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Wyświetlanie 1-2 z 2
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:
Fuzzy Pareto Dominance in Multiple Criteria Project Scheduling Problem
Autorzy:
Krzeszowska-Zakrzewska, Bogumiła
Powiązania:
https://bibliotekanauki.pl/articles/578536.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Ekonomiczny w Katowicach
Tematy:
Algorytmy
Harmonogram
Optimum Pareto
Optymalizacja wielokryterialna
Planowanie projektu
Zarządzanie projektem
Algorithms
Multiple criteria optimization
Pareto optimality
Project management
Project planning
Schedule
Opis:
Planning is one of the most important aspects of project management. A project plan defines objectives, activities and timeframe for project realization. To be able to define the required timeframe for project realization it is important to prepare its schedule. The purpose of this paper is to present the project scheduling problem as a multiple criteria decision making problem and to solve it using two evolutionary algorithms: SPEA2 and an evolutionary algorithm driven by the fuzzification of Pareto dominance. A comparison of these two approaches is conducted to investigate if it is reasonable to use the fuzzification of the Pareto dominance relation in evolutionary algorithms for the multiple criteria project scheduling problem
Źródło:
Multiple Criteria Decision Making; 2015, 10; 93-104
2084-1531
Pojawia się w:
Multiple Criteria Decision Making
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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