AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks

Andrus, McKane; Dean, Sarah; Gilbert, Thomas; Zick, Tom

doi:10.1109/istas50296.2020.9462193

Cited by 7 publications

(3 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In what follows, we outline the motivating concerns of each subfield and identify key developments in their evolution. For a more complete analysis see [5].…”

Section: Historical Backgroundmentioning

confidence: 99%

“…Due in part to a gap between the pace and investment in advancing fundamental AI technologies and reflecting on its potential harms, identifying points of engagement between critical theory and various AI domains remains challenging. Furthermore, emerging subdomains such as AI Safety, Fair Machine Learning (Fair ML), and Human-in-the-Loop (HIL) Autonomy lack a common origin, and subsequently hold different conceptualizations about the relationship between their systems and society [5]. While these differences make it challenging to develop interventions with field wide relevance, the distinct lines of inquiry pursued by each subfield present opportunities to research AI's increasingly central role in social relations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Axes for Sociotechnical Inquiry in AI Research

Dean

Gilbert²,

Zick

2021

IEEE Trans. Technol. Soc.

Self Cite

View full text Add to dashboard Cite

The development of artificial intelligence (AI) technologies has far exceeded the investigation of their relationship with society. Sociotechnical inquiry is needed to mitigate the harms of new technologies whose potential impacts remain poorly understood. To date, subfields of AI research develop primarily individual views on their relationship with sociotechnics, while tools for external investigation, comparison, and cross-pollination are lacking. In this paper, we propose four directions for inquiry into new and evolving areas of technological development: value-what progress and direction does a field promote, optimization-how the defined system within a problem formulation relates to broader dynamics, consensus-how agreement is achieved and who is included in building it, and failurewhat methods are pursued when the problem specification is found wanting. The paper provides a lexicon for sociotechnical inquiry and illustrates it through the example of consumer drone technology.

show abstract

“…In what follows, we outline the motivating concerns of each subfield and identify key developments in their evolution. For a more complete analysis see [5].…”

Section: Historical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Axes for Sociotechnical Inquiry in AI Research

Dean

Gilbert²,

Zick

2021

IEEE Trans. Technol. Soc.

Self Cite

View full text Add to dashboard Cite

show abstract

“…SeeLeveson (2011) for further details.10 The 'solutionism trap' occurs when it is assumed that technical solutions alone can solve complex social and political problems. According toAndrus et al (2021), a first step to avoid the solutionism trap is to maintain a robust culture of questioning which problems should be addressed, and why. A second step would be to examine which properties are not tied to the technical objects under investigation but to their social contexts.…”

mentioning

confidence: 99%

Ethics-based auditing of automated decision-making systems: intervention points and policy implications

Mökander

axente²

2021

AI & Soc

View full text Add to dashboard Cite

Organisations increasingly use automated decision-making systems (ADMS) to inform decisions that affect humans and their environment. While the use of ADMS can improve the accuracy and efficiency of decision-making processes, it is also coupled with ethical challenges. Unfortunately, the governance mechanisms currently used to oversee human decision-making often fail when applied to ADMS. In previous work, we proposed that ethics-based auditing (EBA)—that is, a structured process by which ADMS are assessed for consistency with relevant principles or norms—can (a) help organisations verify claims about their ADMS and (b) provide decision-subjects with justifications for the outputs produced by ADMS. In this article, we outline the conditions under which EBA procedures can be feasible and effective in practice. First, we argue that EBA is best understood as a ‘soft’ yet ‘formal’ governance mechanism. This implies that the main responsibility of auditors should be to spark ethical deliberation at key intervention points throughout the software development process and ensure that there is sufficient documentation to respond to potential inquiries. Second, we frame AMDS as parts of larger sociotechnical systems to demonstrate that to be feasible and effective, EBA procedures must link to intervention points that span all levels of organisational governance and all phases of the software lifecycle. The main function of EBA should, therefore, be to inform, formalise, assess, and interlink existing governance structures. Finally, we discuss the policy implications of our findings. To support the emergence of feasible and effective EBA procedures, policymakers and regulators could provide standardised reporting formats, facilitate knowledge exchange, provide guidance on how to resolve normative tensions, and create an independent body to oversee EBA of ADMS.

show abstract

Philosophical Investigations into AI Alignment: A Wittgensteinian Framework

Pérez-Escobar,

Sarikaya

2024

Philos. Technol.

View full text Add to dashboard Cite

We argue that the later Wittgenstein’s philosophy of language and mathematics, substantially focused on rule-following, is relevant to understand and improve on the Artificial Intelligence (AI) alignment problem: his discussions on the categories that influence alignment between humans can inform about the categories that should be controlled to improve on the alignment problem when creating large data sets to be used by supervised and unsupervised learning algorithms, as well as when introducing hard coded guardrails for AI models. We cast these considerations in a model of human–human and human–machine alignment and sketch basic alignment strategies based on these categories and further reflections on rule-following like the notion of meaning as use. To sustain the validity of these considerations, we also show that successful techniques employed by AI safety researchers to better align new AI systems with our human goals are congruent with the stipulations that we derive from the later Wittgenstein’s philosophy. However, their application may benefit from the added specificities and stipulations of our framework: it extends on the current efforts and provides further, specific AI alignment techniques. Thus, we argue that the categories of the model and the core alignment strategies presented in this work can inform further AI alignment techniques.

show abstract

AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks

Cited by 7 publications

References 43 publications

Axes for Sociotechnical Inquiry in AI Research

Axes for Sociotechnical Inquiry in AI Research

Ethics-based auditing of automated decision-making systems: intervention points and policy implications

Philosophical Investigations into AI Alignment: A Wittgensteinian Framework

Contact Info

Product

Resources

About