Workplace impact of artificial intelligence

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AI-enabled wearable sensor networks may improve worker safety and health through access to real-time, personalized data, but also presents psychosocial hazards such as micromanagement, a perception of surveillance, and information security concerns. Autographer lifelogging device.png
AI-enabled wearable sensor networks may improve worker safety and health through access to real-time, personalized data, but also presents psychosocial hazards such as micromanagement, a perception of surveillance, and information security concerns.

The impact of artificial intelligence on workers includes both applications to improve worker safety and health, and potential hazards that must be controlled.

Contents

One potential application is using AI to eliminate hazards by removing humans from hazardous situations that involve risk of stress, overwork, or musculoskeletal injuries. Predictive analytics may also be used to identify conditions that may lead to hazards such as fatigue, repetitive strain injuries, or toxic substance exposure, leading to earlier interventions. Another is to streamline workplace safety and health workflows through automating repetitive tasks, enhancing safety training programs through virtual reality, or detecting and reporting near misses.

When used in the workplace, AI also presents the possibility of new hazards. These may arise from machine learning techniques leading to unpredictable behavior and inscrutability in their decision-making, or from cybersecurity and information privacy issues. Many hazards of AI are psychosocial due to its potential to cause changes in work organization. These include changes in the skills required of workers, [1] increased monitoring leading to micromanagement, algorithms unintentionally or intentionally mimicking undesirable human biases, and assigning blame for machine errors to the human operator instead. AI may also lead to physical hazards in the form of human–robot collisions, and ergonomic risks of control interfaces and human–machine interactions. Hazard controls include cybersecurity and information privacy measures, communication and transparency with workers about data usage, and limitations on collaborative robots.

From a workplace safety and health perspective, only "weak" or "narrow" AI that is tailored to a specific task is relevant, as there are many examples that are currently in use or expected to come into use in the near future. "Strong" or "general" AI is not expected to be feasible in the near future,[ according to whom? ] and discussion of its risks is within the purview of futurists and philosophers rather than industrial hygienists.

Certain digital technologies are predicted to result in job losses. Starting in the 2020s, the adoption of modern robotics has led to net employment growth. However, many businesses anticipate that automation, or employing robots would result in job losses in the future. This is especially true for companies in Central and Eastern Europe. [2] [3] [4] Other digital technologies, such as platforms or big data, are projected to have a more neutral impact on employment. [2] [4] A large number of tech workers have been laid off starting in 2023; [5] many such job cuts have been attributed to artificial intelligence. [6]

Health and safety applications

In order for any potential AI health and safety application to be adopted, it requires acceptance by both managers and workers. For example, worker acceptance may be diminished by concerns about information privacy, [7] or from a lack of trust and acceptance of the new technology, which may arise from inadequate transparency or training. [8] : 26–28, 43–45  Alternatively, managers may emphasize increases in economic productivity rather than gains in worker safety and health when implementing AI-based systems. [9]

Eliminating hazardous tasks

Call centers involve significant psychosocial hazards due to surveillance and overwork. AI-enabled chatbots can remove workers from the most basic and repetitive of these tasks. Callcentre.jpg
Call centers involve significant psychosocial hazards due to surveillance and overwork. AI-enabled chatbots can remove workers from the most basic and repetitive of these tasks.

AI may increase the scope of work tasks where a worker can be removed from a situation that carries risk. In a sense, while traditional automation can replace the functions of a worker's body with a robot, AI effectively replaces the functions of their brain with a computer. Hazards that can be avoided include stress, overwork, musculoskeletal injuries, and boredom. [10] : 5–7 

This can expand the range of affected job sectors into white-collar and service sector jobs such as in medicine, finance, and information technology. [11] As an example, call center workers face extensive health and safety risks due to its repetitive and demanding nature and its high rates of micro-surveillance. AI-enabled chatbots lower the need for humans to perform the most basic call center tasks. [10] : 5–7 

Analytics to reduce risk

The NIOSH lifting equation is calibrated for a typical healthy worker to avoid back injuries, but AI-based methods may instead allow real-time, personalized calculation of risk. Niosh-lifting-equation-illustration.gif
The NIOSH lifting equation is calibrated for a typical healthy worker to avoid back injuries, but AI-based methods may instead allow real-time, personalized calculation of risk.

