Environmental, Health, Safety, & Quality Professionals Can Mitigate Risk with AI

Alan L. Johnson

All companies deal with risks. Companies that work in hazardous environments or handle hazardous materials deal with acute health, safety, environmental, & security risks. Most Environmental, Health, Safety, & Quality (EHSQ) professionals are at the crossroads of some of the most risky environments in the world. As AI continues to influence industries and organizational specific work-flows, what will the positive benefits be in the lives of EHSQ professionals?


Employee physical, mental, and emotional health are serious risks to productivity for EHSQ professionals in most industries. Many advancements in AI have made it easier to monitor worker health and the environment in which they work. Benefits are helping to ensure appropriate worker environmental or behavioral responses are made in the moment to reduce risk. An example of an AI monitoring program for worker health is Fujitsu's new algorithm.

According to Fujitsu's website:

In addition to the existing data of humidity, temperature and pulse, the new algorithm estimates the level of heat stress based on new data, such the amount of activity, as well as data that shifts over time. Since machine learning is appropriate for making estimates from a wide variety of data with unclear correlations, Fujitsu developed a logic in which AI can extract the characteristics of high heat stress from the stock of actual data and data evaluated by experts. This has enabled the algorithm to estimate accumulated heat stress in the same way that labor science experts would, enabling users to observe the status of individual employees in situations that do not require a great deal of activity, such as security guards who must spend long hours standing in the hot sun.


Keeping workers safe is a serious risk when hazards are high. There is no doubt that AI is making impacts already in safety. According to the MIT Technology Review even the construction industry stands to advance itself where workers are five times more likely to get killed than in other industries.

According to MIT:

Suffolk, a Boston-based general contractor with annual sales of $3 billion, is developing an algorithm that analyzes photos from its job sites, scans them for safety hazards such as workers not wearing protective equipment, and correlates the images with its accident records. The company is still fine-tuning the technology but says it could potentially compute “risk ratings” for projects so safety briefings can be held when an elevated threat is detected.

The trend is not just with worker safety as we've seen with Uber's commitment to make passenger safety a priority.

As CNET says:

Safety is a consistent issue for Uber, both for drivers and passengers. A report in May claimed that at least 103 Uber drivers and 18 Lyft drivers sexually assaulted passengers. On the digital side, Uber drivers were victims of a phishing scam where thieves emptied their accounts through a sophisticated scheme. Getting into a car with a complete stranger -- while giving out your name and address -- is also a concern the company looks to fix.


Discharges to the environment and highly pollutant processes are large social risks to modern companies. There is a ton of work that is being done globally to reduce emissions and make the world a safer place, but AI is taking a dominant role in automating these efforts. In fact, the world economic forum announced a new report in collaboration with PwC and the Stanford Woods Institute where they identified eight “game changer” AI applications to address this planet’s challenges:

Autonomous and connected electric vehicles - AI-guided autonomous vehicles (AVs) will enable a transition to mobility on-demand over the coming years and decades. Substantial greenhouse gas reductions for urban transport can be unlocked through route and traffic optimisation, eco-driving algorithms, programmed “platooning” of cars to traffic, and autonomous ride-sharing services. Electric AV fleets will be critical to deliver real gains.

Distributed energy grids - AI can enhance the predictability of demand and supply for renewables across a distributed grid, improve energy storage, efficiency and load management, assist in the integration and reliability of renewables and enable dynamic pricing and trading, creating market incentives.

Smart agriculture and food systems - AI-augmented agriculture involves automated data collection, decision-making and corrective actions via robotics to allow early detection of crop diseases and issues, to provide timed nutrition to livestock, and generally to optimise agricultural inputs and returns based on supply and demand. This promises to increase the resource efficiency of the agriculture industry, lowering the use of water, fertilisers and pesticides which cause damage to important ecosystems, and increase resilience to climate extremes.

Next generation weather and climate prediction - A new field of “Climate Informatics” is blossoming that uses AI to fundamentally transform weather forecasting and improve our understanding of the effects of climate change. This field traditionally requires high performance energy-intensive computing, but deep-learning networks can allow computers to run much faster and incorporate more complexity of the ‘real-world’ system into the calculations.

Smart disaster response - AI can analyse simulations and real-time data (including social media data) of weather events and disasters in a region to seek out vulnerabilities and enhance disaster preparation, provide early warning, and prioritise response through coordination of emergency information capabilities. Deep reinforcement learning may one day be integrated into disaster simulations to determine optimal response strategies, similar to the way AI is currently being used to identify the best move in games like AlphaGo.

AI-designed intelligent, connected and livable cities - AI could be used to simulate and automate the generation of zoning laws, building ordinances and floodplains, combined with augmented and virtual reality (AR and VR). Real-time city-wide data on energy, water consumption and availability, traffic flows, people flows, and weather could create an “urban dashboard” to optimise urban sustainability.

A transparent digital Earth - A real-time, open API, AI-infused, digital geospatial dashboard for the planet would enable the monitoring, modelling and management of environmental systems at a scale and speed never before possible – from tackling illegal deforestation, water extraction, fishing and poaching, to air pollution, natural disaster response and smart agriculture.

Reinforcement learning for Earth sciences breakthroughs - This nascent AI technique – which requires no input data, substantially less computing power, and in which the evolutionary-like AI learns from itself – could soon evolve to enable its application to real-world problems in the natural sciences. Collaboration with Earth scientists to identify the systems – from climate science, materials science, biology, and other areas – which can be codified to apply reinforcement learning for scientific progress and discovery is vital. For example, DeepMind co-founder, Demis Hassabis, has suggested that in materials science, a descendant of AlphaGo Zero could be used to search for a room temperature superconductor – a hypothetical substance that allows for incredibly efficient energy systems.


Companies can have major security risks in the products they use, manufacture, or store. In hazardous environments security is becoming more of a focal point with the rise of clandestine terrorism. In fact The Department developed Appendix A that includes a list of chemicals that present one or more security issues. If a company has any of these materials above their Screening Threshold Quantities (STQs), then they are subject to this regulation. In fact, McKinsey Global Institute (MGI) suggests that IoT security systems will reduce the cost of building security by over $6 billion per year by 2025, and a great deal of these savings are likely to come from AI-powered security technology. AI is already being used in surveillance.

AI is also taking a prominent role in cyber security as well. CSO Online reports the following statistics about AI in cybersecurity:

29 percent want to use AI-based cybersecurity technology to accelerate incident detection. In many cases, this means doing a better job of curating, correlating, and enriching high-volume security alerts to piece together a cohesive incident detection story across disparate tools.

27 percent want to use AI-based cybersecurity technology to accelerate incident response. This means improving operations, prioritizing the right incidents, and even automating remediation tasks.

24 percent want to use AI-based cybersecurity technology to help their organization better identify and communicate risk to the business. In this case, AI is used to sort through mountains of software vulnerabilities, configuration errors, and threat intelligence to isolate high-risk situations that call for immediate attention.

22 percent want to use AI-based cybersecurity technology to gain a better understanding of cybersecurity situational awareness. In other words, CISOs want AI in the mix to give them a unified view of security status across the network.

No matter if physical or digital assets are in jeopardy AI can help ensure their safety and protect organizations from nefarious risk.

The use of AI will revolutionize EHSQ. From saving lives, to protecting the environment, to driving productivity, to securing assets, AI will reduce common enterprise risks for organizations.

Reality Changing Observations:

Q1. Are you an EHSQ professional that is working on an AI project?

Q2. What responsibilities do you think that we have with AI in the workplace?

Q3. How can you help educate others and yourself to the value of AI?