Artificial intelligence (AI) is rapidly being adopted across environmental health and safety (EHS) programs. In this context, AI refers to systems that perform one or more cognitive functions: perceiving conditions (such as computer vision and sensor systems), recognizing patterns, predicting outcomes, generating language, or recommending actions. From camera systems that flag unsafe behaviors to algorithms that forecast injury rates and generative tools that summarize regulations, AI is often marketed as a neutral, tireless safety professional that never looks away.

In practice, however, AI systems inherit the assumptions, blind spots, and data limitations of the humans who design and deploy them. When these systems fail, the consequences are not abstract. They are physical injuries, missed hazards, flawed compliance decisions, and false assurances of safety.

A widely reported example occurred in November 2023 at an agricultural distribution center in South Korea, where a worker inspecting a sensor on a robotic lifting system was fatally crushed when the machine’s vision software failed to distinguish him from the boxes it was programmed to handle. This was not a simple mechanical malfunction. The sensors worked and the code executed, but the system lacked the contextual understanding to differentiate between “product” and “person.” 1

This incident highlights a critical vulnerability in modern automation: algorithms do not see the world; they classify patterns based on prior training. When classification fails, the outcome can be catastrophic.

The Illusion of Objectivity in AI Safety Systems

One of the most persistent myths surrounding AI in safety is that it is objective. Algorithms do not evaluate hazards the way an experienced industrial hygienist or safety professional does. They identify statistical patterns in historical data. If that data is incomplete, biased, or unrepresentative of real-world conditions, the system will confidently produce incorrect conclusions.

AI-based camera systems designed to detect missing personal protective equipment (PPE) or unsafe proximity to equipment may perform well in controlled environments. However, glare, dust, shadows, unconventional PPE, or atypical body positioning can cause both missed detections and false alarms. When alerts are frequently incorrect, workers develop automation-driven alert fatigue – a new risk pathway created not by human complacency, but by technological overconfidence.

AI does not understand context. A worker entering a restricted area during routine operations may represent unsafe behavior. The same action during an emergency repair may be necessary and lifesaving. Algorithms enforce rules based on pattern recognition, not situational judgment.

Predictive Analytics and Reporting Bias

Predictive safety platforms claim to forecast injuries by analyzing incident reports, near misses, and behavioral observations. In theory, this allows earlier intervention. In practice, these systems often amplify existing reporting biases.

If near misses are underreported or minor incidents are discouraged from documentation, the model interprets the absence of data as the absence of risk. High-risk tasks may appear statistically “safe” simply because events were normalized and never recorded. Conversely, more visible or recently scrutinized work groups may appear disproportionately risky.

Early adopters of predictive tools have discovered that models frequently prioritize frequency over severity. Numerous minor ergonomic complaints may outweigh a single low-frequency but catastrophic process safety hazard. Human safety professionals recognize that low-probability, high-consequence risks demand disproportionate attention. Many algorithms do not.

Generative AI in Safety Practice: A New Risk Frontier

For many safety professionals, exposure to AI now occurs primarily through generative large language models (LLMs). These tools are increasingly used to summarize standards, draft safety procedures, generate training materials, and interpret regulatory requirements.

While efficient, generative AI introduces distinct failure modes, LLMs may produce confident but incorrect regulatory interpretations, fabricate citations, oversimplify complex compliance obligations, or omit critical exceptions. Because their language is fluent and authoritative in tone, errors may go undetected unless carefully reviewed by a qualified professional.

In safety and compliance contexts, an inaccurate summary of an Occupational Safety & Health Administration (OSHA) requirement or a misinterpreted exposure limit is not a minor inconvenience; it may influence policy decisions, documentation, or enforcement posture. Generative AI can assist with drafting an information synthesis, but it cannot replace regulatory expertise, professional judgment, or legal accountability.

Automation Does Not Eliminate Risk – It Redistributes It

Automation in robotics, material handling, and industrial systems has improved efficiency and reduced certain physical exposures. In narrow, repetitive, well-controlled tasks, AI systems may outperform humans in speed and consistency.

However, automation does not remove risk; it redistributes it. Mechanical risks may decrease while systemic, classification, oversight, and governance risks increase. When organizations treat AI as a replacement for hazard analysis rather than as an input to it, failures become inevitable.

