The Evolution of AI on the Commercial Flight Deck: Finding Balance between Efficiency and Safety While Maintaining the Integrity of Operator Trust
Abstract
As artificial intelligence (AI) seeks to improve modern society, the commercial aviation industry offers a significant opportunity. Although many parts of commercial aviation including maintenance, the ramp, and air traffic control show promise to integrate AI, the highly computerized digital flight deck (DFD) could be challenging. The researchers seek to understand what role AI could provide going forward by assessing AI evolution on the commercial flight deck over the past 50 years. A modified SHELL diagram is used to complete a Human Factors (HF) analysis of the early use for AI on the commercial flight deck through introduction of the Ground Proximity Warning System (GPWS), followed by the Enhanced GPWS (EGPWS) used currently, to demonstrate a form of Trustworthy AI (TAI). The recent Boeing 737 MAX 8 accidents are analyzed using an updated SHELL analysis that illustrates increased computer automation and information on the contemporary DFD. The 737 MAX 8 accidents and the role of the MCAS AI system are scrutinized to reveal the extent to which AI can fail and create distrust among end-users. Both analyses project what must be done to implement and integrate TAI effectively in a contemporary DFD design. The ergonomic evolution of AI on the commercial flight deck illustrates how it has helped achieve industry safety gains. Through gradual integration, the quest for pilot trust has been challenged when attempting to balance efficiency and safety in commercial flight. Preliminary data from a national survey of company pilots indicates that trust in AI is regarded positively in general, although less so when applied to personal involvement. Implications for DFD design incorporating more advanced AI are considered further within the realm of trust and reliability.
Keywords: Artificial Intelligence – Digital Flight Deck – Trust
DOI: 10.54941/ahfe1004175
Cite this paper
More from this volume
- CHAAIS: Climate-focused Human-machine teaming and Assurance in Artificial Intelligence Systems – Framework applied toward wildfire management case study
- TAUCHI-GPT: Leveraging GPT-4 to create a Multimodal Open-Source Research AI tool
- A Survey of Beliefs and Attitudes toward Artificial Intelligence — Practical Implications and Fictional Depictions
- Exploring the Impact of Generative Artificial Intelligence on the Design Process: Opportunities, Challenges, and Insights
- Relationships among Personality Traits, ChatGPT Usage and Concept Generation in Innovation Design
- Leveraging Multi-User Dungeons for Ethical AI Decision Support Systems: A Novel Approach
- Measuring the Impact of Picture-Based Explanations on the Acceptance of an AI System for Classifying Laundry
- Automated generation of synthetic person activity data for AI models training
- Human-Animal Teaming as a Model for Human-AI-Robot Teaming: Advantages and Challenges
- A method to generate adversarial examples based on color variety of adjacent pixels
- Integrating Domain Expertise and Artificial Intelligence for Effective Supply Chain Management Planning Tasks: A Collaborative Approach
- User Trust Towards an AI-assisted Healthcare Decision Support System under Varied Explanation Formats and Expert Opinions


AHFE Open Access