Search for: All records

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. 

Augmented Reality (AR) glasses separate dyadic interactions on diferent sides of the lens, where the person wearing the glasses (primary user) sees an AR world overlaid on their partner (secondary actor). The secondary actor interacts with the primary user under- standing they are seeing both physical and virtual worlds. We use grounded theory to study interaction tasks, participatory design ses- sions, and in-depth interviews of 10 participants and explore how AR real-time modifcations afect them. We observe a power imbalance attributed to the: (1) lack of transparency of the primary user’s view, (2) violation of agency over self-presentation, and (3) discreet record- ing capabilities of AR glasses. This information asymmetry leads to a negotiation of behaviors to reach a silently understood equilibrium. This paper addresses underlying design issues that contribute to power imbalances in dyadic interactions and ofers nuanced insights into the dynamics between primary users and secondary actors.