AI is trained on historical data. Safety, by definition, is concerned with preventing novel and future harm. Algorithms codify past patterns; they do not anticipate unprecedented failure modes.

A Realistic Role for AI in Health and Safety

AI can be valuable when treated as an assistant rather than an authority. It can surface weak signals, identify trends across large datasets, reduce administrative burden, and support documentation efficiency.

The most effective safety programs treat AI outputs as prompts for professional evaluation, not final determinations. When an algorithm flags risk, a competent safety professional investigates. When it reports no risk, that absence is questioned rather than accepted blindly. AI should augment the foundational principles of anticipation, recognition, evaluation, and control – not override them.

Technology Does Not Eliminate Responsibility

Health and safety failures involving AI are rarely failures of technology alone. They are failures of governance, expectation management, and professional oversight. No algorithm can be held accountable during an OSHA inspection, deposition, or incident investigation. Responsibility remains with employers and safety professionals.

As AI becomes more embedded in EHS systems, the need for human expertise becomes more critical – not less. The central question is not whether AI can improve safety. It is whether organizations understand its limits before those limits result in harm.

ETI: Professional Judgment in an AI-Driven Safety Landscape

HETI’s team of certified industrial hygienists and experienced environmental health & safety professionals helps organizations critically evaluate and responsibly integrate AI-based tools into comprehensive EHS programs. Our services include assessing automated system limitations, validating AI-generated safety data through field verification, identifying gaps in hazard recognition, and ensuring that professional oversight remains central to risk management decisions.

By combining technical expertise, regulatory knowledge, and real-world observation, HETI helps clients use artificial intelligence to enhance safety performance without compromising worker protection.

 

Reference-

1 The Guardian. “Industrial robot crushes man to death in South Korean distribution center,” citing Yonhap News Agency, November 8, 2023. Retrieved January 20, 2026. https://www.theguardian.com/technology/2023/nov/08/south-korean-man-killed-by-industrial-robot-in-distribution-centre

To find out more about HETI’s industrial hygiene and safety services, please contact us.

Daniel Farcas, PhD, CIH, CSP, CHMM Senior Industrial Hygienist

Occupational noise exposure remains one of the most prevalent workplace health hazards in the United States. Widely cited national data indicate that approximately 25% of all U.S. workers have been exposed to hazardous noise during their working lives. Beyond its prevalence, social impacts, and clinical implications, the economic burden is significant. National surveillance data estimate that occupational hearing loss accounts for approximately $242 million each year in workers’ compensation costs related to hearing loss disability. This figure reflects only direct claim costs and does not capture broader impacts such as lost productivity, retraining, medical management, or reduced quality of life.

What makes occupational noise exposure particularly impactful is that it can result in permanent damage, even though its progression is often gradual and painless. By the time hearing loss is identified, the underlying damage has already occurred and cannot be reversed. At the same time, noise-induced hearing loss is one of the most preventable occupational illnesses when exposure is identified and addressed proactively.

Widespread Exposure, Often Unrecognized

Hazardous noise is frequently associated with obvious industrial environments, such as manufacturing floors or construction sites. In practice, excessive noise exposure occurs across a wide range of operations – such as facilities management, utilities, warehousing, laboratory support areas, mechanical rooms, and intermittent maintenance activities.

A significant challenge in managing occupational noise lies in human perception. Through sensory adaptation, constant input becomes less noticeable over time – much like how a smell fades shortly after entering a room or how conversation can be followed in a crowded space despite background noise. This adaptive response reflects how human perception developed to prioritize meaningful cues. In modern workplaces, the same sensory adaptation can cause sustained hazardous noise levels to feel routine, even as physiological damage continues to accumulate.

Human behavioral factors also contribute to the challenges associated with occupational risk management, including noise hazards. Workers may naturally discount risks that are diminished by sensory adaptation, do not produce immediate symptoms, feel temporary, or are perceived as interfering with task completion. This dynamic is common with noise exposure, where short-duration tasks are often assumed to pose minimal risk despite cumulative or high-level exposure potential. A familiar example occurs outside the workplace: the inconvenience of grabbing hearing protection for a brief activity, such as operating a weed trimmer, can outweigh concern for potential hearing loss.

