Abstract
Human social learning is increasingly occurring on online social platforms, such as Twitter, Facebook, and TikTok. On these platforms, algorithms exploit existing social-learning biases (i.e., towards prestigious, ingroup, moral, and emotional information, or ‘PRIME’ information) to sustain users’ attention and maximize engagement. Here, we synthesize emerging insights into ‘algorithm-mediated social learning’ and propose a framework that examines its consequences in terms of functional misalignment. We suggest that, when social-learning biases are exploited by algorithms, PRIME information becomes amplified via human–algorithm interactions in the digital social environment in ways that cause social misperceptions and conflict, and spread misinformation. We discuss solutions for reducing functional misalignment, including algorithms promoting bounded diversification and increasing transparency of algorithmic amplification.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 947-960 |
| Number of pages | 14 |
| Journal | Trends in Cognitive Sciences |
| Volume | 27 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 2023 |
Funding
We thank the Marketing Department at Northwestern University, Kellogg School of Management for feedback on ideas included in this manuscript. We also thank the University of Maryland Social, Decision and Organizational Sciences seminar series, members of the Center for Humans and Machines at the Max Planck Institute for Human Development, and members of Northwestern Institute on Complex Systems (NICO) for feedback. We also thank Arvind Narayanan, Katherine Glenn Bass, and the participants at the ‘Optimizing for What?’ conference hosted by the Knight First Amendment Institute at Columbia University for feedback on earlier drafts. We thank the Stanford artificial intelligence group hosted by Michael Bernstein for feedback on earlier drafts. W.J.B. and J.C.J. thank the ChatGPT algorithm for complimenting our learning abilities and helping us choose the acronym ‘PRIME’. W.J.B. thanks Andy Stott and Gunnar Haslam for their music, which was used during the writing of this manuscript. During the preparation of this work, the authors used ChatGPT in order to help come up with the acronym ‘PRIME’. After using this tool, the authors reviewed and edited the acronym as needed and take full responsibility for the content of the publication. The authors have no interests to declare.
Keywords
- algorithms
- norms
- social learning
- social media
- social networks
ASJC Scopus subject areas
- Neuropsychology and Physiological Psychology
- Experimental and Cognitive Psychology
- Cognitive Neuroscience