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Billions of dollars are being invested into developing medical artificial intelligence (AI) systems and yet public opinion of AI in the medical field seems to be mixed. Although high expectations for the future of medical AI do exist in the American public, anxiety and uncertainty about what it can do and how it works is widespread. Continuing evaluation of public opinion on AI in healthcare is necessary to ensure alignment between patient attitudes and the technologies adopted. We conducted a representative-sample survey (total N = 203) to measure the trust of the American public towards medical AI. Primarily, we contrasted preferences for AI and human professionals to be medical decision-makers. Additionally, we measured expectations for the impact and use of medical AI in the future. We present four noteworthy results: (1) The general public strongly prefers human medical professionals make medical decisions, while at the same time believing they are more likely to make culturally biased decisions than AI. (2) The general public is more comfortable with a human reading their medical records than an AI, both now and “100 years from now.” (3) The general public is nearly evenly split between those who would trust their own doctor to use AI and those who would not. (4) Respondents expect AI will improve medical treatment but more so in the distant future than immediately. 

Over the past few years, pronoun lists have become more prevalent in online spaces. Currently, various LGBT+ activists, universities, and corporations encourage people to share their preferred pronouns. Little research exists examining the characteristics of individuals who do publicly share their preferred pronouns. Using Twitter bios from the US between early 2015 and June 30, 2022, we explored users’ expression of preferred pronouns. First, we noted the prevalence of users with pronoun lists within their bio has increased substantially. Second, we observed that certain linguistic tokens systematically co-occurred with pronoun lists. Specifically, tokens associated with left-wing politics, gender or sexual identity, and social media argot co-occurred disproportionately often alongside pronoun lists, while tokens associated with right-wing politics, religion, sports, and finance co-occurred infrequently. Additionally, we discovered clustering among Twitter users with pronouns in their bios. Specifically, we found an above-average proportion of the followers and friends of Twitter users with pronouns in their bio also had pronouns in their bios. Twitter users who did not share their preferred pronouns, on the other hand, were disproportionately unlikely to be connected with Twitter users who did. 

Personally expressed identity is who or what an individual themselves says they are, and it should be studied at scale. At scale means with data on millions of individuals, which is newly available and comes timestamped and geocoded. This work introduces a dataset for the study of identity at scale and describes the method for collecting and aggregating such data. Further, tools and theory for working with the data are presented. A demonstration analysis provides evidence that personal, individual development and changing cultural norms can be observed with these data and methods.