Machine learning is used for people analytics to make predictions about worker behavior to assist management decision-making, such as hiring and performance assessment. These could also be used to improve worker health. The analytics may be based on inputs such as online activities, monitoring of communications, location tracking, and voice analysis and body language analysis of filmed interviews. For example, sentiment analysis may be used to spot fatigue to prevent overwork. [10] : 3–7  Decision support systems have a similar ability to be used to, for example, prevent industrial disasters or make disaster response more efficient. [14]

For manual material handling workers, predictive analytics and artificial intelligence may be used to reduce musculoskeletal injury. Traditional guidelines are based on statistical averages and are geared towards anthropometrically typical humans. The analysis of large amounts of data from wearable sensors may allow real-time, personalized calculation of ergonomic risk and fatigue management, as well as better analysis of the risk associated with specific job roles. [7]

Wearable sensors may also enable earlier intervention against exposure to toxic substances than is possible with area or breathing zone testing on a periodic basis. Furthermore, the large data sets generated could improve workplace health surveillance, risk assessment, and research. [14]

Streamlining safety and health workflows

AI can also be used to make the workplace safety and health workflow more efficient. Digital assistants, like Amazon Alexa, Google Assistant, and Apple Siri, are increasingly adopted in workplaces to enhance productivity by automating routine tasks. These AI-based tools can manage administrative duties, such as scheduling meetings, sending reminders, processing orders, and organizing travel plans. This automation can improve workflow efficiency by reducing time spent on repetitive tasks, thus supporting employees to focus on higher-priority responsibilities. [15] Digital assistants are especially valuable in streamlining customer service workflows, where they can handle basic inquiries, reducing the demand on human employees. [15] However, there remain challenges in fully integrating these assistants due to concerns over data privacy, accuracy, and organizational readiness. [15]

One example is coding of workers' compensation claims, which are submitted in a prose narrative form and must manually be assigned standardized codes. AI is being investigated to perform this task faster, more cheaply, and with fewer errors. [16] [17]

AI‐enabled virtual reality systems may be useful for safety training for hazard recognition. [14]

Artificial intelligence may be used to more efficiently detect near misses. Reporting and analysis of near misses are important in reducing accident rates, but they are often underreported because they are not noticed by humans, or are not reported by workers due to social factors. [18]

Hazards

Some machine learning training methods are prone to unpredictabiliy and inscrutability in their decision-making, which can lead to hazards if managers or workers cannot predict or understand an AI-based system's behavior. Blackbox3D.png
Some machine learning training methods are prone to unpredictabiliy and inscrutability in their decision-making, which can lead to hazards if managers or workers cannot predict or understand an AI-based system's behavior.

There are several broad aspects of AI that may give rise to specific hazards. The risks depend on implementation rather than the mere presence of AI. [10] : 2–3 

Systems using sub-symbolic AI such as machine learning may behave unpredictably and are more prone to inscrutability in their decision-making. This is especially true if a situation is encountered that was not part of the AI's training dataset, and is exacerbated in environments that are less structured. Undesired behavior may also arise from flaws in the system's perception (arising either from within the software or from sensor degradation), knowledge representation and reasoning, or from software bugs. [8] : 14–18  They may arise from improper training, such as a user applying the same algorithm to two problems that do not have the same requirements. [10] : 12–13  Machine learning applied during the design phase may have different implications than that applied at runtime. Systems using symbolic AI are less prone to unpredictable behavior. [8] : 14–18 

The use of AI also increases cybersecurity risks relative to platforms that do not use AI, [8] : 17  and information privacy concerns about collected data may pose a hazard to workers. [7]

Psychosocial

Introduction of new AI-enabled technologies may lead to changes in work practices that carry psychosocial hazards such as a need for retraining or fear of technological unemployment. Pyxis Pharmacy Robot by Nurse Station.JPG
Introduction of new AI-enabled technologies may lead to changes in work practices that carry psychosocial hazards such as a need for retraining or fear of technological unemployment.

