Tori Helen Cotton

Tori Helen Cotton

Ph.D. student working on AI, feminist epistemology, and computational approaches to scientific language. She/they.

Research

Digital Dirty Laundry: Conversational AI and the Evidential Value of Unguarded Data

Conversational AI chatbots are trained on large-scale human language data and shaped by user interaction. Drawing on standpoint epistemology, I argue that they occupy an epistemic position structurally analogous to that of insider-outsiders: marginalized persons treated as socially irrelevant, affording access to information others overlook. Chatbots’ exposure to unguarded disclosures may generate a novel evidential resource for studying patterns of human behavior, bias, and self-disclosure often filtered out by traditional methods such as surveys or interviews. However, access to this evidence is highly asymmetric. The organizations that develop these systems are positioned to collect and use these data at scale.

Scientific Metaphor and the Space Between Words

Metaphors are central to scientific explanation, yet philosophers of science lack scalable methods for studying their use. This paper introduces an approach to the study of scientific metaphor that is informed by methods in natural language processing and the digital humanities. It argues that these methods can be repurposed as a philosophical tool for analyzing scientific language at scale. Using a corpus of reproductive biology articles spanning several decades and thousands of texts, the analysis draws on familiar claims from feminist philosophy of biology about gendered metaphors in descriptions of sperm and egg cells as a case study, and operationalizes these claims to track shifting patterns of linguistic association over time. The results suggest that metaphors previously identified in the literature are recoverable within this framework and that their associated patterns change in measurable ways—often through restructuring rather than disappearance. Overall, this shows how new computational methods can extend qualitative work by making large-scale patterns in metaphor use empirically tractable.

Headlines and Hashtags: Climate News and Online Engagement

In our technologically saturated society, social media platforms increasingly shape public perception of urgent issues, including climate change. However, despite rising media exposure and a significant increase in climate-related disasters, public concern about climate change has not increased proportionally. Using data from over 10 million tweets and 140,000 news articles from 2016 to 2018, this study examines the relationship between media coverage and online engagement with climate change. The analysis compares the volume and sentiment of climate-related news stories with patterns in climate-related tweets. While a modest positive correlation appears between the sentiment of tweets and news articles, negative correlations emerge across most other measures. In particular, higher volumes of climate-related news coverage are associated with lower average stance and sentiment in climate-related tweets, and the average stance of tweets is negatively correlated with the sentiment of news articles. These findings suggest that increased media coverage may not foster public urgency as expected, raising questions about the role of news media in shaping public perception of politicized issues and highlighting the need for further research on the dynamics of media influence.

The Problem With Who I Know: What Contextualism Can Tell Us About Interpersonal Knowledge Claims, Southwest Philosophy Review 39 (2): 135–148 (2023)

‘I know his name.’ ‘I know something about him.’ ‘I know him.’ Consider how these uses of ‘know’ differ. The first two instances of know, seem to point to knowledge about something. Yet in the latter claim, the subject of the assertion is not a singular fact, but another person. I call these knowledge claims interpersonal knowledge. In the following paper, I provide an account for these interpersonal knowledge claims which employs the Conversational Contextualist view of language by synthesizing Allan Gibbard’s Norm-Expressivist account for ‘good’ with an account of knowledge based in social epistemology. Under my theory ‘knowing someone claims’ amount to endorsements of our beliefs; as such, there is no truth-apt interpersonal knowledge. What is occurring is a self-assessment of our relationship to another person, based on our non-cognitive attitude towards the fact that we should know them. Therefore interpersonal knowledge claims are self-affirmations that assert we are doing what we believe we should, in an attempt to embody our perceived relationship with another person.