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SOCIAL SCIENCES

Social sciences examine human behavior, social structures, and interactions in various settings. Fields such as sociology, psychology, anthropology, and economics study social relationships, cultural norms, and institutions. By using different research methods, social scientists seek to understand community dynamics, the effects of policies, and factors driving social change. This field is important for tackling current issues, guiding public discussions, and developing strategies for social progress and innovation.

Posts tagged algorithmic bias
Online Hate Speech and Discrimination in the Age of AI

By Petra Regeni and Claudia Wallner

RUSI and the Institute for Strategic Dialogue (ISD) Germany convened a closed-door roundtable event in Berlin on 31 March 2025 to discuss online hate speech and discrimination in Europe in the age of AI. The roundtable included presentations across three sessions (corresponding with the sections of this paper) and participants from academia, civil society, advocacy groups, legal non-profit organisations and the private sector. The event provided a space to discuss online hate speech and discriminatory rhetoric – ranging from antisemitism, misogyny and anti-LGBTQI+ narratives to racism and xenophobia – as well as the implications of AI in their spread and amplification. Discussions centred around the complexities introduced by AI-generated and targeted hate speech, and explored potential responses, from regulatory measures and content moderation to educational initiatives promoting critical thinking skills. This conference report summarises key themes and points raised during the roundtable, none of which are attributable to individual participants and presenters.

Conference Report

London: Royal United Services Institute for Defence and Security Studies (RUSI), 2025, 13p.

Social ScienceRead-Me.Orgonline hate speech, discrimination, artificial intelligence, algorithmic bias, digital platforms
Predatory Data: Eugenics in Big Tech and Our Fight for an Independent Future

By Anita Say Chan

Predatory Data illuminates the connections between the nineteenth century’s anti‑immigration and eugenics movements and today’s sprawling systems of techno-surveillance and algorithmic discrimination. Historical and globally multisited, the book examines how dispossession, misrecognition, and segregation are being magnified by dominant knowledge institutions in the Age of Big Data. Technological advancement has a history, including efforts to chart a path for alternative futures. Anita Say Chan explores these important parallel stories of defiant refusal and liberatory activism, such as how feminist, immigrant, and other minoritized actors worked to develop alternative data practices. Their methods and traditions, over a century old, continue to reverberate through global justice‑based data initiatives today. Predatory Data charts a path for an alternative historical consciousness grounded in the pursuit of global justice. “Anita Say Chan highlights the power of community‑based alternatives to extractive data that are rooted in feminist, people of color, and Indigenous perspectives.

Oakland, CA: University of California Press, 2025. 263p.

Equity, Diversity, InclusionRead-Me.Orgeugenics in big tech, data ethics, algorithmic bias, digital surveillance, technological discrimination