Addressing contingency in algorithmic misinformation detection: Toward a responsible innovation agenda

Hernández, Andrés Domínguez; Owen, Richard; Nielsen, Dan Saattrup; McConville, Ryan

doi:10.48550/arxiv.2210.09014

Cited by 1 publication

(1 citation statement)

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“…Content algorithms also amplify the most popular content and information, which can bolster the wisdom of crowds in specific contexts that do not typically involve moralization such as stock market decision-making (Chen et al, 2014). Finally, although content algorithms tend to amplify PRIME information, if they are designed in the right way they can also act as key information quality filters to downrank divisive content and misinformation that tend to hinder our social learning (Hernández et al, 2022). By identifying current areas where algorithm-mediated social learning improves learning outcomes, both scientists and practitioners can better understand how to improve content algorithms' current design.…”

Section: Box 1: Benefits Of Algorithm-mediated Social Learningmentioning

confidence: 99%

Algorithm-Mediated Social Learning in Online Social Networks

Brady¹,

Jackson²,

Lindström³

et al. 2023

Preprint

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Humans rely heavily on social learning to navigate the social and physical world. For the first time in history, we are interacting in online social networks where content algorithms filter social information, yet little is known about how these algorithms influence our social learning. In this review, we synthesize emerging insights into this ‘algorithm-mediated social learning’ and propose a framework that examines its consequences in terms of functional misalignment. We argue that the functions of human social learning and the goals of content algorithms are misaligned in practice. Algorithms exploit basic human social learning biases (i.e., a bias toward PRestigious, Ingroup, Moral and Emotional information, or PRIME information) as a side effect of their goals to sustain attention and maximize engagement on platforms. Social learning biases function to promote adaptive behaviors that foster cooperation and collective problem-solving. However, when social learning biases are exploited by algorithms, PRIME information becomes amplified in the digital social environment in ways that can stimulate conflict and spread misinformation. We show how this problem is ultimately driven by human-algorithm interactions where observational and reinforcement learning exacerbate algorithmic amplification, and how it may even escalate to impact cultural evolution. Finally, we discuss practical solutions for reducing functional misalignment in human-algorithm interactions via strategies that help algorithms promote more diverse and contextually-sensitive information environments.

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Section: Box 1: Benefits Of Algorithm-mediated Social Learningmentioning

confidence: 99%