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Wyświetlanie 1-3 z 3
Tytuł:
Stakeholder-accountability model for artificial intelligence projects
Autorzy:
Miller, Glorja J.
Powiązania:
https://bibliotekanauki.pl/articles/2163225.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Ekonomiczny w Katowicach
Tematy:
accountability
artificial intelligence
algorithms
project management
ethics
Opis:
Aim/purpose – This research presents a conceptual stakeholder accountability model for mapping the project actors to the conduct for which they should be held accountable in artificial intelligence (AI) projects. AI projects differ from other projects in important ways, including in their capacity to inflict harm and impact human and civil rights on a global scale. The in-project decisions are high stakes, and it is critical who decides the system’s features. Even well-designed AI systems can be deployed in ways that harm individuals, local communities, and society. Design/methodology/approach – The present study uses a systematic literature review, accountability theory, and AI success factors to elaborate on the relationships between AI project actors and stakeholders. The literature review follows the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement process. Bovens’ accountability model and AI success factors are employed as a basis for the coding framework in the thematic analysis. The study uses a web-based survey to collect data from respondents in the United States and Germany employing statistical analysis to assess public opinion on AI fairness, sustainability, and accountability. Findings – The AI stakeholder accountability model specifies the complex relationships between 16 actors and 22 stakeholder forums using 78 AI success factors to define the conduct and the obligations and consequences that characterize those relationships. The survey analysis suggests that more than 80% of the public thinks AI development should be fair and sustainable, and it sees the government and development organizations as most accountable in this regard. There are some differences between the United States and Germany regarding fairness, sustainability, and accountability. Research implications/limitations – The results should benefit project managers and project sponsors in stakeholder identification and resource assignment. The definitions offer policy advisors insights for updating AI governance practices. The model presented here is conceptual and has not been validated using real-world projects. Originality/value/contribution – The study adds context-specific information on AI to the project management literature. It defines project actors as moral agents and provides a model for mapping the accountability of project actors to stakeholder expectations and system impacts.
Źródło:
Journal of Economics and Management; 2022, 44; 446-494
1732-1948
Pojawia się w:
Journal of Economics and Management
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Algorithmic Human Resources Management – Perspectives and Challenges
Autorzy:
Sienkiewicz, Łukasz
Powiązania:
https://bibliotekanauki.pl/articles/2166151.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Tematy:
human resources management
HR analytics
algorithms
HRM ethics
Opis:
Theoretical background: Technology – most notably processes of digitalisation, the use of artificial in telligence, machine learning, big data and prevalence of remote work due to pandemic – changes the way organizations manage human resources. One of the increasing trends is the use of so-called “algorithmic management”. It is notably different than previous e-HRM or HRIS (human resources information systems) applications, as it automates HR-related duties. Algorithms, being autonomous computational formulae, are considered objective and mathematically correct decision-making mechanisms. Limiting human in volvement and oversight of the labour process might lead to serious ethical and managerial challenges. Many areas – previously being sole responsibility of managers (including HR managers), like employment relations, hiring, performance management, remuneration – are increasingly affected, or even taken over, by algorithmic management.Purpose of the article: The purpose of this article is to review the development, perspectives and challenges (including possible biases and ethical considerations) of algorithmic human resources management. This novel approach is fuelled by the speeding processes of digitalisation, the use of artificial intelligence, big data and increased analytical capabilities and applications used by contemporary companies. Algorithms are formulas that autonomously make decisions based on statistical models or decision rules without human intervention. Therefore, the use of algorithmic HRM automates decision-making processes and duties of human resources managers, thereby limiting human involvement and oversight, which can have negative consequences for the organization.Research methods: The article provides a critical literature review of theoretical sources and empirical evidence on the application of algorithmic human resources management practices. Scientific journals in the field of human resources management and technology applications have been reviewed, as well as research reports from academic institutions and renowned international organizations.Main findings: Applications of algorithmic human resources management are an emerging field of study that is currently not extensively researched. Little is known about the scale of use as well as consequences of this more automated approach to manage human work. Scarce evidence suggests possible negative con sequences, including ethical concerns, biases leading to discriminatory decisions and adverse employees’ reactions to decisions based on algorithms. After the review of possible future developments and challenges connected to algorithmic HRM, this article proposed actions aimed at re-humanisation of the approach to managerial decision-making with the support of algorithms, ensuring transparency of the algorithms construction and functionalities, and increasing reliability and reduction of possible biases.
Źródło:
Annales Universitatis Mariae Curie-Skłodowska, sectio H – Oeconomia; 2021, 55, 2; 95-105
0459-9586
2449-8513
Pojawia się w:
Annales Universitatis Mariae Curie-Skłodowska, sectio H – Oeconomia
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimization of daily operations in the marine industry using ant colony optimization (ACO)-An artificial intelligence (AI) approach
Autorzy:
Sardar, A.
Anantharaman, M.
Garaniya, V.
Khan, F.
Powiązania:
https://bibliotekanauki.pl/articles/24201433.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
ant colony optimization
artificial intelligence
maritime transport
International Maritime Organization
international safety management
formal safety assessment
algorithms
Opis:
The maritime industry plays a crucial role in the global economy, with roughly 90% of world trade being conducted through the use of merchant ships and more than a million seafarers. Despite recent efforts to improve reliability and ship structure, the heavy dependence on human performance has led to a high number of casualties in the industry. Decision errors are the primary cause of maritime accidents, with factors such as lack of situational awareness and attention deficit contributing to these errors. To address this issue, the study proposes an Ant Colony Optimization (ACO) based algorithm to design and validate a verified set of instructions for performing each daily operational task in a standardised manner. This AI-based approach can optimise the path for complex tasks, provide clear and sequential instructions, improve efficiency, and reduce the likelihood of human error by minimising personal preference and false assumptions. The proposed solution can be transformed into a globally accessible, standardised instructions manual, which can significantly contribute to minimising human error during daily operational tasks on ships.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2023, 17, 2; 290--295
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
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