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Can we design artificial intelligence (AI) systems that rank our social mediafeeds to consider democratic values such as mitigating partisan animosity aspart of their objective functions? We introduce a method for translatingestablished, vetted social scientific constructs into AI objective functions,which we term societal objective functions, and demonstrate the method withapplication 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 qualitativecodebooks for these constructs, and their precision facilitates translationinto detailed prompts for large language models. We apply this method to createa democratic attitude model that estimates the extent to which a social mediapost promotes anti-democratic attitudes, and test this democratic attitudemodel across three studies. In Study 1, we first test the attitudinal andbehavioral effectiveness of the intervention among US partisans (N=1,380) bymanually annotating (alpha=.895) social media posts with anti-democraticattitude scores and testing several feed ranking conditions based on thesescores. Removal (d=.20) and downranking feeds (d=.25) reduced participants'partisan animosity without compromising their experience and engagement. InStudy 2, we scale up the manual labels by creating the democratic attitudemodel, finding strong agreement with manual labels (rho=.75). Finally, in Study3, we replicate Study 1 using the democratic attitude model instead of manuallabels to test its attitudinal and behavioral impact (N=558), and again findthat the feed downranking using the societal objective function reducedpartisan animosity (d=.25). This method presents a novel strategy to draw onsocial science theory and methods to mitigate societal harms in social mediaAIs.

Embedding Democratic Values into Social Media AIs via Societal Objective Functions