Psychosocial hazards are those that arise from the way work is designed, organized, and managed, or its economic and social contexts, rather than arising from a physical substance or object. They cause not only psychiatric and psychological outcomes such as occupational burnout, anxiety disorders, and depression, but they can also cause physical injury or illness such as cardiovascular disease or musculoskeletal injury. [19] Many hazards of AI are psychosocial in nature due to its potential to cause changes in work organization, in terms of increasing complexity and interaction between different organizational factors. However, psychosocial risks are often overlooked by designers of advanced manufacturing systems. [9]

Changes in work practices

AI is expected to lead to changes in the skills required of workers, requiring training of existing workers, flexibility, and openness to change. [1] The requirement for combining conventional expertise with computer skills may be challenging for existing workers. [9] Over-reliance on AI tools may lead to deskilling of some professions. [14]

Increased monitoring may lead to micromanagement and thus to stress and anxiety. A perception of surveillance may also lead to stress. Controls for these include consultation with worker groups, extensive testing, and attention to introduced bias. Wearable sensors, activity trackers, and augmented reality may also lead to stress from micromanagement, both for assembly line workers and gig workers. Gig workers also lack the legal protections and rights of formal workers. [10] : 2–10 

There is also the risk of people being forced to work at a robot's pace, or to monitor robot performance at nonstandard hours. [10] : 5–7 

Bias

Algorithms trained on past decisions may mimic undesirable human biases, for example, past discriminatory hiring and firing practices. Information asymmetry between management and workers may lead to stress, if workers do not have access to the data or algorithms that are the basis for decision-making. [10] : 3–5 

In addition to building a model with inadvertently discriminatory features, intentional discrimination may occur through designing metrics that covertly result in discrimination through correlated variables in a non-obvious way. [10] : 12–13 

In complex human‐machine interactions, some approaches to accident analysis may be biased to safeguard a technological system and its developers by assigning blame to the individual human operator instead. [14]

Physical

Automated guided vehicles are examples of cobots currently in common use. Use of AI to operate these robots may affect the risk of physical hazards such as the robot or its moving parts colliding with workers. AGVs amarillos.jpg
Automated guided vehicles are examples of cobots currently in common use. Use of AI to operate these robots may affect the risk of physical hazards such as the robot or its moving parts colliding with workers.

Physical hazards in the form of human–robot collisions may arise from robots using AI, especially collaborative robots (cobots). Cobots are intended to operate in close proximity to humans, which makes impossible the common hazard control of isolating the robot using fences or other barriers, which is widely used for traditional industrial robots. Automated guided vehicles are a type of cobot that as of 2019 are in common use, often as forklifts or pallet jacks in warehouses or factories. [8] : 5, 29–30  For cobots, sensor malfunctions or unexpected work environment conditions can lead to unpredictable robot behavior and thus to human–robot collisions. [10] : 5–7 

Self-driving cars are another example of AI-enabled robots. In addition, the ergonomics of control interfaces and human–machine interactions may give rise to hazards. [9]

Hazard controls

AI, in common with other computational technologies, requires cybersecurity measures to stop software breaches and intrusions, [8] : 17  as well as information privacy measures. [7] Communication and transparency with workers about data usage is a control for psychosocial hazards arising from security and privacy issues. [7] Proposed best practices for employer‐sponsored worker monitoring programs include using only validated sensor technologies; ensuring voluntary worker participation; ceasing data collection outside the workplace; disclosing all data uses; and ensuring secure data storage. [14]

