Overcoming the Challenges of Collaboratively Adopting Artificial Intelligence in the Public Sector

Campion, Averill; Gascó-Hernández, Mila; Mikhaylov, Slava; Estève, Marc

doi:10.1177/0894439320979953

Cited by 55 publications

(42 citation statements)

References 42 publications

(147 reference statements)

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“…Shneiderman (2020) emphasizes including management to achieve reliability and security (Shneiderman 2020). Others point out that specific challenges arise from employing AI in the context of inter-organizational collaboration (Campion et al 2020).…”

Section: Human Collaboration and Organization Within Hcimentioning

confidence: 99%

“…Handling these influences requires a continuous monitoring of and coordination with the external world (4.4, 4a). Some changes result from external factors (4a) that include the societal context (Rahwan 2018), others usually include aspects of inter-organizational collaboration (Campion et al 2020) with an ecology of external partners. We call all those tasks that deal with contextual factors and changes "C-Tasks."…”

Section: Interplay 4: Between the Drivers Of Dealing With Changes And...mentioning

confidence: 99%

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Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence

Herrmann

Pfeiffer

2022

AI & Soc

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The human-centered AI approach posits a future in which the work done by humans and machines will become ever more interactive and integrated. This article takes human-centered AI one step further. It argues that the integration of human and machine intelligence is achievable only if human organizations—not just individual human workers—are kept “in the loop.” We support this argument with evidence of two case studies in the area of predictive maintenance, by which we show how organizational practices are needed and shape the use of AI/ML. Specifically, organizational processes and outputs such as decision-making workflows, etc. directly influence how AI/ML affects the workplace, and they are crucial for answering our first and second research questions, which address the pre-conditions for keeping humans in the loop and for supporting continuous and reliable functioning of AI-based socio-technical processes. From the empirical cases, we extrapolate a concept of “keeping the organization in the loop” that integrates four different kinds of loops: AI use, AI customization, AI-supported original tasks, and taking contextual changes into account. The analysis culminates in a systematic framework of keeping the organization in the loop look based on interacting organizational practices.

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Section: Human Collaboration and Organization Within Hcimentioning

confidence: 99%

Section: Interplay 4: Between the Drivers Of Dealing With Changes And...mentioning

confidence: 99%

Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence

Herrmann

Pfeiffer

2022

AI & Soc

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“…deep learning), mašininis samprotavimas (angl. machine reasoning) ir robotika (Campion et al, 2020).…”

Section: Didžiųjų Duomenų Ir Dirbtinio Intelekto Technologijų Naudojimo Viešojo Valdymo Ir Socialinės Politikos Srityje Galimybėsunclassified

The Analysis of Opportunities of the Application of Big Data and Artificial Intelligence Technologies in Public Governance and Social Policy

Rakšnys

Gudelis

Guogis

2021

STEPP

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This interdisciplinary article presents a concept of the 21st century and phenomena that are products of the 4th industrial revolution – big data and Artificial Intelligence technologies – as well as the opportunities of their application in public governance and social policy. This paper examines the advantages and disadvantages of big data, problems of data collection, its reliability and use. Big data can be used for the analysis and modeling of phenomena relevant to public governance and social policy. Big data consist of three main types: a) historical data, b) present data with little delay, c) prognostic data for future forecasting. The following categories of big data can be defined as: a) data from social networks, b) traditional data from business systems, c) machine-generated data, such as water extraction, pollution, satellite information. The article analyzes the advantages and disadvantages of big data. There are big data challenges such as data security, lack of cooperation in civil service and social work, in rare situations – data fragmentation, incompleteness and erroneous issues, as well as ethical issues regarding the analysis of data and its use in social policy and social administration. Big data, covered by Artificial Intelligence, can be used in public governance and social policy by identifying “the hot spots” of various phenomena, by prognosing the meanings of variables in the future on the basis of past time rows, and by calculating the optimal motion of actions in the situations where there are possible various alternatives. The technologies of Artificial Intelligence are used more profoundly in many spheres of public policy, and in the governance of COVID-19 pandemics too. The substantial advantages of the provided big data and Artificial Intelligence are a holistic improvement of public services, possibilities of personalization, the enhancement of citizen satisfaction, the diminishing of the costs of processing expenditure, the targeting of adopted and implemented decisions, more active involvement of citizens, the feedback of the preferences of policy formation and implementation, the observation of social phenomenas in real time, and possibilities for more detailed prognosing. Challenges to security of data, necessary resources and competences, the lack of cooperation in public service, especially rare instances of data fragmentation, roughness, falseness, and ethical questions regarding data analysis and application can be evaluated as the most significant problems of using big data and Artificial Intelligence technologies. Big data and their analytics conducted using Artificial Intelligence technologies can contribute to the adequacy and objectivity of decisions in public governance and social policy, effectively curbing corruption and nepotism by raising the authority and confidence of public sector organizations in governance, which is so lacking in the modern world.

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“…If data is collected by a government, but then used by a private sector company to train an algorithm, can the company sell this trained algorithm to other countries, or can the procuring organization retain complete ownership over the data? This issue works in the reverse as well, in the case that data is coming from the private sector organization (Campion et al, 2020).…”

Section: Challenges Associated With the Public Procurement Of Aimentioning

confidence: 99%

Towards a Systematic Understanding on the Challenges of Procuring Artificial Intelligence in the Public Sector

McBride¹,

Noordt²,

Misuraca³

et al. 2021

Preprint

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There is an increased interest amongst governments and public sector organisations about how to best integrate artificial intelligence into their day-to-day business processes. Yet, a large majority of technical know how is concentrated within the private sector, requiring most public sector organisations to rely on public procurement for AI systems. While many governments may have experience with traditional forms of public technological procurement, this paper argues that the public procurement of AI is different and new insight is needed to understand this differentiation and procure AI better. This paper offers an initial contribution to the public administration and management literature by describing this difference, and identifying the challenges associated with the public procurement of AI. In order to achieve this contribution, the research studied guidelines in four European countries (Estonia, Netherlands, Serbia, and the United Kingdom) to generate insight into the challenges faced, and potential solutions to these challenges, during the public procurement of AI process.

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Overcoming the Challenges of Collaboratively Adopting Artificial Intelligence in the Public Sector

Cited by 55 publications

References 42 publications

Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence

Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence

The Analysis of Opportunities of the Application of Big Data and Artificial Intelligence Technologies in Public Governance and Social Policy

Towards a Systematic Understanding on the Challenges of Procuring Artificial Intelligence in the Public Sector

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