Human Error in High-Risk Industries: What We (Don’t) Understand so Far

By Florin Necula, PhD | Maintenance Manager & Licensed Aircraft Engineer
Nov 11th, 2025

Abstract

Human error remains one of the most debated and misunderstood phenomena in high-risk industries such as aviation, nuclear power and healthcare. Despite decades of investigation, incident rates suggest that we are still far from mastering the prevention or prediction of human error. This article examines the evolution of our understanding of human error from mechanistic models and linear causality to dynamic, multidisciplinary perspectives that embrace complexity and resilience. Drawing from psychology, neuroscience, physiology and anthropology, this article provides a critical narrative of what we know—and what remains elusive—about human fallibility in complex socio-technical systems.

1. What Is Human Error?

Human error can be broadly defined as a deviation from expected or intended behaviour that may result in an undesired outcome. However, this definition oversimplifies the phenomenon. According to Reason (1990), human errors can be classified into slips, lapses, mistakes and violations, each with distinct cognitive origins. These categories distinguish between errors in execution (slips/lapses) and planning (mistakes), as well as intentional breaches of procedure (violations).

Importantly, many errors are not due to negligence but occur despite best intentions and competence. Dekker (2011) argues that “human error is an attribution after the fact,” meaning that it is a retrospective judgment shaped by outcome severity rather than by observable intent or process. Thus, understanding human error requires a move beyond moral judgment toward systemic inquiry.

2. Historical Evolution and Scientific Development

2.1 Taylorism and Mechanistic Views (1911)

In the early 20th century, Frederick Winslow Taylor’s Scientific Management (1911) laid the foundation for industrial efficiency, positing that work could be optimised through standardisation and task decomposition. Taylor viewed workers as components of a machine, where deviation (i.e., error) reflected inefficiency or non-compliance. While Taylorism advanced productivity, it ignored cognitive variability, emotion and system complexity—factors central to modern error theory.

2.2 Heinrich’s Domino Theory and Blame Culture (1931)

Herbert W. Heinrich’s Industrial Accident Prevention (1931) introduced the “domino theory,” suggesting that 88% of accidents were due to unsafe acts by individuals. Errors were thus framed as personal failings. His view promoted disciplinary approaches—training, supervision or termination—to manage safety.

Though influential, this model fostered a blame culture, ignoring system design, leadership and organizational pressures (Reason, 1997). It also introduced the problematic concept of the “error-prone individual”, a myth still embedded in some safety discourses today (Dekker, 2011).

2.3 Behaviourism and Operant Conditioning (1940s–1960s)

Emerging from psychology, behaviourism, led by B.F. Skinner (1953), framed human error as a product of maladaptive behaviours that could be shaped by reinforcement or punishment. This theory influenced military training programmes, checklists and repetitive drills during and after WWII.

While behaviourism contributed valuable tools for compliance and procedural discipline, it neglected internal mental states, emotion and cognition—elements crucial for understanding error in high-tempo, ambiguous environments.

2.4 The Rise of Human Factors (1950s–1970s)

The field of Human Factors gained prominence during the World War II, particularly in aviation, space and nuclear power. Post-war accidents exposed the limitations of training alone and redirected attention to system interfaces, workload and cognitive performance.

Human Factors introduced the idea that errors are often by-products of design flaws. Notably, investigations of the Three Mile Island accident (1979) emphasized poor HMI (human-machine interface) design and information overload, revealing how latent system issues can lead to frontline errors (Rasmussen, 1983).

2.5 Reason’s Swiss Cheese Model (1990)

James Reason’s Human Error (1990) formalized a systemic model of accidents. His Swiss Cheese Model visualised safety barriers as imperfect, each with potential “holes.” When these holes align, incidents occur.

Reason distinguished between:

• Active failures (operator errors)
• Latent conditions (organizational/systemic weaknesses)

This model transformed safety investigation from blaming individuals to examining how organisational culture, processes and leadership create environments prone to failure.

2.6 High Reliability Organisations (1980s–1990s)

During the same period, researchers examined how certain sectors—such as air traffic control and nuclear aircraft carriers—achieved near-error-free performance. These High Reliability Organizations (HROs) succeeded not by eliminating error, but by anticipating and managing it (Weick & Sutcliffe, 1999).

Key HRO traits include:

• Reluctance to simplify interpretations
• Preoccupation with failure
• Sensitivity to operations
• Commitment to resilience
• Deference to expertise

This mindset marked a transition from control-based safety to adaptive safety—embracing complexity and human adaptability.

2.7 Safety Culture and Just Culture (1990s–Present)

High-profile disasters—Chernobyl (1986), Challenger (1986) and Columbia (2003)—highlighted that technical causes were often secondary to organisational silence, groupthink and suppression of dissent.

This led to the rise of Safety Culture and Just Culture models. Reason (1997) defined a Just Culture as one that encourages reporting and learning while maintaining accountability for recklessness.

