Framing Artificial Intelligence Futures

Framing Artificial Intelligence Futures: Implications for Higher

EducationDr. Inga Ulnicane

Centre for Higher Education Studies (CHES) UCL seminar28 April 2021

Agenda

• Introduction: Why focus on AI policy?

• Conceptual & methodological framework

• Emerging tech, framing, methods & data

• Findings on framing AI policy

• Framing universities & higher education in AI policy

• Dominant frames, marginal frames, omissions & silences

• Open questions

Framing Artificial Intelligence Futures 2

AI policy as a site of contestation of possible & desirable futures


Conceptual & methodological framework


AI as an emerging technology

• While AI has been known since the 1950s, recent major advances due to availability of big data & computing power

• Each emerging tech – robotics, neurotech, nano – different but also some common features: radical novelty, relatively fast growth, prominent impact, uncertainty & ambiguity (Rotolo et al 2015)

• Hype, positive & negative expectations & their performative role – irrespective of how accurate, they guide activities, set agendas & provide legitimacy (Van Lente et al 2013)


Policy framing

• Policy frames are narratives ‘diagnostic/ prescriptive stories that tell, within a given issue terrain, what needs fixing and how it might be fixed’ (Rein & Schon 1996)

• Importance of silences & politics hidden in the framing – ‘the non-innocence of how “problems” get framed within policy proposals, how the frames will affect what can be thought about and how this affects possibilities for action’ (Bacchi 2000)

• Rhetorical frames ‘constructed from the policy-relevant texts that play important roles in policy discourse, where the context is one of debate, persuasion or justification’ (Rein & Schon 1996)


Methods & data

• Policy documents - ‘vehicles of messages, communicating or reflecting intentions, objectives, commitments, proposals, “thinking”, ideology and responses to external events’ (Freeman & Maybin 2011)

• Analysis of 49 AI policy documents from national governments, international organizations, think tanks & consultancies

• Following actors’ definitions of AI• Adopted 2016-2018• Europe & US

• Qualitative analysis of the documents in Nvivo focusing on topics such as how AI is framed, benefits & problems, governance & policy recommendations


AI policy document analysis team (AY 2018-2019)


Inga Ulnicane

Findings on framing AI policy


AI governance & policy research programme


Context: AI as an emerging technology

• Performative function of hype & expectations – fast developing AI policies

• Countries & organizations learning & borrowing from each other• Role of international organizations such as the World Economic Forum &

OECD• Competitiveness & global leadership discourse, ‘next space race’

• Changing technology policy paradigms – shift from focus of economics & supply-driven policies to addressing societal challenges & demand-side (Diercks et al 2019)

• While both paradigms can be found in the documents, launch of numerous AI policy documents suggest strong technology push and supply-side driven approach


Framing AI

• Revolutionary, transformative & disruptive• Similar to previous revolutions – industrial, digital• Part of the Fourth Industrial Revolution• Can help to solve societal challenges & achieve the UN Sustainable Development

Goals• Changes every aspect of our lives

• ‘a transformative technology that holds promise for tremendous societal and economic benefit. AI has the potential to revolutionize how we live, work learn and communicate’ (Executive Office of the President 2016)

• Contested: brings opportunities & benefits but also entails risks & concerns about impact on e.g. equality, distribution of power, fairness

• Transformation & disruption of labour markets


Implications for AI policy

• Realize opportunities• Investments, commercialization of research,

support for companies incl. SMEs

• Mitigate socio-economic problems• Prepare for changes in labour markets,

retraining schemes

• Ensure ethics & responsibility• Preparing ethics guidelines & regulation

• Need to collaborate at international level• Global Partnership for AI launched in 2020


Framing universities & higher education in AI policy


Universities & higher education in AI policy

• Policy documents assign important roles to universities, higher education, academics and academia in AI transformation/ revolution but overarching AI documents are quite general about their roles

• Dominant frames – instrumental, responsive; traditional roles of universities – training & research - adjusted to AI

• E.g. training AI workforce (see also Schiff, forthcoming); source of academic research

• Omissions & silences – more active roles of universities in co-shaping development & use of AI

• E.g. spaces for critical debates


Main frames of Universities in AI policy

• Investments in training, education & learning to respond to AI transforming the labour market

• Jobs disappearing, not affected (e.g. human interaction, creativity), created• Modernising, restructuring, redesigning & adapting education systems & curricula

(including re-training programmes)• Include focus on digital skills & AI literacy

• Training (diverse) AI workforce – to address shortages of ICT, STEM experts• New AI study programmes, PhD scholarships, attract international talent • Include ethics of AI, interdisciplinary aspects (law, psychology)

• Collaboration between Universities & AI companies • Bring AI academic research to commercialisation & spin-out start-ups• Train PhDs & MScs in AI – sharing costs between public & private sector


Marginal frames of Universities in AI policy

• Using AI – e.g. in student selection, teaching • Benefits of AI in teaching – larger classrooms,

individualized approaches• Concerns – using AI in selection could reinforce

historical biases• In passing, academics among stakeholders

contributing to multi-stakeholder forums • Implicit: Universities as hubs of AI knowledge &

expertise – numerous mentions of AI experts from universities & AI research done in universities


Omissions & silences in framing Universities in AI policy• Universities co-shaping

development & use of AI • Universities as spaces for critical

debates • Providing academic freedom for

study & research of AI & its implications (including problematic aspects)

• How can AI help to achieve SDG4 ‘Quality Education’?


Open questions

• Why certain – instrumental - frames are dominant, while others -more active, co-shaping – are overlooked? Path-dependence? Interests? Are these frames challenged? Are alternatives discussed?

• Any novel ideas in revolutionary & transformative framing of AI? Or largely just renewal & amplification of traditional recipes & prescriptions?

• Relationship between rhetorical frames & action frames ‘constructed from the evidence provided by observations of patterns of action inherent in the practice of policy practitioners’ (Rein & Schon 1996)


References

• Ulnicane, I., W. Knight, T. Leach, B. C. Stahl and W.-G. Wanjiku (2020) Framing governance for a contested emerging technology: insights from AI policy, Policy and Society, https://doi.org/10.1080/14494035.2020.1855800

• Ulnicane, I., D. O. Eke, W. Knight, G. Ogoh & B. C. Stahl (2021) Good governance as a response to discontents? Déjà vu, or lessons for AI from other emerging technologies, Interdisciplinary Science Reviews, 46:1-2, 71-93 https://doi.org/10.1080/03080188.2020.1840220


https://doi.org/10.1080/14494035.2020.1855800

https://doi.org/10.1080/03080188.2020.1840220

Documents

Framing Artificial Intelligence Futures