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Intersectionality of social and philosophical frameworks with technology: could ethical AI restore equality of opportunities in academia?
Intersectionality of social and philosophical frameworks with technology: could ethical AI restore equality of opportunities in academia?
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Intersectionality of social and philosophical frameworks with technology: could ethical AI restore equality of opportunities in academia?
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Intersectionality of social and philosophical frameworks with technology: could ethical AI restore equality of opportunities in academia?
Intersectionality of social and philosophical frameworks with technology: could ethical AI restore equality of opportunities in academia?
Journal Article

Intersectionality of social and philosophical frameworks with technology: could ethical AI restore equality of opportunities in academia?

2022
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Overview
Academia is far from a meritocratic distribution of opportunities. This leads to inequalities, lack of diversity, and unfairness. The objective of this conceptual paper is to propose an integrative framework to help the academic community address its pervasive but persistent inequalities of opportunities. The framework emerges from the intersections of Bourdieu, Bronfenbrenner, and Rawls frameworks and propose the use of ethical artificial intelligence (AI) to contextualise merit and recreate true equality of opportunities. More specifically, I argue that academia has structures and doxa that may be inaccessible to individuals from different social origins, and are perpetuated by privileged individuals who achieve positions of power within academia. The privileged individuals inherit and are exposed to opportunities to acquire capital from early life, resulting in the continuation of status quo practices and alienation of minorities that do not share—or do not have the ability to acquire—capital. I argue that this process occurs as a result of the social origins of the individual and, as Bronfenbrennian framework suggests, disadvantaged individuals lack both the (inherited) capital, but also lack the ability and opportunities to acquire capital relative to privileged counterparts. I argue that the only way to mitigate this inequitable system is to retrieve the Rawlsian original position of ignorance (veil of ignorance) in the allocation of academic capital based on merit, which can only be objectively quantified relative to social origins of individuals. As opposed to current subjective assessments (e.g., peer-review) or lottery systems, I propose the use of Big Data and ethical AI to reconstruct the position of ignorance and contextualise merit based on the expected merit given individuals’ social origins. I also discuss the concept of ‘years post-PhD’ as it is used to introduce fairness in allocation of academic capital and propose a different and less relativistic landmark that accounts for the years post-first authorship publication. This is a novel conceptual framework which can stimulate further research into the ecology of social justice.