For industrial cobots equipped with AI‐enabled sensors, the International Organization for Standardization (ISO) recommended: (a) safety‐related monitored stopping controls; (b) human hand guiding of the cobot; (c) speed and separation monitoring controls; and (d) power and force limitations. Networked AI-enabled cobots may share safety improvements with each other. [14] Human oversight is another general hazard control for AI. [10] : 12–13 

Risk management

Both applications and hazards arising from AI can be considered as part of existing frameworks for occupational health and safety risk management. As with all hazards, risk identification is most effective and least costly when done in the design phase. [9]

Workplace health surveillance, the collection and analysis of health data on workers, is challenging for AI because labor data are often reported in aggregate and does not provide breakdowns between different types of work, and is focused on economic data such as wages and employment rates rather than skill content of jobs. Proxies for skill content include educational requirements and classifications of routine versus non-routine, and cognitive versus physical jobs. However, these may still not be specific enough to distinguish specific occupations that have distinct impacts from AI. The United States Department of Labor's Occupational Information Network is an example of a database with a detailed taxonomy of skills. Additionally, data are often reported on a national level, while there is much geographical variation, especially between urban and rural areas. [11]

Standards and regulation

As of 2019, ISO was developing a standard on the use of metrics and dashboards, information displays presenting company metrics for managers, in workplaces. The standard is planned to include guidelines for both gathering data and displaying it in a viewable and useful manner. [10] : 11  [20] [21]

In the European Union, the General Data Protection Regulation, while oriented towards consumer data, is also relevant for workplace data collection. Data subjects, including workers, have "the right not to be subject to a decision based solely on automated processing". Other relevant EU directives include the Machinery Directive (2006/42/EC), the Radio Equipment Directive (2014/53/EU), and the General Product Safety Directive (2001/95/EC). [10] : 10, 12–13 

Related Research Articles

<span class="mw-page-title-main">Occupational injury</span> Bodily damage resulting from working

An occupational injury is bodily damage resulting from working. The most common organs involved are the spine, hands, the head, lungs, eyes, skeleton, and skin. Occupational injuries can result from exposure to occupational hazards, such as temperature, noise, insect or animal bites, blood-borne pathogens, aerosols, hazardous chemicals, radiation, and occupational burnout.

<span class="mw-page-title-main">National Institute for Occupational Safety and Health</span> US federal government agency

The National Institute for Occupational Safety and Health is the United States federal agency responsible for conducting research and making recommendations for the prevention of work-related injury and illness. NIOSH is part of the Centers for Disease Control and Prevention (CDC) within the U.S. Department of Health and Human Services. Despite its name, it is not part of either the National Institutes of Health nor OSHA. Its current director is John Howard.

<span class="mw-page-title-main">Occupational hygiene</span> Management of workplace health hazards

Occupational hygiene or industrial hygiene (IH) is the anticipation, recognition, evaluation, control, and confirmation (ARECC) of protection from risks associated with exposures to hazards in, or arising from, the workplace that may result in injury, illness, impairment, or affect the well-being of workers and members of the community. These hazards or stressors are typically divided into the categories biological, chemical, physical, ergonomic and psychosocial. The risk of a health effect from a given stressor is a function of the hazard multiplied by the exposure to the individual or group. For chemicals, the hazard can be understood by the dose response profile most often based on toxicological studies or models. Occupational hygienists work closely with toxicologists (see Toxicology) for understanding chemical hazards, physicists (see Physics) for physical hazards, and physicians and microbiologists for biological hazards (see Microbiology, Tropical medicine, Infection). Environmental and occupational hygienists are considered experts in exposure science and exposure risk management. Depending on an individual's type of job, a hygienist will apply their exposure science expertise for the protection of workers, consumers and/or communities.

<span class="mw-page-title-main">Waste collector</span> Person employed by a public or private enterprise to collect and dispose of waste

A waste collector, also known as a garbage man, garbage collector, trashman, binman or dustman, is a person employed by a public or private enterprise to collect and dispose of municipal solid waste (refuse) and recyclables from residential, commercial, industrial or other collection sites for further processing and waste disposal. Specialised waste collection vehicles featuring an array of automated functions are often deployed to assist waste collectors in reducing collection and transport time and for protection from exposure. Waste and recycling pickup work is physically demanding and usually exposes workers to an occupational hazard.

