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The current study examined three hypotheses about experience in close friendships and psychosocial adjustment. At Time 1, 51 same-sex close friend dyads ( n = 102 friends, 51% female, mean age = 20 years) completed self-report measures and participated in a brief observational assessment. The hypothesis that friendship quality would be associated with clinical symptomatology and self-esteem was supported and indicated that high levels of negative friendship features were positively associated with clinical symptoms, whereas positive features were most strongly associated with self-esteem. The second hypothesis that changes in the friendship would be associated with adjustment one year later (68% participation rate at Time 2) was supported only for interpersonal sensitivity such that perceived negative changes in the relationship predicted increased symptoms. Finally, friends’ perceptions of the features and quality of their relationship were somewhat consistent, yet as hypothesized, discordant perceptions predicted higher symptomatology and lower social support and satisfaction in the relationship. The results highlight the importance of considering both positive and negative aspects of friendship in early adulthood.

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Artificial intelligence (AI) has entered election administration. Across the country, election officials are beginning to use AI systems to purge voter records, verify mail-in ballots, and draw district lines. Already, these technologies are having a profound effect on voting rights and democratic processes. However, they have received relatively little attention from AI experts, advocates, and policymakers. Scholars have sounded the alarm on a variety of “algorithmic harms” resulting from AI’s use in the criminal justice system, employment, healthcare, and other civil rights domains. Many of these same algorithmic harms manifest in elections and voting but have been underexplored and remain unaddressed. This Note offers three contributions. First, it documents the various forms of “algorithmic decisionmaking” that are currently present in U.S. elections. This is the most comprehensive survey of AI’s use in elections and voting to date. Second, it explains how algorithmic harms resulting from these technologies are disenfranchising eligible voters and disrupting democratic processes. Finally, it identifies several unique characteristics of the U.S. election administration system that are likely to complicate reform efforts and must be addressed to safeguard voting rights.