In aviation, these principles are embedded in ICAO Annex 19, EASA OPS, Part CAMO and Part 145 requirements. Cultures that punish all error deter reporting, while cultures that ignore accountability breed complacency.

2.8 Resilience Engineering and Adaptive Capacity (2000s–Present)

In recent decades, Resilience Engineering has emerged, led by Hollnagel, Woods, and Dekker. This paradigm emphasizes how systems succeed, rather than only why they fail (Hollnagel et al., 2006).

Humans are seen as sources of flexibility, not failure. Resilient systems monitor for weak signals, adapt in real time and recover from unexpected disturbances.

Human error is no longer viewed solely as deviation, but as part of normal performance variability in complex, tightly coupled environments.

3. The Multidisciplinary Nature of Human Error

Understanding human error today demands an interdisciplinary lens. No single domain can explain why competent individuals make mistakes in tightly controlled systems.

• Psychology studies attention, memory and bias (Dehais et al., 2014).
• Neuroscience examines how stress impairs cognitive control and decision-making (Franca & Hollnagel, 2022).
• Physiology shows how fatigue and circadian disruption raise error rates, particularly in shift-based work (Ice & James, 2006).
• Anthropology and Sociology analyse cultural norms, authority gradients and power structures that shape behaviour and reporting (Choudhury, 2010).

Together, these disciplines form a meta-framework for understanding not only how errors occur, but why, when and under what conditions.

4. Can We Eliminate Human Error?

The prevailing consensus is no—human error is inevitable, but its consequences are manageable.

Automation has reduced some forms of error but introduced new ones—automation complacency, mode confusion and monitoring failures (Brown, 2016). In aviation, pilots have been known to miss critical alerts or misinterpret system states due to automation opacity.

Therefore, safety efforts must focus on designing for error tolerance, including:

• Human-centred interfaces
• Training for adaptive decision-making
• Organizational learning systems

As Woods and Hollnagel (2006) argue, human variability is both the source of failure and the foundation of resilience.

5. The Limits of Human Understanding

Humans are not ideal information processors. We are limited by:

• Working memory (7±2 items; Miller, 1956)
• Cognitive biases (confirmation bias, availability heuristic)
• Bounded rationality (Simon, 1956)

Under stress, cortisol impairs hippocampal function, reducing memory recall and increasing reliance on intuition (Karwowski, 2012). These neurobiological realities mean that even well-trained professionals will err under pressure.

The role of systems must be to support human cognition, not to punish its limitations.

6. What We Don’t Know (Yet)

Despite decades of study, many questions remain:

• What are the neural correlates of high-stakes decision-making under ambiguity?
• How does chronic fatigue alter team safety performance?
• How do cultural values shape risk perception and error reporting?
• What is the true cost of errors that were prevented but not recorded?

Future research must be cross-cultural, interdisciplinary and longitudinal, incorporating neurophysiology, organisational behaviour and systems theory.

7. Conclusion: Toward Human-Centred Safety Systems

Human error is not a character flaw. It is a signal—of complexity, adaptation and system boundaries.

The challenge for leaders, engineers and investigators is not to ask “Who failed?” but “What conditions made failure likely?” and “How can we build systems that fail safely?”

High-risk industries must shift from error suppression to error anticipation, resilience and learning. This shift demands humility: to accept that humans are fallible and that systems must be built to absorb, not reject, that fallibility.

References

Brown, J.P. (2016). The Effect of Automation on Human Factors in Aviation. The Journal of Instrumentation and Human Factors.

Choudhury, S. (2010). Culturing the adolescent brain. Social Cognitive and Affective Neuroscience, 5(2–3), 159–167.

Dehais, F., et al. (2014). Inattentional deafness in the cockpit. Human Factors, 56(4), 631–644.

Dekker, S. (2011). Drift into Failure. Ashgate.

Franca, J., & Hollnagel, E. (2022). Neuroscience and Risk Management in Sociotechnical Systems. ResearchGate.

Heinrich, H.W. (1931). Industrial Accident Prevention: A Scientific Approach. McGraw-Hill.

Hollnagel, E., Woods, D.D., & Leveson, N. (2006). Resilience Engineering. CRC Press.

Ice, G.H., & James, G.D. (2006). Measuring Stress in Humans: A Practical Guide for the Field. Cambridge University Press.

Karwowski, W. (2012). Challenges of human performance in complex systems. Human Factors, 54(6), 983–1005.

Miller, G.A. (1956). The magical number seven, plus or minus two. Psychological Review, 63(2), 81–97.

Reason, J. (1990). Human Error. Cambridge University Press.

Reason, J. (1997). Managing the Risks of Organizational Accidents. Ashgate.

Simon, H.A. (1956). Rational choice and the structure of the environment. Psychological Review, 63(2), 129–138.

Taylor, F.W. (1911). The Principles of Scientific Management. Harper & Brothers.

Weick, K.E., & Sutcliffe, K.M. (1999). Managing the Unexpected. Jossey-Bass.