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Generative AI is already widespread in the public sector

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arxiv 2401.01291 v1 pith:R3GMZEEB submitted 2024-01-02 cs.CY

classification cs.CY
keywords generativepublicsectoralreadyclearpotentialrespondentsservices
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Generative AI has the potential to transform how public services are delivered by enhancing productivity and reducing time spent on bureaucracy. Furthermore, unlike other types of artificial intelligence, it is a technology that has quickly become widely available for bottom-up adoption: essentially anyone can decide to make use of it in their day to day work. But to what extent is generative AI already in use in the public sector? Our survey of 938 public service professionals within the UK (covering education, health, social work and emergency services) seeks to answer this question. We find that use of generative AI systems is already widespread: 45% of respondents were aware of generative AI usage within their area of work, while 22% actively use a generative AI system. Public sector professionals were positive about both current use of the technology and its potential to enhance their efficiency and reduce bureaucratic workload in the future. For example, those working in the NHS thought that time spent on bureaucracy could drop from 50% to 30% if generative AI was properly exploited, an equivalent of one day per week (an enormous potential impact). Our survey also found a high amount of trust (61%) around generative AI outputs, and a low fear of replacement (16%). While respondents were optimistic overall, areas of concern included feeling like the UK is missing out on opportunities to use AI to improve public services (76%), and only a minority of respondents (32%) felt like there was clear guidance on generative AI usage in their workplaces. In other words, it is clear that generative AI is already transforming the public sector, but uptake is happening in a disorganised fashion without clear guidelines. The UK's public sector urgently needs to develop more systematic methods for taking advantage of the technology.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SAIF: A Comprehensive Framework for Evaluating the Risks of Generative AI in the Public Sector

    cs.AI 2025-01 conditional novelty 5.0 of 10

    SAIF is a proposed framework that generates multimodal test prompts from a risk taxonomy, jailbreak tricks, and prompt styles to evaluate generative AI risks in the public sector.

  2. Generative AI Literacy: Twelve Defining Competencies

    cs.HC 2024-11 conditional novelty 5.0 of 10

    A literature-based proposal of twelve competencies for generative AI literacy, intended as a framework for education, policy, and assessment.

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