Tensions in transparent urban AI: designing a smart electric vehicle charge point

Abstract

The increasing use of artificial intelligence (AI) by public actors has led to a push for more transparency. Previous research has conceptualized AI transparency as knowledge that empowers citizens and experts to make informed choices about the use and governance of AI. Conversely, in this paper, we critically examine if transparency-as-knowledge is an appropriate concept for a public realm where private interests intersect with democratic concerns. We conduct a practice-based design research study in which we prototype and evaluate a transparent smart electric vehicle charge point, and investigate experts’ and citizens’ understanding of AI transparency. We find that citizens experience transparency as burdensome; experts hope transparency ensures acceptance, while citizens are mostly indifferent to AI; and with absent means of control, citizens question transparency’s relevance. The tensions we identify suggest transparency cannot be reduced to a product feature, but should be seen as a mediator of debate between experts and citizens.

Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Dispute

“…An overlapping but distinct area of research focuses on the role of AI in the built environment, so-called urban AI [1,42,56,57,67,73,79]. Many application contexts here are mobility-related, for example smart electric vehicle charging [1]; autonomous vehicles [56]; and automated parking control systems [67]. The focus of this research tends to be more on how AI molds, mediates, and orchestrates the daily lived experience of urban places and spaces.…”

“…We then use reflexive thematic analysis [13][14][15] to generate themes from the interview transcripts that together describe the major challenges facing the implementation of contestability in public AI. 1 The empirical work for this study was conducted in Amsterdam. The city has previously explored ways of making camera cars more "human-friendly."…”

Publics’ views on ethical challenges of artificial intelligence: a scoping review

“…Studies motivated by democratization promoted broader public participation in AI, aiming to empower citizens both [40, 43, 45, 78-83, 88, 96] e [46,48,61,78,79,84,85,96,98] f [78,79,86,87,103] Innovation (33 articles) a : illustrative quotes Public engagement presents valuable opportunities to incorporate diverse views and perspectives and to enable critical reflection on organisational practices and/or the direction of innovation. (…) Wider public engagement can play a valuable role in contesting framings and opening up discourses around data ethics and responsible AI to a wider range of perspectives and considerations.…”

“…The tensions we identify suggest transparency cannot be reduced to a product feature, but should be seen as a mediator of debate between experts and citizens. (…) We can also see that not only decisions, but motivations for them must be made transparent [98] Accountability (31 articles) g : illustrative quotes Some participants indicated media as a key mechanism for accountability. Some participants indicated skepticism that institutions and companies could be held accountable [93] The potential psychological impact of AI is shown (…) when young people begin to question whether they may be the problem or cause for not getting the results they expect or want.…”

A Survey on Trustworthy Recommender Systems

“…The term "Explainable Recommendation" was first defined by Zhang et al [341]. As an important sub-field of AI and machine learning research and due to the fact that recommendation naturally involves humans in the loop, the recommender system community has been leading the research on Explainable AI ever since, which triggers a broader scope of explainability research in other AI and machine learning sub-fields [71,340], such as explainability in scientific research [181], computer vision [297], natural language processing [40,106,172,217,229], graph neural networks [265,299], database [112,291], healthcare systems [121,228,350], online education [9,20,216,264,277], psychological studies [271] and cyber-physical systems [10,12,134,135,241].…”