Deliberative Democracy as a Strategy for Co-designing University Ethics Around Analytics and AI in Education

Year: 2021

Author: Buckingham Shum, Simon

Type of paper: Symposium

Universities can see an increasing range of student and staff activity as it becomes digitally visible in their platform ecosystems. The fields of Learning Analytics and AI in Education have demonstrated the significant benefits that ethically responsible, pedagogically informed analysis of student activity data can bring, but such services are only possible because they are undeniably a form of “surveillance”, raising legitimate questions about how the use of such tools should be governed. Our prior work has drawn on the rich concepts and methods developed in human-centred system design, and participatory/co-design, to design, deploy and validate practical tools that give a voice to non-technical stakeholders (e.g. educators; students) in shaping such systems. We are now expanding the depth and breadth of engagement that we seek, looking to the Deliberative Democracy movement for inspiration. This is a response to the crisis in confidence in how typical democratic systems engage citizens in decision making. A hallmark is the convening of a Deliberative Mini-Public (DMP) which may work at different scales (organisation; community; region; nation) and can take diverse forms (e.g. Citizens’ Juries; Citizens’ Assemblies; Consensus Conferences; Planning Cells; Deliberative Polls). DMP’s combination of stratified random sampling to ensure authentic representation, neutrally facilitated workshops, balanced expert briefings, and real support from organisational leaders, has been shown to cultivate high quality dialogue in sometimes highly conflicted settings, leading to a strong sense of ownership of the DMP's final outputs (e.g. policy recommendations). This symposium contribution will describe how the DMP model is informing university-wide consultation on the ethical principles that should govern the use of analytics and AI around teaching and learning data.