Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency -- A Review
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Abstract (arXiv)
The rise of advanced technology in project management (PM) highlights a crucial need for inclusiveness. This work examines the enhancement of both inclusivity and efficiency in PM through technological integration, focusing on defining and measuring inclusiveness. This approach illuminates how inclusivity-centered technology can significantly elevate project outcomes. The research navigates through the challenges of achieving inclusivity, mainly biases in learning databases and the design process of these technologies, assessment of transformative potential of these technologies, particularly in automating tasks like data collection and analysis, thus enabling managers to prioritize human-centric aspects of projects. However, the integration of such technology transcends efficiency, indicating a paradigm shift in understanding their societal roles. This shift necessitates a new approach in the development of these systems to prevent perpetuating social inequalities. We proposed a methodology involving criteria development for evaluating the inclusiveness and effectiveness of these technologies. This methodical approach is vital to comprehensively address the challenges and limitations inherent in these systems. Emphasizing the importance of inclusivity, the study advocates for a balance between technological advancement and ethical considerations, calling for a holistic understanding and regulation. In conclusion, the paper underscores that while these technologies can significantly improve outcomes, their mindful integration, ensuring inclusivity, is paramount. This exploration into the ethical and practical aspects of technology in PM contributes to a more informed and balanced approach within the field.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 18 Nov 2023.
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9 of 29 references.
References · 9
- 2022Survey of Hallucination in Natural Language Generationcited by 2,056
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- 2022On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?cited by 137
- 2023Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategiescited by 83
- 2023Large Language Models for Business Process Management: Opportunities and Challengescited by 24
- 2023Investigating the use of ChatGPT for the scheduling of construction projectscited by 17
- 2023Challenges and Solutions in AI for Allcited by 3
- 2023AutoRepo: A general framework for multi-modal LLM-based automated construction reportingcited by 3
- 2023Harnessing The Collective Wisdom: Fusion Learning Using Decision Sequences From Diverse Sourcescited by 1
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