Permanent Outcomes Extend Beyond Hearing Loss

Noise-induced hearing loss is characterized by typically permanent anatomical damage. However, the consequences extend beyond hearing loss documented on an audiogram.

From a safety perspective, reduced auditory acuity can impair verbal communication, diminish the ability to recognize alarms, warning signals, or approaching equipment, and reduce awareness of hazardous noise conditions themselves. In environments where situational awareness is critical, these limitations can increase the likelihood of secondary incidents and delay corrective actions when noise exposures exceed safe levels.

There are also important mental health and psychosocial considerations. Chronic noise exposure and hearing loss have been associated with increased fatigue, cognitive strain, difficulty concentrating, and social withdrawal. Multiple studies indicate that adults with moderate or more advanced hearing loss are more likely to experience mental health challenges than those without hearing impairment.

Regulatory Framework and Measurable Thresholds

Occupational noise is one of the most measurable workplace exposures, with clearly defined regulatory criteria. Under the Occupational Safety & Health Administration’s (OSHA’s) Occupational Noise Exposure Standard (29 CFR 1910.95), a hearing conservation program is required when employee noise exposures reach the action level of 85 A-weighted decibels (dBA) as an eight-hour time-weighted average (TWA). The permissible exposure limit (PEL) is 90 dBA as an eight-hour TWA.

Despite the clarity of these thresholds, many programs remain reactive – initiated only after audiometric shifts or compliance concerns are identified rather than through proactive exposure evaluation and risk control.

Prevention is More Efficient than Program Repair

Early evaluation of noise exposure often identifies straightforward opportunities for risk reduction – including targeted engineering controls, equipment modifications, or administrative adjustments. Addressing noise hazards during facility design, process changes, and routine risk assessments allows organizations to manage exposure before permanent health effects occur.

By contrast, correcting deficiencies after hearing loss has been documented typically requires expanded monitoring, program restructuring, and long-term medical surveillance – increasing both cost and administrative burden. Preventing exposure supports effective risk management while demonstrating a commitment to protecting workers from permanent, avoidable health impacts.

Elements of an Effective Noise Control Strategy

Effective noise management follows risk assessment techniques and the well-established hierarchy of controls. Risk assessment often begins with data gathering through measurement – whether dosimetry for personal exposure evaluation or noise level monitoring of areas or equipment. Engineering controls – such as source modification, equipment enclosures, vibration isolation, or selection of lower-noise alternatives – provide the most reliable exposure reduction. Administrative controls, including task rotation and exposure-time management, may further reduce risk when engineering solutions are limited.

Hearing protection devices remain an important component but should be considered the last line of defense, not the primary control. While critical as an interim protective measure when noise hazards are identified, hearing protection is too often implemented in place of more effective engineering or administrative controls. Personal Protective Equipment (PPE) can become the preferred solution because it feels immediate and convenient, rather than because it provides the most effective risk reduction. When not supported by exposure data, appropriate selection, and training, reliance on hearing protection can limit communication and recognition of auditory hazard cues such as alarms or approaching equipment.

Implementing these measures effectively often requires objective data, technical expertise, and a clear understanding of regulatory expectations.

HETI’s Role in Proactive Noise Management

Given the widespread nature of occupational noise exposure and the permanence of its effects, early, data-driven evaluation is essential. HETI supports clients across regulated industries through noise exposure assessments, personal dosimetry, hazard control design, and hearing conservation program evaluations aligned with regulatory requirements and best practice standards.

HETI’s noise control evaluations are led by experienced Certified Industrial Hygienists (CIHs) with expertise in noise exposure assessment, regulatory compliance, and practical risk control.

HETI’s approach emphasizes practical, defensible recommendations that support compliance while prioritizing prevention. In many cases, early assessments identify manageable exposure pathways before they result in irreversible health outcomes, regulatory scrutiny, or costly remediation. Occupational hearing loss should never be the indicator that action was needed. Noise hazards will always be widespread and capable of causing permanent harm, but with proactive evaluation and effective control design, the outcomes become manageable – and workers remain safe, healthy, and protected.

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To find out more about this and other HETI industrial hygiene services, please contact us.

Ben MacDonald, CIH, CSP Senior Industrial Hygienist