mlsanityData to 7 Oct 2026

Trustworthy AI and Robotics and the Implications for the AEC Industry: A Systematic Literature Review and Future Potentials

arXiv 2109.13373 ↗First posted 27 Sep 2021cs.HC cs.AI cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Human-technology interaction deals with trust as an inevitable requirement for user acceptance. As the applications of artificial intelligence (AI) and robotics emerge and with their ever-growing socio-economic influence in various fields of research and practice, there is an imminent need to study trust in such systems. With the opaque work mechanism of AI-based systems and the prospect of intelligent robots as workers' companions, context-specific interdisciplinary studies on trust are key in increasing their adoption. Through a thorough systematic literature review on (1) trust in AI and robotics (AIR) and (2) AIR applications in the architecture, engineering, and construction (AEC) industry, this study identifies common trust dimensions in the literature and uses them to organize the paper. Furthermore, the connections of the identified dimensions to the existing and potential AEC applications are determined and discussed. Finally, major future directions on trustworthy AI and robotics in AEC research and practice are outlined.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 203 references that are in the map. First posted on arXiv 27 Sep 2021.

Builds on

19 of 203 references.

References · 19

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  2. 2013Intriguing properties of neural networkscited by 5,114
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  4. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  5. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  6. 2018Robustness May Be at Odds with Accuracycited by 873
  7. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 411
  8. 2017Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Modelscited by 202
  9. 2020The Next Decade in AI: Four Steps Towards Robust Artificial Intelligencecited by 194
  10. 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 182
  11. 2018The Frontiers of Fairness in Machine Learningcited by 139
  12. 2017Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciencescited by 84
7 more
  1. 2017What Does Explainable AI Really Mean? A New Conceptualization of Perspectivescited by 72
  2. 2020Trustworthy AIcited by 62
  3. 2017Efficient Defenses Against Adversarial Attackscited by 53
  4. 2017Explainable Planningcited by 40
  5. 2021Explainable Artificial Intelligence (XAI): An Engineering Perspectivecited by 6
  6. 2019AI-based Prediction of Independent Construction Safety Outcomes from Universal Attributescited by 5
  7. 2020Trust Considerations for Explainable Robots: A Human Factors Perspectivecited by 4

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 2.

4 papers.

  1. 2024Establishing and Evaluating Trustworthy AI: Overview and Research Challengescited by 12
  2. 2024Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approachcited by 3
  3. 2023Assessing Trust in Construction AI-Powered Collaborative Robots using Structural Equation Modelingcited by 0
  4. 2023Ethics of Artificial Intelligence and Robotics in the Architecture, Engineering, and Construction Industrycited by 0