LIST OF PANELS

General Objective:

The social impacts of national AI public administration failures and the international urgency to regulate AI make national news, daily (Heikkilä, 2022; Kleinman, 2023).  The shortcomings of these AI policy concerns often reach journalists from critical data studies (CDS) research (Murgia, 2024). Despite this, CDS scholars and approaches are under-represented in critical policy studies (Oman, 2022). This panel will reflect on how approaches from critical data studies can be better incorporated into the field of critical policy studies. We argue this is vital to inform impact on this dynamic area of policy and practice.

Scientific relevance: 

There is an urgency to adopt AI to meet aspirations for innovation and efficiency, whilst also acknowledging the need to legislate against potential harms (i.e., UK Government, 2024; EU, 2024), and nations differ in approach. For example, from 2021 to 2024 Mexico created 43 AI-related laws, mostly sanctions to protect human rights, but without due consideration of the governance, institutions or investment required for AI (Peña Mendoza de la 2024). It is therefore important to learn from across national contexts and what has constituted policy success.

Data-driven (or evidence-based) approaches to policy-making have long been seen as too simplistic to capture the ‘wicked problems’ of complex social issues (Rittel and Webber 1973; Head 2019). Policy studies, however, has undervalued the wicked problem of data-driven decision-making in and of itself (Oman, 2022). Alongside this, social policy scholarship has overlooked digital policy (Henman, 2022) as have domain-specific policy contexts (Oman, 2024). Meanwhile, the proliferation of AI-enabled systems in welfare (Eubanks, 2019), justice and education judgements (O’Neill, 2016) continue to lead to high-profile public administration and legislative failures that impact most negatively on marginalised populations (Heikkilä, 2022; Murgia, 2024). Gaps in understanding across policy, practice and research therefore have scientific and social implications that policy studies should urgently address.

AI policy-makers and commentators are increasingly acknowledging the need for social science and humanities research to ascertain the social impacts of AI innovations and the importance of different approaches (Tassioulas, 2024). For example, participatory decision-making has gained popularity in data governance, owing to its reliance on personal data, alongside the social impacts of data privacy (e.g. Ada Lovelace, n.d). This has also seen pressures to include attitudes research to inform AI policy and practices, but the default survey methodologies that inform policy insufficiently capturing affective responses (Taylor et al., 2023). Methods also don’t often incorporate issues of making data and AI systems legible to all publics, which are vital to democracy (Bates et al., 2023). The evidence informing AI policy-making and regulation is, therefore, often limited. It is clear, then, that policy studies must address AI as another wicked problem for policy-making and there is a clear role for interdisciplinary approaches to policy studies. 

Critical Data Studies scholars are increasingly focussed on AI policy and practice, but are not often incorporated into policy studies (Oman, 2022), despite these scholars’ approaches as contributions to understanding policy failures (e.g. Fahimi et al., 2024). This panel will contribute to the field of policy studies by reflecting on CDS approaches, including methods, case studies and theoretical developments. 

Research Question: What can critical data studies approaches bring to policy studies on the subject of AI? 

We aim to address hypotheses regarding the benefits and challenges of interdisciplinary study in AI policy. We expect these to include: 1, concerns regarding power asymmetries in international collaborative policy, practice and research; 2, discourse on including public voices into AI policy-making; 3, methods to understand the complexity of AI policy and governance from specific contexts and case studies to national and international standards and frameworks.

References

Ada Lovelace (n.d.) Participatory data governance. Available at: https://www.adalovelaceinstitute.org/project/participatory-data-governance/ 

Bates, J., Kennedy, H., Medina Perea, I., Oman, S. & Pinney, L. (2023) Socially meaningful transparency in data-based systems: reflections and proposals from practice. Journal of Documentation. 80(1). DOI:/10.1108/JD-01-2023-0006.

EU (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence. Available at: https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:32024R1689 

Eubanks, V. (2019). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. New York: Picador, St Martin’s Press

Fahimi, M., Falk, P., Gray, J. W. Y., Jarke, J., Kinder-Kurlanda, K., Light, E., McGeachey, E., Medina Perea, I., Poechhacker, N., Poirier, L., Röhle, T., Sharon, T., Stevens, M., Gastel, B. van, White, Q., & Zakharova, I. (2024). 3: In/visibilities in Data Studies: Methods, Tools, and Interventions. In Dialogues in Data Power: Shifting Response-abilities in a Datafied World. Bristol University Press. https://bristoluniversitypressdigital.com/edcollchap-oa/book/9781529238327/ch003.xml

Head, B. (2022). Wicked Problems in Public Policy: Understanding and Responding to Complex Challenges. Palgrave Macmillan. 

