When Users Control the Algorithms

Abstract

Recent interest in ethical AI has brought a slew of values, including fairness, into conversations about technology design. Research in the area of algorithmic fairness tends to be rooted in questions of distribution that can be subject to precise formalism and technical implementation. We seek to expand this conversation to include the experiences of people subject to algorithmic classification and decision-making. By examining tweets about the "Twitter algorithm" we consider the wide range of concerns and desires Twitter users express. We find a concern with fairness (narrowly construed) is present, particularly in the ways users complain that the platform enacts a political bias against conservatives. However, we find another important category of concern, evident in attempts to exert control over the algorithm. Twitter users who seek control do so for a variety of reasons, many well justified. We argue for the need for better and clearer definitions of what constitutes legitimate and illegitimate control over algorithmic processes and to consider support for users who wish to enact their own collective choices. CCS Concepts: • Human-centered computing → HCI theory, concepts and models; Empirical studies in collaborative and social computing.

Development and Validation of an Algorithm Literacy Scale for Internet Users

“…Acknowledging that algorithms largely govern our digital lives and can be considered powerful actors with more or less visible encoded values and assumptions of our world (Gillespie, 2014), raises the question of whether internet users are aware and understand algorithmic systems. Being aware of and knowing about algorithms can at least limit the exploitation and manipulation of users through algorithmic operations and allows them to maintain their autonomy in decision-making (Dogruel et al, 2020;Burrell et al, 2019;Susser et al, 2019). Understanding the role of the user has also implications for policy making and the governance of algorithmic systems (Katzenbach & Ulbricht, 2019;Latzer & Festic, 2019;Yeung, 2018).…”

Mapping the Design Space of Teachable Social Media Feed Experiences

“…14 identified as women, 8 as men, and 2 identified as non-binary. All had at least 3 years of experience on social media, with all but one having at least 5 years and just over half (14) having at least 10 years.…”

Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

“…The benefits of more user control in content moderation technologies have also been noted [207]. Personalisation can centre the end-user in the designation of model behaviours, allowing them to exert more control over their interactions [33], and become a "perceived locus of casuality" [176, p.5]. This benefit only arises given sufficient protections on how personalised data is collected because autonomy relies on an 'unpressured' engagement in an activity.…”