Towards Algorithmic Experience: Initial Efforts for Social Media Contexts

By Oscar Alvarado, Annika Wærn. 2018

130 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Safety Research, Human-Computer Interaction

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Citations in the corpus

Manipulating and Measuring Model Interpretability (2018, background): “B147 [3] Oscar Alvarado and Annika Waern.”
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? (2018, background): “N Machine learning (ML) systems increasingly influence every facet of our lives, including the quality of healthcare and education we receive [18, 34, 35, 53], which news or social media posts we see [4, 20, 91], who receives a job [50, 89], who is released from jail [8, 25], and who is subjected to”
Algorithmic Folk Theories and Identity: How TikTok Users Co-Produce Knowledge of Identity and Engage in Algorithmic Resistance (2021, methodology): “The user-algorithm interactions described in this paper are all part of users’ algorithmic experience (AX) [8] on TikTok.”
Design Principles for Generative AI Applications (2024, background): “This characteristic has been described by Weisz et al. [182] as generative variability, and it provides what Alvarado and Waern [6] describe as an “algorithmic experience,” raising questions on appropriate types of user control, levels of algorithmic transparency, and user awareness of how the…”
ORES (2019, background): “are classic cases which raise many of the same lessons as contemporary machine learning [8, 67, 98]. Recent work has focused on more participatory [63], value-sensitive [112], and experiential design [3] approaches to machine learning. CSCW and HCI has also long studied human-in-the-loop systems [14”
Experiencing Algorithms: How Young People Understand, Feel About, and Engage With Algorithmic News Selection on Social Media (2021, background): “They judged the way the platform infers recommendations from previous watching behavior as “over the top” (Onno, 21) and a too rigid interpretation of their tastes that ignores how users’ news interests change over time (Alvarado & Waern, 2018; Monzer et al., 2020).”