Work

The politics and reciprocal (re)configuration of accountability and fairness in data-driven education

Year: 2021

Type: article

Abstract: As awareness of bias in educational machine learning applications increases, accountability for technologies and their impact on educational equality is becoming an increasingly important constituent of ethical conduct and accountability in education. This article critically examines the relationship between so-called algorithmic fairness and algorithmic accountability in education. I argue that operative political meanings of accountability and fairness are constructed, operationalized, and reciprocally configured in the performance of algorithmic accountability in education. Tools for measuring forms of unwanted bias in machine learning systems, and technical fixes for mitigating them, are value-laden and may conceal the politics behind quantifying educational inequality. Crucially, some approaches may also disincentivize systemic reforms for substantive equality in education in the name of accountability.

Source: Learning Media and Technology

DOI: 10.1080/17439884.2021.1986065

Author: Otto Sahlgren

Institutions: Tampere University, University of Tampere

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Quality education, Reduced inequalities, Peace, justice, and strong institutions

Open Access status: hybrid

Estimated APC paid: $3,337