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Oscar Blessed Deho Chen Zhan Jiuyong Li Jixue Liu Lin Liu Thuc Duy Le 《British journal of educational technology : journal of the Council for Educational Technology》2022,53(4):822-843
Practitioner notes
What is already known about this topic- LA is increasingly being used to leverage actionable insights about students and drive student success.
- LA models have been found to make discriminatory decisions against certain student demographic subgroups—therefore, raising ethical concerns.
- Fairness in education is nascent. Only a few works have examined fairness in LA and consequently followed up with ensuring fair LA models.
- A juxtaposition of unfairness mitigation algorithms across the entire LA pipeline showing how they compare and how each of them contributes to fair LA.
- Ensuring ethical LA does not always lead to a dip in performance. Sometimes, it actually improves performance as well.
- Fairness in LA has only focused on some form of outcome equality, however equality of outcome may be possible only when the playing field is levelled.
- Based on desired notion of fairness and which segment of the LA pipeline is accessible, a fairness-minded decision maker may be able to decide which algorithm to use in order to achieve their ethical goals.
- LA practitioners can carefully aim for more ethical LA models without trading significant utility by selecting algorithms that find the right balance between the two objectives.
- Fairness enhancing technologies should be cautiously used as guides—not final decision makers. Human domain experts must be kept in the loop to handle the dynamics of transcending fair LA beyond equality to equitable LA.