Ethics of Artificial Intelligence and Robotics in the Architecture, Engineering, and Construction Industry

• URL: http://arxiv.org/abs/2310.05414v1
• Date: Mon, 9 Oct 2023 05:17:14 GMT
• Title: Ethics of Artificial Intelligence and Robotics in the Architecture, Engineering, and Construction Industry
• Authors: Ci-Jyun Liang, Thai-Hoa Le, Youngjib Ham, Bharadwaj R. K. Mantha, Marvin H. Cheng, Jacob J. Lin
• Abstract summary: This study systematically reviews AI and robotics research through the lens of ethics in the architecture, engineering, and construction (AEC) community. It identifies nine key ethical issues namely job loss, data privacy, data security, data transparency, decision-making conflict, acceptance and trust, reliability and safety, fear of surveillance, and liability.
• Score: 1.1650821883155187
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Artificial intelligence (AI) and robotics research and implementation emerged in the architecture, engineering, and construction (AEC) industry to positively impact project efficiency and effectiveness concerns such as safety, productivity, and quality. This shift, however, warrants the need for ethical considerations of AI and robotics adoption due to its potential negative impacts on aspects such as job security, safety, and privacy. Nevertheless, this did not receive sufficient attention, particularly within the academic community. This research systematically reviews AI and robotics research through the lens of ethics in the AEC community for the past five years. It identifies nine key ethical issues namely job loss, data privacy, data security, data transparency, decision-making conflict, acceptance and trust, reliability and safety, fear of surveillance, and liability, by summarizing existing literature and filtering it further based on its AEC relevance. Furthermore, thirteen research topics along the process were identified based on existing AEC studies that had direct relevance to the theme of ethics in general and their parallels are further discussed. Finally, the current challenges and knowledge gaps are discussed and seven specific future research directions are recommended. This study not only signifies more stakeholder awareness of this important topic but also provides imminent steps towards safer and more efficient realization.

Related papers

• Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
  Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks. In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
  arXiv  Detail & Related papers  (2025-01-09T03:59:10Z)
• Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems [45.31340537171788]
  Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. Despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited.
  arXiv  Detail & Related papers  (2024-05-28T20:54:41Z)
• Towards an Ethical and Inclusive Implementation of Artificial Intelligence in Organizations: A Multidimensional Framework [0.0]
  This article analyzes the impact of artificial intelligence on contemporary society and the importance of adopting an ethical approach to its development and implementation within organizations. Various actors, such as governments, academics, and civil society, can play a role in shaping the development of AI aligned with human and social values.
  arXiv  Detail & Related papers  (2024-05-02T19:43:51Z)
• The Global Impact of AI-Artificial Intelligence: Recent Advances and Future Directions, A Review [0.0]
  The article highlights the implications of AI, including its impact on economic, ethical, social, security & privacy, and job displacement aspects. It discusses the ethical concerns surrounding AI development, including issues of bias, security, and privacy violations. The article concludes by emphasizing the importance of public engagement and education to promote awareness and understanding of AI's impact on society at large.
  arXiv  Detail & Related papers  (2023-12-22T00:41:21Z)
• Investigating Responsible AI for Scientific Research: An Empirical Study [4.597781832707524]
  The push for Responsible AI (RAI) in such institutions underscores the increasing emphasis on integrating ethical considerations within AI design and development. This paper aims to assess the awareness and preparedness regarding the ethical risks inherent in AI design and development. Our results have revealed certain knowledge gaps concerning ethical, responsible, and inclusive AI, with limitations in awareness of the available AI ethics frameworks.
  arXiv  Detail & Related papers  (2023-12-15T06:40:27Z)
• Survey on AI Ethics: A Socio-technical Perspective [0.9374652839580183]
  Ethical concerns associated with AI are multifaceted, including challenging issues of fairness, privacy and data protection, responsibility and accountability, safety and robustness, transparency and explainability, and environmental impact. This work unifies the current and future ethical concerns of deploying AI into society.
  arXiv  Detail & Related papers  (2023-11-28T21:00:56Z)
• A Review of the Ethics of Artificial Intelligence and its Applications in the United States [0.0]
  The paper highlights the impact AI has in every sector of the US economy and the resultant effect on entities spanning businesses, government, academia, and civil society. Our discussion explores eleven fundamental 'ethical principles' structured as overarching themes. These encompass Transparency, Justice, Fairness, Equity, Non- Maleficence, Responsibility, Accountability, Privacy, Beneficence, Freedom, Autonomy, Trust, Dignity, Sustainability, and Solidarity.
  arXiv  Detail & Related papers  (2023-10-09T14:29:00Z)
• EALM: Introducing Multidimensional Ethical Alignment in Conversational Information Retrieval [43.72331337131317]
  We introduce a workflow that integrates ethical alignment with an initial ethical judgment stage for efficient data screening. We present the QA-ETHICS dataset adapted from the ETHICS benchmark, which serves as an evaluation tool by unifying scenarios and label meanings. In addition, we suggest a new approach that achieves top performance in both binary and multi-label ethical judgment tasks.
  arXiv  Detail & Related papers  (2023-10-02T08:22:34Z)
• Artificial Intelligence/Operations Research Workshop 2 Report Out [38.11462021949535]
  This Report Out focuses on the foundational elements of trustworthy AI and OR technology, and how to ensure all AI and OR systems implement these elements in their system designs. Four sessions on various topics within Trustworthy AI were held, these being Fairness, Explainable AI/Causality, Robustness/Privacy, and Human Alignment and Human-Computer Interaction. Following discussions of each of these topics, workshop participants also brainstormed challenge problems which require the collaboration of AI and OR researchers and will result in the integration of basic techniques from both fields to eventually benefit societal needs.
  arXiv  Detail & Related papers  (2023-04-10T15:51:39Z)
• The Road to a Successful HRI: AI, Trust and ethicS-TRAITS [64.77385130665128]
  The aim of this workshop is to foster the exchange of insights on past and ongoing research towards effective and long-lasting collaborations between humans and robots. We particularly focus on AI techniques required to implement autonomous and proactive interactions.
  arXiv  Detail & Related papers  (2022-06-07T11:12:45Z)
• Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
  Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research. An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system. We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
  arXiv  Detail & Related papers  (2022-04-11T14:36:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.