Heikkilä, M. (2022, March 29). Dutch scandal serves as a warning for Europe over risks of using algorithms. POLITICO. https://www.politico.eu/article/dutch-scandal-serves-as-a-warning-for-europe-over-risks-of-using-algorithms/

Henman, P.W.F. (2022). ‘Digital Social Policy: Past, Present, Future’, Journal of Social Policy, 51(3), pp. 535–550. doi:10.1017/S0047279422000162.

Kleinman, Z. (2023). Can Rishi Sunak’s big summit save us from AI nightmare?. 28 October 2023. BBC news. Available at: https://www.bbc.co.uk/news/technology-67172230

Murgia, M. (2024). Code Dependent: Living in the Shadow of AI. London: Pan Macmillan. 

O’Neill, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. London: Penguin.

Oman, S. (2022). ‘Re-performance: a critical and reparative methodology for everyday expertise and data practice in policy knowledge’, International Review of Public Policy, 3(3), pp. 291-313. DOI:/10.4000/irpp.1833.

Oman, S. (2024). ‘Digital culture - a review of evidence and experience, with recommendations for UK policy, practice and research.’ London: UK Government.

Peña Mendoza de la, S., Ibarra Sánchez, E., Santoyo De Jesús, C. (2024). Panorama de la inteligencia artificial en México: hacia una estrategia nacional. Available at: https://cdnusers3ros.s3.amazonaws.com/public/9e3213120ef1ec5246ed316117908803/47ddfa6a29074e2b1426d394295660281717889675_1717889675.pdf

Rittel, H. W. J., & M. M. Webber. (1973). Dilemmas in a General Theory of Planning. Policy Sciences, 4(2): 155–169.  doi:10.1007/BF01405730. 

Tasioulas, J. (2021). The role of the arts and humanities in thinking about artificial intelligence (AI). Ada Lovelace. Available here: https://www.adalovelaceinstitute.org/blog/role-arts-humanities-thinking-artificial-intelligence-ai/ 

Taylor, M., Kennedy, H. & Oman, S. (2024). Challenging assumptions about the relationship between awareness of and attitudes to data uses amongst the UK public, The Information Society, 40(1). 32-53, DOI: 10.1080/01972243.2023.2283729. 

UK Government (2024). A pro-innovation approach to AI regulation: government response. Command Paper: CP 1019 presented to Parliament by the Secretary of State for Science, Innovation and Technology by Command of His Majesty on 6 February 2024. Available here: https://www.gov.uk/government/consultations/ai-regulation-a-pro-innovation-approach-policy-proposals/outcome/a-pro-innovation-approach-to-ai-regulation-government-response

This panel invites researchers, policy-makers, and practitioners to submit papers that work at the boundaries of Critical Data Studies (CDS) and Critical Policy Studies, with application to the dynamic field of artificial intelligence (AI) policy. Of particular interest is how CDS approaches can contribute to policy studies and practise for impact against AI’s potential for social harms. 

Growing pressures to adopt AI to meet aspirations for innovation and efficiency are coupled with acknowledgements of the need to legislate against potential harms (i.e., UK Government, 2024; EU, 2024). Data-driven (or evidence-based) approaches to policy-making have long been seen as too simplistic to capture the ‘wicked problems’ of complex social issues (Rittel and Webber, 1973; Head, 2019). Policy studies, however, has undervalued the wicked problem of data-driven decision-making in and of itself (Oman, 2022), and social policy scholarship has overlooked digital concerns (Henman, 2022). This gap is increasingly urgent for policy studies and practice, with the proliferation of AI-enabled systems in welfare (Eubanks, 2019), justice and education judgements (O’Neill, 2016) leading to high profile public administration and legislative failures that impact most negatively on marginalised populations (Heikkilä, 2022; Murgia, 2024). 

AI policy makers and commentators acknowledge more social science and humanities research is required to ascertain the social impacts of AI innovations and the importance of different approaches (Tassioulas, 2024). For example, the resurgence in citizen-informed decision-making for data governance has pressured the inclusion of attitudes research to inform AI policy and practices, but the default survey methodologies that inform policy insufficiently capturing affective responses (Taylor et al., 2023). Methods also don’t often incorporate issues of making data and AI systems legible to all publics, which are vital to democracy (Bates et al., 2023). The evidence informing AI policy-making and regulation is, therefore, often limited. It is clear, then, that policy studies must address what is becoming an increasingly wicked problem for policy-making and there is a clear role for interdisciplinary approaches. 

This panel begins with the hypothesis that critical data studies approaches can inform AI policy, practice and research. We particularly welcome papers that address one or more of the following themes, but the panel is not limited by them:

Landscape of AI policy, whether comparative analysis, or systematic analyses of international standards  In depth case studies of regions, nations, localities, marginalised communities, policy domains (i.e. health) or public sector deployment (i.e. local schools) 
Empirical papers on AI practice
Critical data studies informed methods and tools for approaching the study of AI policy and practice  (including creative, ethnographic and theoretical approaches)
Other arts, humanities and social science approaches that may contribute to the field of AI policy