<span class="mw-page-title-main">Occupational hazard</span> Hazard experienced in the workplace

An occupational hazard is a hazard experienced in the workplace. This encompasses many types of hazards, including chemical hazards, biological hazards (biohazards), psychosocial hazards, and physical hazards. In the United States, the National Institute for Occupational Safety and Health (NIOSH) conduct workplace investigations and research addressing workplace health and safety hazards resulting in guidelines. The Occupational Safety and Health Administration (OSHA) establishes enforceable standards to prevent workplace injuries and illnesses. In the EU, a similar role is taken by EU-OSHA.

<span class="mw-page-title-main">Musculoskeletal disorder</span> Pain in the muscular or skeletal systems

Musculoskeletal disorders (MSDs) are injuries or pain in the human musculoskeletal system, including the joints, ligaments, muscles, nerves, tendons, and structures that support limbs, neck and back. MSDs can arise from a sudden exertion, or they can arise from making the same motions repeatedly, or from repeated exposure to force, vibration, or awkward posture. Injuries and pain in the musculoskeletal system caused by acute traumatic events like a car accident or fall are not considered musculoskeletal disorders. MSDs can affect many different parts of the body including upper and lower back, neck, shoulders and extremities. Examples of MSDs include carpal tunnel syndrome, epicondylitis, tendinitis, back pain, tension neck syndrome, and hand-arm vibration syndrome.

A recommended exposure limit (REL) is an occupational exposure limit that has been recommended by the United States National Institute for Occupational Safety and Health. The REL is a level that NIOSH believes would be protective of worker safety and health over a working lifetime if used in combination with engineering and work practice controls, exposure and medical monitoring, posting and labeling of hazards, worker training and personal protective equipment. To formulate these recommendations, NIOSH evaluates all known and available medical, biological, engineering, chemical, trade, and other information. Although not legally enforceable limits, RELS are transmitted to the Occupational Safety and Health Administration (OSHA) or the Mine Safety and Health Administration (MSHA) of the U.S. Department of Labor for use in promulgating legal standards.

An occupational exposure limit is an upper limit on the acceptable concentration of a hazardous substance in workplace air for a particular material or class of materials. It is typically set by competent national authorities and enforced by legislation to protect occupational safety and health. It is an important tool in risk assessment and in the management of activities involving handling of dangerous substances. There are many dangerous substances for which there are no formal occupational exposure limits. In these cases, hazard banding or control banding strategies can be used to ensure safe handling.

The Institute for Occupational Safety and Health of the German Social Accident Insurance is a German institute located in Sankt Augustin near Bonn and is a main department of the German Social Accident Insurance. Belonging to the Statutory Accident Insurance means that IFA is a non-profit institution.

Workplace health surveillance or occupational health surveillance (U.S.) is the ongoing systematic collection, analysis, and dissemination of exposure and health data on groups of workers. The Joint ILO/WHO Committee on Occupational Health at its 12th Session in 1995 defined an occupational health surveillance system as "a system which includes a functional capacity for data collection, analysis and dissemination linked to occupational health programmes".

Occupational health psychology (OHP) is an interdisciplinary area of psychology that is concerned with the health and safety of workers. OHP addresses a number of major topic areas including the impact of occupational stressors on physical and mental health, the impact of involuntary unemployment on physical and mental health, work-family balance, workplace violence and other forms of mistreatment, psychosocial workplace factors that affect accident risk and safety, and interventions designed to improve and/or protect worker health. Although OHP emerged from two distinct disciplines within applied psychology, namely, health psychology and industrial and organizational psychology, for a long time the psychology establishment, including leaders of industrial/organizational psychology, rarely dealt with occupational stress and employee health, creating a need for the emergence of OHP. OHP has also been informed by other disciplines, including occupational medicine, sociology, industrial engineering, and economics, as well as preventive medicine and public health. OHP is thus concerned with the relationship of psychosocial workplace factors to the development, maintenance, and promotion of workers' health and that of their families. The World Health Organization and the International Labour Organization estimate that exposure to long working hours causes an estimated 745,000 workers to die from ischemic heart disease and stroke in 2016, mediated by occupational stress.

