Introducing contextual transparency for automated decision systems

Sloane, Mona; Solano-Kamaiko, Ian René; Yuan, Jian; Dasgupta, Aritra; Stoyanovich, Julia

doi:10.1038/s42256-023-00623-7

Cited by 4 publications

(8 citation statements)

References 65 publications

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“…(2022) and Obar (2022). As Sloane et al . (2023) identify, this latter approach defines meaningfulness in the pragmatic tradition as “the property of conveying information that is receivable and useful to a recipient, and that has consequences in that it makes a difference to practice”.…”

Section: Questioning Transparency Practicesmentioning

confidence: 90%

“…Schor et al (2022) and Obar (2022). As Sloane et al (2023) identify, this latter approach defines meaningfulness in the pragmatic tradition as "the property of conveying information that is receivable and useful to a recipient, and that has consequences in that it makes a difference to practice". Pasquale's (2015, p. 217) suggestion that clear and actionable information that allows citizens to judge the safety of a system, rather than detail about how it functions, is needed can be understood as an example of this type of more meaningful transparency practice.…”

Section: Questioning Transparency Practicesmentioning

confidence: 99%

“…Our first three and our final two proposals articulate our emphasis on the social in “socially meaningful transparency”, while proposals four and five build on existing individual in context definitions of meaningful transparency in the literature (e.g. Sloane et al ., 2023; Schor et al ., 2022).…”

Section: Challenges As Openings: Proposals For Meaningful Transparencymentioning

confidence: 99%

“…In this way, socially meaningful transparency departs from other suggestions for addressing the transparency challenge. It is more socially grounded in nature than, for example, individualised approaches to meaningful transparency (Sloane et al ., 2023; Schor et al ., 2022), or legal approaches such as Obar's (2020) suggestion that individuals could delegate responsibilities to expert practitioners or Pasquale's (2015) idea to make available information that enables individuals to assess the safety of a system.…”

Section: Conclusion: Enacting Socially Meaningful Transparency In Con...mentioning

confidence: 99%

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Socially meaningful transparency in data-based systems: reflections and proposals from practice

Bates

Kennedy

Perea

et al. 2023

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PurposeThe purpose is to present proposals to foster what we call a socially meaningful transparency practice that aims to enhance public understanding of data-based systems through the production of accounts that are relevant and useful to diverse publics, and society more broadly.Design/methodology/approachThe authors’ proposals emerge from reflections on challenges they experienced producing written and visual accounts of specific public sector data-based systems for research purposes. Following Ananny and Crawford's call to see limits to transparency practice as “openings”, the authors put their experience into dialogue with the literature to think about how we might chart a way through the challenges. Based on these reflections, the authors outline seven proposals for fostering socially meaningful transparency.FindingsThe authors identify three transparency challenges from their practice: information asymmetry, uncertainty and resourcing. The authors also present seven proposals related to reduction of information asymmetries between organisations and non-commercial external actors, enhanced legal rights to access information, shared decision making about what gets made transparent, making visible social impacts and uncertainties of data-systems, clear and accessible communication, timing of transparency practices and adequate resourcing.Social implicationsSocially meaningful transparency aims to enhance public understanding of data-based systems. It is therefore a necessary condition not only for informed use of data-based products, but crucially for democratic engagement in the development of datafied societies.Originality/valueThe paper contributes to existing debates on meaningful transparency by arguing for a more social, rather than individual, approach to imagining how to make transparency practice more meaningful. The authors do this through their empirical reflection on our experience of doing transparency, conceptually through our notion of socially meaningful transparency, and practically through our seven proposals.

