Publications & Pre-prints

2024

1. Generative AI and Perceptual Harms: Who’s Suspected of using LLMs?

   Kowe Kadoma, Danaë Metaxa, and Mor Naaman

   2024
2. Why So Serious? Exploring Humor in AAC Through AI-Powered Interfaces

   Tobias Weinberg, Kowe Kadoma, Ricardo E. Gonzalez Penuela, and 2 more authors

   2024
3. The Role of Inclusion, Control, and Ownership in Workplace AI-Mediated Communication

   Kowe Kadoma, Marianne Aubin Le Quere, Xiyu Jenny Fu, and 3 more authors

   In Proceedings of the CHI Conference on Human Factors in Computing Systems 2024

   Abs

   Given large language models’ (LLMs) increasing integration into workplace software, it is important to examine how biases in the models may impact workers. For example, stylistic biases in the language suggested by LLMs may cause feelings of alienation and result in increased labor for individuals or groups whose style does not match. We examine how such writer-style bias impacts inclusion, control, and ownership over the work when co-writing with LLMs. In an online experiment, participants wrote hypothetical job promotion requests using either hesitant or self-assured auto-complete suggestions from an LLM and reported their subsequent perceptions. We found that the style of the AI model did not impact perceived inclusion. However, individuals with higher perceived inclusion did perceive greater agency and ownership, an effect more strongly impacting participants of minoritized genders. Feelings of inclusion mitigated a loss of control and agency when accepting more AI suggestions.

4. Estimating Exposure to Information on Social Networks

   Buddhika Nettasinghe, Kowe Kadoma, Mor Naaman, and 1 more author

   Trans. Soc. Comput. Aug 2024

   Abs

   Estimating exposure to information on a social network is a problem with important consequences for our society. The exposure estimation problem involves finding the fraction of people on the network who have been exposed to a piece of information (e.g., a URL of a news article on Facebook, a hashtag on Twitter). The exact value of exposure to a piece of information is determined by two features: the structure of the underlying social network and the set of people who shared the piece of information. Often, both features are not publicly available (i.e., access to the two features is limited only to the internal administrators of the platform) and are difficult to estimate from data. As a solution, we propose two methods to estimate the exposure to a piece of information in an unbiased manner: a vanilla method which is based on sampling the network uniformly and a method which non-uniformly samples the network motivated by the Friendship Paradox. We provide theoretical results which characterize the conditions (in terms of properties of the network and the piece of information) under which one method outperforms the other. Further, we outline extensions of the proposed methods to dynamic information cascades (where the exposure needs to be tracked in real-time). We demonstrate the practical feasibility of the proposed methods via experiments on multiple synthetic and real-world datasets.