Constructing algorithmic impacts on equality: A study on fairness and accountability in data-driven education
Sahlgren, Otto (2021)
2021
Kasvatuksen ja yhteiskunnan tutkimuksen maisteriohjelma - Master´s Programme in Educational Studies
Kasvatustieteiden ja kulttuurin tiedekunta - Faculty of Education and Culture
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Hyväksymispäivämäärä
2021-09-02
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202108126530
https://urn.fi/URN:NBN:fi:tuni-202108126530
Tiivistelmä
As awareness of bias in machine learning applications increases, accountability for technologies and their impact on equality emerges as a novel constituent of accountability in education. To legitimate their actions in the eyes of the public, decision-makers are increasingly searching for ways to evince the equitable and non-discriminatory impact of employed algorithmic technologies. This creates demand for novel instruments for algorithmic accountability. This thesis, which contains an ‘Authors Original Manuscript’ of an article submitted to “Learning, Media and Technology” published by Taylor & Francis Group, discusses assessments of algorithmic impacts on equality as such an instrument.
The literature contains numerous formal definitions and measures for “algorithmic fairness” used to assess algorithmic impacts on equality. They differ in their operationalization of concepts such as ’equality’ and ‘discrimination’, although they share methodological commonalities. The study examines the political, epistemological, and ethical dimensions and implications of algorithmic fairness through the lens of accountability. It is argued that operative political meanings of accountability and fairness are constructed, operationalized, and reciprocally configured in the performance of algorithmic accountability in education. Operative conceptions regarding agents’ responsibilities in preventing discrimination and in promoting equality are reflected in, and built through, methodological choices in instrumentation.
It is argued that the operative meanings constructed through assessments of algorithmic fairness are neither politically nor ethically neutral. Crucially, certain methodological choices can lead to the reproduction and legitimation of existing social inequalities in the name of accountability. This finding corroborates existing findings in education policy and technology ethics research.
The literature contains numerous formal definitions and measures for “algorithmic fairness” used to assess algorithmic impacts on equality. They differ in their operationalization of concepts such as ’equality’ and ‘discrimination’, although they share methodological commonalities. The study examines the political, epistemological, and ethical dimensions and implications of algorithmic fairness through the lens of accountability. It is argued that operative political meanings of accountability and fairness are constructed, operationalized, and reciprocally configured in the performance of algorithmic accountability in education. Operative conceptions regarding agents’ responsibilities in preventing discrimination and in promoting equality are reflected in, and built through, methodological choices in instrumentation.
It is argued that the operative meanings constructed through assessments of algorithmic fairness are neither politically nor ethically neutral. Crucially, certain methodological choices can lead to the reproduction and legitimation of existing social inequalities in the name of accountability. This finding corroborates existing findings in education policy and technology ethics research.