page_title: Workplace impact of artificial intelligence
section_title: Hazards
hierarchical_section_title: Workplace impact of artificial intelligence / Hazards
caption_attribution_description: English: add 3d borders and stimulus/response to original File:Blackbox.svg
context_page_description: 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.
context_section_description: There are several aspects of AI that may give rise to more specific hazards.  The risks depend on implementation rather than the mere presence of AI.  
  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.  They may arise from improper training, such as a user applying the same algorithm to two problems that do not have different requirements.  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.
  The use of AI also increases cybersecurity risks relative to platforms that do not use AI, and information privacy concerns about collected data may pose a hazard to workers.
caption: 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.
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