Each of the solutions considered were trying to address a (very real) moral problem of fairness. However, each one introduced new moral problems. The final place we end up seems better from the point of view of most individual students (their grades are likely to be higher than if they had sat the exam), but this benefit is not necessarily evenly distributed. It also introduces a host of problems on a societal, systematic level in terms of university placements and for the function of exams being in part to distinguish students well based on ability.
And in fact, we saw some of these problems come to fruition, with huge leaps in numbers of students admitted at some unis, and drops at others, with concerns about the financial and resource impacts of that. This has resulted in concerns over the amount of space for the 2021 cohort, that universities are increasing their grade entry requirements to compensate, and that parents - disproportionately of children in private schools - are pressuring teachers to increase grade predictions.
In 2021 we’re back to first fairness problem of grade inflation!
Learning lessons
Now, on the one hand, we could leave it here as particular example of algorithms in education. We could see that the use of an algorithm in the Ofqual case is a unique instance. It is different from other algorithmic instances as it was not to improve a currently existing system or to make any small gain in efficiency but employed to make up for something that couldn’t be done. A one off instance (leaving aside that the UK is facing a similar problem in 2021).
The algorithm was a complex balancing act - an attempt to do something (accurately predict grades), that could not be done in the usual way (exams), while also trying to address issues of fairness.
On the other hand, while we’ve tried to explore fairness in this specific context, a key takeaway is that we need to consider the wider system. Neither humans or algorithms are going to be able to make an completely fair set of choices. Indeed, the use of algorithmic decision making still involves human decision making. A focus of explainable AI and fairness in AI is often on how we build systems to be fair. But as we’ve shown, there are often irreconcilable tensions in fairness.
What is less often discussed in such work is where we should choose not to deploy algorithms, and the wider context and implications of these technologies (such as PESTLE stands for Political, Economic, Social, Technological, Legal and Environmental factors). The fairness tradeoffs we outlined above span multiple algorithmic decision making instances. Furthermore, if there is an identified public need and value for algorithms, then participatory research is vital. This can include co-design and co-production with stakeholders who will be impacted by algorithmic decison making.
Thanks for reading (and playing). We hope it has tested your thinking about the use of algorithms in education, and you've enjoyed thinking through the ways different uses of data can lead to different outcomes.
We’d love to hear from you if you learned from our algorithm game.