Algorithms are increasingly being used to make decisions that have a lasting impact on our current and future lives. There is a growing public awareness that algorithms, especially those used in forms of artificial intelligence, need to be understood as raising issues of fairness. But while everyone may have a vernacular understanding of what is fair or unfair, when algorithms are used numerous trade-offs are involved.
In the CiteLearn project, funded by Wikicred, we are developing a tool to support people in learning a key skill of verifiability, to support the writing and flow of credible information.
A body of work - particularly led by Dilek Cetindamar Kozanoglu - has focussed on digital transformation in organisations, and how managers and employees learn to work with digital technologies, including AI technologies.
In a project led by Kalervo Gulson (USyd), centre members are collaborating with the Gradient Institute and ANU’s Claire Benn to co-design an ‘algorithm game’ intending to explore issues around fairness and data, using the case study of the 2020 UK Exams Algorithm Controversy.
This question is central to work being undertaken by colleagues at UTS and internationally. Evidence and data are increasingly emphasised in educational contexts, with the spread of What Works centres such as the Educational Endowment Foundation (UK), Evidence for Learning (Australia), the What Works Clearinghouse (USA), international (PISA), national (SATS, NAPLAN, etc.
Artificial intelligence holds great potential for both students and teachers – but only if used wisely -- Data big and small have come to education, from creating online platforms to increasing standardised assessments.