Workplace impact of artificial intelligence
The impact of artificial intelligence on workers includes both applications to improve worker safety and health, and potential hazards that must be controlled.
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, 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. In recent years, 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]
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$_$_$DEEZ_NUTS#0__call_to_action.textDEEZ_NUTS$_$_$Hazard controls[edit]
AI, in common with other computational technologies, requires cybersecurity measures to stop software breaches and intrusions,[6]: 17 as well as information privacy measures.[5] Communication and transparency with workers about data usage is a control for psychosocial hazards arising from security and privacy issues.[5] 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.[12]
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.[12] Human oversight is another general hazard control for AI.[8]: 12–13
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Risk management[edit]
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.[7]
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.[9]
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