When fairness is an abstraction: Equity and AI in Swedish compulsory education

TL;DR

This paper argues that fairness in AI should be understood within social and political contexts, especially in education, and examines how AI use in Swedish compulsory education may reinforce existing inequalities.

Contribution

It highlights the importance of contextual fairness in AI and analyzes Swedish education policies to reveal how AI may exacerbate social inequalities.

Findings

Identifies three groups valuing efficiency: economical, pedagogical, accessibility.

Challenges the idea of fairness as formal equality of opportunities.

Emphasizes fairness as embedded in social, political, and economic contexts.

Abstract

Artificial intelligence experts often question whether AI is fair. They view fairness as a property of AI systems rather than of sociopolitical and economic systems. This paper emphasizes the need to be fair in the social, political, and economic contexts within which an educational system operates and uses AI. Taking Swedish decentralized compulsory education as the context, this paper examines whether and how the use of AI envisaged by national authorities and edtech companies exacerbates unfairness. A qualitative content analysis of selected Swedish policy documents and edtech reports was conducted using the concept of relevant social groups to understand how different groups view the risks and benefits of AI for fairness. Three groups that view efficiency as a key value of AI are identified, and interpreted as economical, pedagogical and accessibility-related. By separating fairness from social justice, this paper challenges the notion of fairness as the formal equality of opportunities.