Michael S. Bernstein | Stanford HAI

Michael S. Bernstein | Stanford HAI

All Related

Response to NSF’s Request for Information on Research Ethics

Quinn Waeiss, Raio Huang, Betsy Arlene Rajala, Michael S. Bernstein, Margaret Levi, David Magnus, Debra Satz

Nov 22, 2024

response to request

Stanford scholars respond to a federal RFI related to research ethics, sharing lessons from their experience operating an ethical reflection process for research grants.

Response to NSF’s Request for Information on Research Ethics

Quinn Waeiss, Raio Huang, Betsy Arlene Rajala, Michael S. Bernstein, Margaret Levi, David Magnus, Debra Satz

Nov 22, 2024

Stanford scholars respond to a federal RFI related to research ethics, sharing lessons from their experience operating an ethical reflection process for research grants.

Ethics, Equity, Inclusion

Sciences (Social, Health, Biological, Physical)

response to request

Internal Fractures: The Competing Logics of Social Media Platforms

Angèle Christin, Michael S. Bernstein, Jeffrey Hancock, Chenyan Jia, Jeanne Tsai, Chunchen Xu

Aug 21, 2024

Research

Social media platforms are too often understood as monoliths with clear priorities. Instead, we analyze them as complex organizations torn between starkly different justifications of their missions. Focusing on the case of Meta, we inductively analyze the company’s public materials and identify three evaluative logics that shape the platform’s decisions: an engagement logic, a public debate logic, and a wellbeing logic. There are clear trade-offs between these logics, which often result in internal conflicts between teams and departments in charge of these different priorities. We examine recent examples showing how Meta rotates between logics in its decision-making, though the goal of engagement dominates in internal negotiations. We outline how this framework can be applied to other social media platforms such as TikTok, Reddit, and X. We discuss the ramifications of our findings for the study of online harms, exclusion, and extraction.

Internal Fractures: The Competing Logics of Social Media Platforms

Angèle Christin, Michael S. Bernstein, Jeffrey Hancock, Chenyan Jia, Jeanne Tsai, Chunchen Xu

Aug 21, 2024

Social media platforms are too often understood as monoliths with clear priorities. Instead, we analyze them as complex organizations torn between starkly different justifications of their missions. Focusing on the case of Meta, we inductively analyze the company’s public materials and identify three evaluative logics that shape the platform’s decisions: an engagement logic, a public debate logic, and a wellbeing logic. There are clear trade-offs between these logics, which often result in internal conflicts between teams and departments in charge of these different priorities. We examine recent examples showing how Meta rotates between logics in its decision-making, though the goal of engagement dominates in internal negotiations. We outline how this framework can be applied to other social media platforms such as TikTok, Reddit, and X. We discuss the ramifications of our findings for the study of online harms, exclusion, and extraction.

Sciences (Social, Health, Biological, Physical)

Communications, Media

Research

Embedding Democratic Values into Social Media AIs via Societal Objective Functions

Chenyan Jia, Michelle Lam, Michael S. Bernstein, Minh Chau Mai

Apr 26, 2024

Research

Mounting evidence indicates that the artificial intelligence (AI) systems that rank our social media feeds bear nontrivial responsibility for amplifying partisan animosity: negative thoughts, feelings, and behaviors toward political out-groups. Can we design these AIs to consider democratic values such as mitigating partisan animosity as part of their objective functions? We introduce a method for translating established, vetted social scientific constructs into AI objective functions, which we term societal objective functions, and demonstrate the method with application to the political science construct of anti-democratic attitudes. Traditionally, we have lacked observable outcomes to use to train such models-however, the social sciences have developed survey instruments and qualitative codebooks for these constructs, and their precision facilitates translation into detailed prompts for large language models. We apply this method to create a democratic attitude model that estimates the extent to which a social media post promotes anti-democratic attitudes, and test this democratic attitude model across three studies. In Study 1, we first test the attitudinal and behavioral effectiveness of the intervention among US partisans (N=1,380) by manually annotating (alpha=.895) social media posts with anti-democratic attitude scores and testing several feed ranking conditions based on these scores. Removal (d=.20) and downranking feeds (d=.25) reduced participants' partisan animosity without compromising their experience and engagement. In Study 2, we scale up the manual labels by creating the democratic attitude model, finding strong agreement with manual labels (rho=.75). Finally, in Study 3, we replicate Study 1 using the democratic attitude model instead of manual labels to test its attitudinal and behavioral impact (N=558), and again find that the feed downranking using the societal objective function reduced partisan animosity (d=.25). This method presents a novel strategy to draw on social science theory and methods to mitigate societal harms in social media AIs.

Embedding Democratic Values into Social Media AIs via Societal Objective Functions

Chenyan Jia, Michelle Lam, Michael S. Bernstein, Minh Chau Mai

Apr 26, 2024

Mounting evidence indicates that the artificial intelligence (AI) systems that rank our social media feeds bear nontrivial responsibility for amplifying partisan animosity: negative thoughts, feelings, and behaviors toward political out-groups. Can we design these AIs to consider democratic values such as mitigating partisan animosity as part of their objective functions? We introduce a method for translating established, vetted social scientific constructs into AI objective functions, which we term societal objective functions, and demonstrate the method with application to the political science construct of anti-democratic attitudes. Traditionally, we have lacked observable outcomes to use to train such models-however, the social sciences have developed survey instruments and qualitative codebooks for these constructs, and their precision facilitates translation into detailed prompts for large language models. We apply this method to create a democratic attitude model that estimates the extent to which a social media post promotes anti-democratic attitudes, and test this democratic attitude model across three studies. In Study 1, we first test the attitudinal and behavioral effectiveness of the intervention among US partisans (N=1,380) by manually annotating (alpha=.895) social media posts with anti-democratic attitude scores and testing several feed ranking conditions based on these scores. Removal (d=.20) and downranking feeds (d=.25) reduced participants' partisan animosity without compromising their experience and engagement. In Study 2, we scale up the manual labels by creating the democratic attitude model, finding strong agreement with manual labels (rho=.75). Finally, in Study 3, we replicate Study 1 using the democratic attitude model instead of manual labels to test its attitudinal and behavioral impact (N=558), and again find that the feed downranking using the societal objective function reduced partisan animosity (d=.25). This method presents a novel strategy to draw on social science theory and methods to mitigate societal harms in social media AIs.

Research

Algorithms and the Perceived Legitimacy of Content Moderation

Christina A. Pan, Sahil Yakhmi, Tara Iyer, Evan Strasnick, Amy X. Zhang, Michael S. Bernstein

Quick ReadDec 15, 2022

policy brief

This brief explores people’s views of Facebook’s content moderation processes, providing a pathway for better online speech platforms and improving content moderation processes.

Algorithms and the Perceived Legitimacy of Content Moderation

Christina A. Pan, Sahil Yakhmi, Tara Iyer, Evan Strasnick, Amy X. Zhang, Michael S. Bernstein

Quick ReadDec 15, 2022

This brief explores people’s views of Facebook’s content moderation processes, providing a pathway for better online speech platforms and improving content moderation processes.

Privacy, Safety, Security

policy brief