of educational technology that is powered by analytics and artificial intelligence. This is not a research paper, seeking to argue conceptual or empirical contributions to academic fields, although research is underway analysing and evaluating this process. We do hope, however, that this represents an interesting and novel ‘data point’ that others will find useful.

Deliberative Democracy (DD) is a movement in response to the crisis in confidence in how typical democratic systems engage citizens in decision making. DD works by creating a Deliberative Mini-Public (DMP). DMPs can be convened at different scales (organisation; community; region; nation) and can take many forms.

A DMP of 20 was selected through stratified sampling from UTS students, casual tutors and academics, who engaged in a series of five online workshops over seven weeks, due to Covid-19 conditions. With little to no prior knowledge among most members, they learned about the topic, worked well together, and converged on a set of principles that they felt reflected their shared values. The university experts who were involved in the workshops recognised the quality of the progress made in such a short period. UTS now has a plausibly representative expression of the community’s values, interests and concerns, in response to the brief. The principles can be viewed in Appendix 1: Draft Ethics Principles.

The raison d’etre for the initiative is to build trust within the university that these technologies are being deployed responsibly. The DMP process delivered on its promise to build engagement and trust across diverse stakeholders. The recording of the final briefing (18 mins, below) conveys the passion and commitment that the DMP invested in the process and outcome, reinforced by the preliminary themes emerging from interviews with students, educators and senior leaders.

Deliberative Democracy, even when conducted wholly online, would appear to offer educational institutions an approach to address the urgent need for meaningful student/staff consultation on the ethical implications of introducing Learning Analytics and Artificial Intelligence into teaching and learning. The implementation process is now beginning, which we will be studying with equal interest.