Algorithmic Fairness in Online Information Mediating Systems

Can Video-based Qualitative Analysis Help Us Understand User-algorithm Interaction?

“…The work described in this paper is undertaken as part of a broader research project, UnBias 1 , which explores the user experience of algorithm-driven online platforms. The study is set within the context of current controversies over the prevalence of algorithms online and seeks to promote fairness in relation to the design, development and use of algorithms [19]. We are particularly interested in studying how people interact with algorithms in order to identify what factors enable trust in algorithms and 1 https://unbias.wp.horizon.ac.uk/.…”

An Action-Oriented AI Policy Toolkit for Technology Audits by Community Advocates and Activists

“…Other approaches to date have aimed to make AI more accessible and interpretable to non-specialist audiences. The UnBias project employs multi-stakeholder engagement and public empowerment, with a particular focus on engaging youths in understanding algorithmic bias [31,32,52]. Google's A-Z of AI [25] presents accessible definitions of many AI terms for a public audience, mirrored by CritPlat's parody A-Z of UAVs [27] for Unmanned Autonomous Vehicles, while Google's Model Cards [39] provide digestible summaries of model bias.…”

Humans versus machines: Who is perceived to decide fairer? Experimental evidence on attitudes toward automated decision-making

“…The first dimension is concerned with the contexts in which ADM systems are used and the impact of a decision for an individual’s life. 11 , 12 Previous research highlighted that empirical results on perceptions in specific ADM contexts may not translate into other contexts, cautioning researchers against over-generalizations. 10 Although each context comes with myriads of idiosyncrasies, it appears likely that the stakes of the decision-making context are one crucial differentiating factor.…”