AI in Wonderland: Engineering in the Age of Overpromised Technology

This report explores the move from traditional industry practices to emerging technologies, specifically the integration of artificial intelligence (AI) solutions in engineering service sectors. It highlights the increasing problem of “technology washing,” when organizations overstate (sometimes deceivingly) their technology abilities and ethics, posing challenges to accountability, transparency, and trust in various fields. The rise of AI-based solutions in sectors like autonomous mobility, manufacturing, and aerospace has exposed a contrast between ambitious future aspirations and current technological barriers. With this, the role of human knowledge in guaranteeing ethical, efficient, and clear technology incorporation becomes essential.Starting with an examination of today’s technological scene, this report tackles topics such as the buzz around autonomous systems and the difficulties of standardizing fresh innovations. It also points out the problem of organizations exaggerating the capabilities of AI, stressing the importance of human monitoring to manage operational risks and uphold public trust. Practical scenarios in autonomous mobility, aerospace, and manufacturing highlight a significant discrepancy between industry targets and technological feasibilities, stressing the indispensable contribution of human intervention in ensuring successful implementation.These examples are analyzed to give insights into current technology successes and limitations and to propose a balanced path for the future. Ultimately, there may be a future where groundbreaking technological advancements remain in harmony with human values. This report challenges established narratives and outlines a path for ethical technological advancement that is transparent and in line with societal values.

• Record URL:
  • https://doi.org/10.4271/EPR2025002
• Supplemental Notes:
  • Abstract reprinted with permission of SAE International.
• Authors:
  • Khan, Samir
• Publication Date: 2025-1-14

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • SAE EDGE™ Research Reports
  • Publisher: SAE International
  • Serial URL: https://www.sae.org/publications/edge-research-reports

Subject/Index Terms

• TRT Terms: Artificial intelligence; Autonomous vehicles; Technological forecasting; Technological innovations
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01942897
• Record Type: Publication
• Source Agency: SAE International
• Report/Paper Numbers: EPR2025002
• Files: TRIS, SAE
• Created Date: Jan 21 2025 11:20AM