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Section: Questioning Transparency Practicesmentioning

confidence: 90%

Section: Questioning Transparency Practicesmentioning

confidence: 99%

Section: Challenges As Openings: Proposals For Meaningful Transparencymentioning

confidence: 99%

Section: Conclusion: Enacting Socially Meaningful Transparency In Con...mentioning

confidence: 99%

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Socially meaningful transparency in data-based systems: reflections and proposals from practice

Bates

Kennedy

Perea

et al. 2023

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“…Contextual analysis is needed to support meaningful participation in (algorithmic) governance. Tools to articulate responsibility and decision-making can be disconnected from what practitioners need [83,114,131], but articulating the decisions being made in the implementation of systems can better reveal where decision-making is happening in the first place [61,62,75,84,113]. Transparency is needed not just of design choices, but also of the policy questions these choices often seek to answer [89].…”

Section: Related Workmentioning

confidence: 99%

Algorithmic Transparency and Participation through the Handoff Lens: Lessons Learned from the U.S. Census Bureau’s Adoption of Differential Privacy

Abdu,

Chambers,

Mulligan

et al. 2024

The 2024 ACM Conference on Fairness, Accountability, and Transparency

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Emerging discussions on the responsible government use of algorithmic technologies propose transparency and public participation as key mechanisms for preserving accountability and trust. But in practice, the adoption and use of any technology shifts the social, organizational, and political context in which it is embedded. Therefore translating transparency and participation efforts into meaningful, effective accountability must take into account these shifts. We adopt two theoretical frames, Mulligan and Nissenbaum's handoff model and Star and Griesemer's boundary objects, to reveal such shifts during the U.S. Census Bureau's adoption of differential privacy (DP) in its updated disclosure avoidance system (DAS) for the 2020 census. This update preserved (and arguably strengthened) the confidentiality protections that the Bureau is mandated to uphold, and the Bureau engaged in a range of activities to facilitate public understanding of and participation in the system design process. Using publicly available documents concerning the Census' implementation of DP, this case study seeks to expand our understanding of how technical shifts implicate values, how such shifts can afford (or fail to afford) greater transparency and participation in system design, and the importance of localized expertise throughout.We present three lessons from this case study toward grounding understandings of algorithmic transparency and participation: (1) efforts towards transparency and participation in algorithmic governance must center values and policy decisions, not just technical design decisions; (2) the handoff model is a useful tool for revealing how such values may be cloaked beneath technical decisions; and(3) boundary objects alone cannot bridge distant communities without trusted experts traveling alongside to broker their adoption.CCS Concepts: • Social and professional topics → Government technology policy; • Security and privacy → Human and societal aspects of security and privacy; • Applied computing → Law, social and behavioral sciences.

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AI for crisis decisions

Comes

2024

Ethics Inf Technol

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Increasingly, our cities are confronted with crises. Fuelled by climate change and a loss of biodiversity, increasing inequalities and fragmentation, challenges range from social unrest and outbursts of violence to heatwaves, torrential rainfall, or epidemics. As crises require rapid interventions that overwhelm human decision-making capacity, AI has been portrayed as a potential avenue to support or even automate decision-making. In this paper, I analyse the specific challenges of AI in urban crisis management as an example and test case for many super wicked decision problems. These super wicked problems are characterised by a coincidence of great complexity and urgency. I will argue that from this combination, specific challenges arise that are only partially covered in the current guidelines and standards around trustworthy or human-centered AI. By following a decision-centric perspective, I argue that to solve urgent crisis problems, the context, capacities, and networks need to be addressed. AI for crisis response needs to follow dedicated design principles that ensure (i) human control in complex social networks, where many humans interact with AI; (ii) principled design that considers core principles of crisis response such as solidarity and humanity; (iii) designing for the most vulnerable. As such this paper is meant to inspire researchers, AI developers and practitioners in the space of AI for (urban) crisis response – and other urgent and complex problems that urban planners are confronted with.

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Introducing contextual transparency for automated decision systems

Cited by 4 publications

References 65 publications

Socially meaningful transparency in data-based systems: reflections and proposals from practice

Socially meaningful transparency in data-based systems: reflections and proposals from practice

Algorithmic Transparency and Participation through the Handoff Lens: Lessons Learned from the U.S. Census Bureau’s Adoption of Differential Privacy

AI for crisis decisions

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