Lindy Le (they/she)

The increasing prevalence of neurodivergence has led society to give greater recognition to the importance of neurodiversity. Yet societal perceptions of neurodivergence continue to be predominantly negative. Drawing on Critical Disability Studies, accessibility researchers have demonstrated how neuronormative assumptions dominate HCI. Despite their guidance, neurodivergent and disabled individuals are still marginalized in technology research. In particular, intersectional identities remain largely absent from HCI neurodivergence research. In this paper, I share my perspective as an outsider of the academic research community: I use critical autoethnography to analyze my experiences of coming to understand, accept, and value my neurodivergence within systems of power, privilege, and oppression. Using Data Feminism as an accessible and practical guide to intersectionality, I derive three tenets for reconceptualizing neurodivergence to be more inclusive of intersectional experiences: (1) neurodivergence is a functional difference, not a deficit; (2) neurodivergent disability is a moment of friction, not a static label; and (3) neurodivergence accessibility is a collaborative practice, not a one-sided solution. Then, I discuss the tenets in the context of existing HCI research, applying the same intersectional lens. Finally, I offer three suggestions for how accessibility research can apply these tenets in future work, to bridge the gap between accessibility theory and practice in HCI neurodivergence research.

Line managers form the first level of management in organizations, and must make complex decisions, while maintaining relationships with those impacted by their decisions. Amidst growing interest in technology-supported decision-making at work, their needs remain understudied. Further, most existing design knowledge for supporting social decision-making comes from domains where decision-makers are more socially detached from those they decide for. We conducted iterative design research with line managers within a technology organization, investigating decision-making practices, and opportunities for technological support. Through formative research, development of a decision-representation tool—DISCERN—and user enactments, we identify their communication and analysis needs that lack adequate support. We found they preferred tools for externalizing reasoning rather than tools that replace interpersonal interactions, and they wanted tools to support a range of intuitive and calculative decision-making. We discuss how design of social decision-making supports, especially in the workplace, can more explicitly support highly interactional social decision-making.

A critical factor in the success of many decision support systems is the accurate modeling of user preferences. Psychology research has demonstrated that users often develop their preferences during the elicitation process, highlighting the pivotal role of system-user interaction in developing personalized systems. This paper introduces a novel approach, combining Large Language Models (LLMs) with Constraint Programming to facilitate interactive decision support. We study this hybrid framework through the lens of meeting scheduling, a time-consuming daily activity faced by a multitude of information workers. We conduct three studies to evaluate the novel framework, including a diary study to characterize contextual scheduling preferences, a quantitative evaluation of the system’s performance, and a user study to elicit insights with a technology probe that encapsulates our framework. Our work highlights the potential for a hybrid LLM and optimization approach for iterative preference elicitation, and suggests design considerations for building systems that support human-system collaborative decision-making processes.