<span class="mw-page-title-main">Physical hazard</span> Hazard due to a physical agent

A physical hazard is an agent, factor or circumstance that can cause harm with contact. They can be classified as type of occupational hazard or environmental hazard. Physical hazards include ergonomic hazards, radiation, heat and cold stress, vibration hazards, and noise hazards. Engineering controls are often used to mitigate physical hazards.

<span class="mw-page-title-main">Occupational safety and health</span> Field concerned with the safety, health and welfare of people at work

Occupational safety and health (OSH) or occupational health and safety (OHS) is a multidisciplinary field concerned with the safety, health, and welfare of people at work. OSH is related to the fields of occupational medicine and occupational hygiene and aligns with workplace health promotion initiatives. OSH also protects all the general public who may be affected by the occupational environment.

<span class="mw-page-title-main">Hierarchy of hazard controls</span> System used in industry to eliminate or minimize exposure to hazards

Hierarchy of hazard control is a system used in industry to prioritize possible interventions to minimize or eliminate exposure to hazards. It is a widely accepted system promoted by numerous safety organizations. This concept is taught to managers in industry, to be promoted as standard practice in the workplace. It has also been used to inform public policy, in fields such as road safety. Various illustrations are used to depict this system, most commonly a triangle.

Total Worker Health is a trademarked strategy defined as policies, programs, and practices that integrate protection from work-related safety and health hazards with promotion of injury and illness prevention efforts to advance worker well-being. It was conceived and is funded by the National Institute for Occupational Safety and Health (NIOSH). Total Worker Health is tested and developed in six Centers of Excellence for Total Worker Health in the United States.

A psychosocial hazard or work stressor is any occupational hazard related to the way work is designed, organized and managed, as well as the economic and social contexts of work. Unlike the other three categories of occupational hazard, they do not arise from a physical substance, object, or hazardous energy.

Engineering controls are strategies designed to protect workers from hazardous conditions by placing a barrier between the worker and the hazard or by removing a hazardous substance through air ventilation. Engineering controls involve a physical change to the workplace itself, rather than relying on workers' behavior or requiring workers to wear protective clothing.

<span class="mw-page-title-main">Occupational exposure banding</span> Process to assign chemicals into categories corresponding to permissible exposure concentrations

Occupational exposure banding, also known as hazard banding, is a process intended to quickly and accurately assign chemicals into specific categories (bands), each corresponding to a range of exposure concentrations designed to protect worker health. These bands are assigned based on a chemical’s toxicological potency and the adverse health effects associated with exposure to the chemical. The output of this process is an occupational exposure band (OEB). Occupational exposure banding has been used by the pharmaceutical sector and by some major chemical companies over the past several decades to establish exposure control limits or ranges for new or existing chemicals that do not have formal OELs. Furthermore, occupational exposure banding has become an important component of the Hierarchy of Occupational Exposure Limits (OELs).

<span class="mw-page-title-main">Workplace robotics safety</span>

Workplace robotics safety is an aspect of occupational safety and health when robots are used in the workplace. This includes traditional industrial robots as well as emerging technologies such as drone aircraft and wearable robotic exoskeletons. Types of accidents include collisions, crushing, and injuries from mechanical parts. Hazard controls include physical barriers, good work practices, and proper maintenance.

An occupational risk assessment is an evaluation of how much potential danger a hazard can have to a person in a workplace environment. The assessment takes into account possible scenarios in addition to the probability of their occurrence, and the results. The five types of hazards to be aware of are safety, chemicals, biological, physical, and ergonomic.

References

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