Algorithmic injustice: a relational ethics approach

By Abeba Birhane. Patterns, 2021

540 citations recorded by the source corpus.

Summary It has become trivial to point out that algorithmic systems increasingly pervade the social sphere. Improved efficiency—the hallmark of these systems—drives their mass integration into day-to-day life. However, as a robust body of research in the area of algorithmic injustice shows, algorithmic systems, especially when used to sort and predict social outcomes, are not only inadequate but also perpetuate harm. In particular, a persistent and recurrent trend within the literature indicates that society's most vulnerable are disproportionally impacted. When algorithmic injustice and harm are brought to the fore, most of the solutions on offer (1) revolve around technical solutions and (2) do not center disproportionally impacted communities. This paper proposes a fundamental shift—from rational to relational—in thinking about personhood, data, justice, and everything in between, and places ethics as something that goes above and beyond technical solutions. Outlining the idea of ethics built on the foundations of relationality, this paper calls for a rethinking of justice and ethics as a set of broad, contingent, and fluid concepts and down-to-earth practices that are best viewed as a habit and not a mere methodology for data science. As such, this paper mainly offers critical examinations and reflection and not “solutions.”

Subfields: Cognitive Neuroscience, Safety Research

View Paper: https://www.semanticscholar.org/paper/a6de042d8a2baeeeb3d7e37e03c66c1fe07e7b36

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Citations in the corpus

Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection (2021, background): “In general, we urge people to embrace that each design decision has socio-political implications (Green, 2020; Cambo, 2021), and encourage them to develop technologies to shift power to the targets of oppression (Blodgett et al., 2020; Kalluri, 2020; Birhane, 2021).”
Data Governance in the Age of Large-Scale Data-Driven Language Technology (2022, methodology): “acknowledging both the need for respecting local expertise and conceptualization of human rights [5] and for more relational and community-oriented notions of justice [17, 98], we complement our initial set of values as outlined in Table 1 to explicitly include the prioritization of local knowledge ”
Multimodal datasets: misogyny, pornography, and malignant stereotypes (2021, background): “It fails to account for the rights, welfare, and interests of vulnerable individuals and communities, many of whom are likely to suffer worst from the downstream impacts of this dataset and the models trained on it [69].”
Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation (2022, background): “We also note that despite the language we employ, we do not suggest that fairness can be treated as a purely algorithmic problem that neglects the sociotechnical frame [12, 50, 51, 109].”
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets (2020, background): “the deployment of a machine learning system (Green & Hu, 2018; Birhane, 2021).”
Power to the People? Opportunities and Challenges for Participatory AI (2022, background): “It is a process that prioritizes the net benefit of participants, especially those disproportionately impacted by oppressive social structures, who often carry the burdens of negative impacts of technology [11, 20, 57, 60] and reflecting a fair or proportionate”
The Forgotten Margins of AI Ethics (2022, background): “objectivity, the idea that there exists a “purely objective” dataset or a “neutral” representation of people, groups, and the social world independent of the observer/modeller [7, 8].”