Work

Insights into Algorithmic Decision-Making Systems via a Decolonial-Intersectional Lens: A Cross-Analysis Case Study

Year: 2024

Type: article

Abstract: As the line between governance, technology, and decision-making becomes ever-blurred, advocates and disparagers of Artificial Intelligence (AI) debate the impacts of its societal integration. Despite efforts to promote Fairness, Accountability, Transparency, and Ethics (FATE), vulnerable populations continue to be systematically marginalized and made “invisible” by the racialised, classed, and colonial institutions buttressing Algorithmic Decision-Making Systems (ADMS). To address these sociotechnical risks and acknowledge our privileged, Western “standpoint epistemology,” we employ a “metaparadigm perspective” to engage the literature and case studies through a critical theory lens. The cross-analysis of three case studies: Systems Theory, the United States’ “Blueprint for an AI Bill of Rights,” and predictive policing demonstrate that our current risk mitigation frameworks are incapable of addressing transhistorical injustices. Therefore, we call for operationalizing intersectional risk theory via the decolonial turn to address ADMS risks. On that note, we propose three algorithmic accountability recommendations. We theorize that through a reformulation of FATE principles to that of the algorithmic (decolonial) self, understanding power imbalances through marginalized risk discourses, and advancing intersectional ML decision-making practices, policymakers may be better positioned to enact equitable and just ADMS.

Source: Digital Society

DOI: 10.1007/s44206-024-00144-9

Author: Joel Tyler Alba

Institution: University of Stavanger

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

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

Open Access status: hybrid

Estimated APC paid: $2,990