ORCID Profile
0000-0002-4794-8339
Current Organisation
University of Melbourne
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Publisher: ACM
Date: 07-09-2022
Publisher: Wiley
Date: 16-05-2022
DOI: 10.1111/BJET.13236
Publisher: Elsevier BV
Date: 04-2020
Publisher: Wiley
Date: 12-04-2022
DOI: 10.1111/BJET.13217
Abstract: With the widespread use of learning analytics (LA), ethical concerns about fairness have been raised. Research shows that LA models may be biased against students of certain demographic subgroups. Although fairness has gained significant attention in the broader machine learning (ML) community in the last decade, it is only recently that attention has been paid to fairness in LA. Furthermore, the decision on which unfairness mitigation algorithm or metric to use in a particular context remains largely unknown. On this premise, we performed a comparative evaluation of some selected unfairness mitigation algorithms regarded in the fair ML community to have shown promising results. Using a 3‐year program dropout data from an Australian university, we comparatively evaluated how the unfairness mitigation algorithms contribute to ethical LA by testing for some hypotheses across fairness and performance metrics. Interestingly, our results show how data bias does not always necessarily result in predictive bias. Perhaps not surprisingly, our test for fairness‐utility tradeoff shows how ensuring fairness does not always lead to drop in utility. Indeed, our results show that ensuring fairness might lead to enhanced utility under specific circumstances. Our findings may to some extent, guide fairness algorithm and metric selection for a given context. 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. What this paper adds 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. Implications for practice and/or policy 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.
Publisher: ACM
Date: 20-07-2023
Publisher: Elsevier BV
Date: 12-2020
Publisher: Springer International Publishing
Date: 05-12-2021
Publisher: Elsevier BV
Date: 09-2018
DOI: 10.1016/J.JBI.2018.07.013
Abstract: Drug safety issues such as Adverse Drug Events (ADEs) can cause serious consequences for the public. The clinical trials that are undertaken to assess medicine efficacy and safety prior to marketing, generally, may provide sufficient s les for discovering common ADEs. However, more s les are needed to detect infrequent and rare events. Additionally, clinical trials may not include all subgroups of patients. For these reasons, post-marketing surveillance of medicines is necessary for identifying drug safety issues. Most regulatory agencies use the Spontaneous Reporting Systems to identify associations between medicines and suspected ADEs. Data mining with effective analytical frameworks and large-scale medical data is potentially an alternative method to discover and monitor ADEs. In the present paper, we aim to detect potential ADEs from prescription data by discovering ADE associated prescription sequences. In an ADE associated prescription sequence 〈D
Publisher: Informa UK Limited
Date: 16-03-2018
Publisher: Elsevier BV
Date: 11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
No related grants have been discovered for Chen Zhan.