Communicating Algorithmic Process in Online Behavioral Advertising

By Motahhare Eslami, Sneha R. Krishna Kumaran, Christian Sandvig, K. G. Karahalios. 2018

186 citations recorded by the source corpus.

Subfields: Clinical Psychology, Safety Research, Sociology and Political Science

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In Clinical Psychology

Nazanin Andalibi

In Safety Research

In Sociology and Political Science

Citations in the corpus

Manipulating and Measuring Model Interpretability (2018, background): “tered approach, building on previous psychology and social science research on human trust in models [4, 17, 21] and similar efforts in the machine learning and human-computer interaction communities [2, 6, 8, 13, 15, 23, 24]. We present a sequence of large-scale, randomized human-subject experiment”
Algorithmic Folk Theories and Identity: How TikTok Users Co-Produce Knowledge of Identity and Engage in Algorithmic Resistance (2021, methodology): “In the context of identity being perceived by algorithms, we turn to Cheney-Lippold’s concept of algorithmic identity—"an identity formation that works through mathematical algorithms to infer categories of identity on otherwise anonymous beings" [22, 165]—to refer to how algorithms create a user’s ”
Progressive Disclosure: Designing for Effective Transparency (2018, background): “Users are prone to blindly believing outputs from algorithmic systems, a phenomena referred to as algorithmic omniscience [23,36,74] and automation bias [17,56].”
Towards Transparency by Design for Artificial Intelligence (2020, background): “Too much transparency can be undesirable or even harmful (Ananny and Crawford 2018) and empirical research has shown that too much transparency can have unintended side-effects such as algorithmic disillusionment (Eslami et al. 2018).”
Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns (2019, background): “Eslami et al. (2018) found that transparency needs to have the right level of speciﬁcity to enhance trust and satisfaction.”
"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making (2019, background): “Domains of study include recommender systems [4], medicine [12, 75], social media [22], creativity [14], and advertisements [23].”