AI management beyond the hype: exploring the co-constitution of AI and organizational context

Holmström, Jonny; Hällgren, Markus

doi:10.1007/s00146-021-01249-2

Cited by 16 publications

(5 citation statements)

References 61 publications

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“…, 2019). The result may be resistance to change (Holmström and Hällgren, 2021). Resistance is likely when the ideation stage has neglected engagement with those directly or indirectly affected by the innovation.…”

Section: Ai Action Framework – Applying Findings To Develop a Model F...mentioning

confidence: 99%

“…These measures often signal the redefining of job roles and uncertain, possibly inequitable impacts on individuals (Fountaine et al, 2019). The result may be resistance to change (Holmstr€ om and H€ allgren, 2021). Resistance is likely when the ideation stage has neglected engagement with those directly or indirectly affected by the innovation.…”

Section: Development (Outcome Training)mentioning

confidence: 99%

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Preparing to work with artificial intelligence: assessing WHS when using AI in the workplace

Cebulla¹,

Szpak²,

Knight³

2023

IJWHM

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PurposeArtificial Intelligence (AI) systems play an increasing role in organisation management, process and product development. This study identifies risks and hazards that AI systems may pose to the work health and safety (WHS) of those engaging with or exposed to them. A conceptual framework of organisational measures for minimising those risks is proposed.Design/methodology/approachAdopting an exploratory, inductive qualitative approach, the researchers interviewed 30 experts in data science, technology and WHS; 12 representatives of nine organisations using or preparing to use AI; and ran online workshops, including with 12 WHS inspectors. The research mapped AI ethics principles endorsed by the Australian government onto the AI Canvas, a tool for tracking AI implementation from ideation via development to operation. Fieldwork and analysis developed a matrix of WHS and organisational–managerial risks and risk minimisation strategies relating to AI use at each implementation stage.FindingsThe study identified psychosocial, work stress and workplace relational risks that organisations and employees face during AI implementation in a workplace. Privacy, business continuity and gaming risks were also noted. All may persist and reoccur during the lifetime of an AI system. Alertness to such risks may be enhanced by adopting a systematic risk assessment approach.Originality/valueA collaborative project involving sociologists, economists and computer scientists, the study relates abstract AI ethics principles to concrete WHS risks and hazards. The study translates principles typically applied at the societal level to workplaces and proposes a process for assessing AI system risks.

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Section: Ai Action Framework – Applying Findings To Develop a Model F...mentioning

confidence: 99%

Section: Development (Outcome Training)mentioning

confidence: 99%

Preparing to work with artificial intelligence: assessing WHS when using AI in the workplace

Cebulla¹,

Szpak²,

Knight³

2023

IJWHM

View full text Add to dashboard Cite

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“…Enhancing human expertise: AI can improve the ability of leaders by providing them with real-time data insights, encourage social contacts among people, and connect them with robots and other machines that cooperate with people. These robots or machines can work alongside humans to increase work efficiency and precision [3]. 4.…”

Section: Advantages Of Ai In Leadershipmentioning

confidence: 99%

“…Furthermore, the allocation of decision rights between AI and leaders is unclear. In other words, there is a difficult trade-off between when leaders should delegate decision-making power to AI tools and how much decision-making power leaders are prepared to delegate to AI tools [3]. Additionally, businesses that rely too much on AI systems also face the risk of collapse.…”

Section: The Risks and Limitations Of Ai In Leadershipmentioning

confidence: 99%

AI and Leadership

Xiong¹

2022

Proceedings of the 2022 7th International Conference on Modern Management and Education Technology (MMET 2022)

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Leaders are important for developing and implementing new strategic plans, as well as for interacting with and motivating employees to improve their commitment to the company's goals. Maintaining leadership skill development is critical to keeping on the right path. Enlightened corporate leaders recognize the problems of employees and put new progress of leadership theory into practice to create a more successful working environment. Meanwhile, artificial intelligence (AI), as a new technology, allows machines to perform difficult tasks that ordinarily need human intelligence. AI will increase the productivity of leaders by taking over some automated, mechanical, and administrative activities. It is difficult for artificial intelligence to replace human care, human thinking, and human interaction with human factors. Future leaders may need to adjust their attention to place more emphasis on these elements.

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“…A significant number of recent studies have countered this AI hype in fields such as translational medicine (Toh et al, 2019), multidisciplinary medical teams (Di Ieva, 2019), radiology (Rockall, 2020), COVID-19 (Abdulkareem and Petersen, 2021), machine vision (Marquardt, 2020), management (Holmström and Hällgren, 2021), and interaction design (Liikkanen, 2019). The advances of the last decade have indeed been significant, but AI is only capable of performing well on narrow tasks for which they can be trained over an extended period with a significant amount of relevant data (and significant number of humans working on labeling this data).…”

Section: Ai Is Not That Special (Socio-technical Critique)mentioning

confidence: 99%

Politics by Automatic Means? A Critique of Artificial Intelligence Ethics at Work

Cole

Cant²,

Spilda³

et al. 2022

Front. Artif. Intell.

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Calls for “ethical Artificial Intelligence” are legion, with a recent proliferation of government and industry guidelines attempting to establish ethical rules and boundaries for this new technology. With few exceptions, they interpret Artificial Intelligence (AI) ethics narrowly in a liberal political framework of privacy concerns, transparency, governance and non-discrimination. One of the main hurdles to establishing “ethical AI” remains how to operationalize high-level principles such that they translate to technology design, development and use in the labor process. This is because organizations can end up interpreting ethics in an ad-hoc way with no oversight, treating ethics as simply another technological problem with technological solutions, and regulations have been largely detached from the issues AI presents for workers. There is a distinct lack of supra-national standards for fair, decent, or just AI in contexts where people depend on and work in tandem with it. Topics such as discrimination and bias in job allocation, surveillance and control in the labor process, and quantification of work have received significant attention, yet questions around AI and job quality and working conditions have not. This has left workers exposed to potential risks and harms of AI. In this paper, we provide a critique of relevant academic literature and policies related to AI ethics. We then identify a set of principles that could facilitate fairer working conditions with AI. As part of a broader research initiative with the Global Partnership on Artificial Intelligence, we propose a set of accountability mechanisms to ensure AI systems foster fairer working conditions. Such processes are aimed at reshaping the social impact of technology from the point of inception to set a research agenda for the future. As such, the key contribution of the paper is how to bridge from abstract ethical principles to operationalizable processes in the vast field of AI and new technology at work.

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AI management beyond the hype: exploring the co-constitution of AI and organizational context

Cited by 16 publications

References 61 publications

Preparing to work with artificial intelligence: assessing WHS when using AI in the workplace

Preparing to work with artificial intelligence: assessing WHS when using AI in the workplace

AI and Leadership

Politics by Automatic Means? A Critique of Artificial Intelligence Ethics at Work

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