arXiv subjects

Louise Hickman

Publications and source records attributed to Louise Hickman.

2 recordsLinked to original sources

arXiv2025

Reciprocity Deficits: Observing AI in the street with everyday publics

The street has emerged as a primary site where everyday publics are confronted with AI as an infrastructural phenomenon, as machine learning-based systems are now commonly deployed in this setting in the form of automated cars, facial recognition, smart billboards and the like. While these deployments of AI in the street have attracted significant media attention and public controversy in recent years, the presence of AI in the street often remains inscrutable, and many everyday publics are unaware of it. In this paper, we explore the challenges and possibilities of everyday public engagement with AI in the situated environment of city streets under these paradoxical conditions. Combining perspectives and approaches from social and cultural studies of AI, Design Research and Science and Technology Studies (STS), we explore the affordances of the street as a site for 'material participation' in AI through design-based interventions: the creation of 'everyday AI observatories.' We narrate and reflect on our participatory observations of AI in five city streets in the UK and Australia and highlight a set of tensions that emerged from them: 1) the framing of the street as a transactional environment, 2) the designed invisibility of AI and its publics in the street 3) the stratification of street environments through statistical governance. Based on this discussion and drawing on Jane Jacobs' notion of "eyes on the street," we put forward the relational notion of "reciprocity deficits" between AI infrastructures and everyday publics in the street. The conclusion reflects on the consequences of this form of social invisibility of AI for situated engagement with AI by everyday publics in the street and for public trust in urban governance.

cs.HC↗

arXiv2022

Definition drives design: Disability models and mechanisms of bias in AI technologies

The increasing deployment of artificial intelligence (AI) tools to inform decision making across diverse areas including healthcare, employment, social benefits, and government policy, presents a serious risk for disabled people, who have been shown to face bias in AI implementations. While there has been significant work on analysing and mitigating algorithmic bias, the broader mechanisms of how bias emerges in AI applications are not well understood, hampering efforts to address bias where it begins. In this article, we illustrate how bias in AI-assisted decision making can arise from a range of specific design decisions, each of which may seem self-contained and non-biasing when considered separately. These design decisions include basic problem formulation, the data chosen for analysis, the use the AI technology is put to, and operational design elements in addition to the core algorithmic design. We draw on three historical models of disability common to different decision-making settings to demonstrate how differences in the definition of disability can lead to highly distinct decisions on each of these aspects of design, leading in turn to AI technologies with a variety of biases and downstream effects. We further show that the potential harms arising from inappropriate definitions of disability in fundamental design stages are further amplified by a lack of transparency and disabled participation throughout the AI design process. Our analysis provides a framework for critically examining AI technologies in decision-making contexts and guiding the development of a design praxis for disability-related AI analytics. We put forth this article to provide key questions to facilitate disability-led design and participatory development to produce more fair and equitable AI technologies in disability-related contexts.

cs.AI↗