The complete guide to AI safety in the workplace

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In an era where technology is reshaping the contours of the workplace, artificial intelligence (AI) emerges as a pivotal ally in enhancing safety measures. This comprehensive guide delves into the transformative role AI plays in safeguarding the workforce.

From leveraging sophisticated algorithms to deploying computer vision, we explore the multifaceted applications of AI that enable Health and Safety managers to foster a secure and efficient work environment.

With a focus on prevention, AI-driven safety tools are revolutionizing how potential hazards are identified and mitigated, ensuring that safety is not just a policy but a cornerstone of the workplace culture.

As we navigate the intricacies of AI in safety protocols, this guide serves as an essential resource for businesses looking to harness the power of cutting-edge technology to protect their most valuable asset—their employees.

Whether you're a seasoned EHS professional or new to the domain, our insights will equip you with the knowledge to integrate AI into your safety strategy effectively, paving the way for a safer, more innovative, and more resilient workplace.

Section 1: Introduction to AI Safety

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Workplace safety is an essential consideration for modern businesses, especially in sectors known for dangerous tasks like the construction or manufacturing industry. According to a report by the National Safety Council, the number of preventable work deaths in the USA in 2020 was 4,113.

This was a 10% decrease from the prior year, and the Council attributed it mainly to the economic disruption caused by the global pandemic. The most preventable work injuries were in the construction sector, which further outlines the importance of instituting safety measures in the workplace.

Depending upon the industry and the nature of work, there can be hundreds of hazards that pose varying levels of risk, including:

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• Harmful chemicals
• Physical hazards like falling objects
• Ergonomic hazards
• Noxious gasses
• Sharp objects

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As you can imagine, this is a partial list. To improve workplace safety, many companies are now using artificial intelligence to their advantage.

The popularity of AI safety is primarily fueled by significant advances in deep learning algorithms, which are now capable of "learning" by simply processing large volumes of data.

Previously, companies had to hire safety officers to ensure workers on site took safety standards seriously. These safety officers would monitor the use of PPE (personal protective equipment) and alert employees to different dangers in the environment.

However, human error is a genuine possibility. Organizations need help to employ safety officers to carefully monitor hundreds of workers across a larger area, such as a construction site.

That's where AI safety comes in. AI safety tools are capable of processing information much quicker. They can be used to identify risks or hazards in the workplace.

Section 2: AI Safety – A Technical Overview

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Artificial intelligence, in its fundamental form, is any algorithm or machine that can mimic the workings of the human brain. It uses algorithms and processes to simulate human intelligence in a machine.

However, this can be considered an abstract definition. Practically, AI is regarded as the ability of a machine to perform a simple cognitive function: learning. AI machines can be fed large amounts of data, and by processing it, they learn to recognize patterns.

AI safety tools use deep learning, a subset of machine learning, to analyze hundreds of thousands of images. They do this by breaking the images into millions of pixels and then analyzing subtle differences between each.

Over time, they can detect various objects. For instance, an AI powered workplace safety tool can be "trained" to detect the use of hard hats on a construction site, ensuring worker safety by monitoring for hazardous conditions and alerting potential safety risks.

They can be connected to a company's existing CCTV network, thus offering real-time monitoring. AI safety tools rely on computer vision and video content analysis to detect PPE usage in real-time.

They can then send alerts, including notifications or even text messages, to employees or the department about safety protocols. While all of this happens within seconds, there's a complex web of technologies that make it happen.

Fairness and Bias in A.I. Systems

Fairness in A.I. systems refers to removing prejudice in the data used to train and build the system. Systems that lack fairness can have a harmful impact on marginalized communities, such as gender, race, and socio-economic status.

 When building A.I. models, developers must ensure that their technology is inclusive and does not discriminate against specific demographics. This study supports these claims by examining AI's impact on occupational safety and health equity.

Bias, on the other hand, occurs when the technology is programmed to respond or behave differently towards individuals based on their background or physical characteristics. This bias leads to unfair treatment wherein A.I. systems evaluate and assess individuals differently. This study by EU-OSHA highlights the need for unbiased AI systems in the workplace. 

It is vital to ensure that A.I. systems are designed to protect individuals' privacy, avoid unintended consequences, and promote fairness. Fairness in A.I. systems support human dignity and ethics while eliminating discriminatory behavior. 

Furthermore, if bias and fairness issues go unaddressed, it can cause negative impacts on individuals and organizations. This may also result in individuals needing more confidence in the technology.

To understand the full definition of safety in the workplace, our glossary can provide you with comprehensive insights.

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Explainability and Interoperability of AI Models

Explainability refers to the transparency of AI models, and it is critical in the EHS context. With explainability, EHS teams can understand the decision-making process behind the AI models, assess their reliability, and identify potential biases. This study echoes this sentiment, providing a perspective on human control in AI for occupational safety.

In contrast, if the decision-making process of AI subsystems is opaque, there will be no way to determine how it arrived at its conclusion. For instance, if an AI model identifies a potential hazard, the EHS team needs to understand how the model arrived at that conclusion.

Interoperability refers to the ability of AI models to communicate and work together seamlessly, often through standardized application programming interfaces (APIs).

From a workplace safety standpoint, the interoperability of AI models is critical when accessing data from different sources. Interoperability can create a more coherent picture of the organization, allowing EHS teams to access all the data needed to make the right decisions.

Let's explore some key technologies that play an essential role in AI safety.

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1. Computer Vision

Computer vision involves using artificial intelligence to enable systems and computers to extract critical information from digital imagery and videos, much like a human sees.

Machines that use computer vision can be trained to detect objects with the help of a camera. Once a data source is connected, systems can "learn" by inspecting different images.

They rely on complex technologies, including convolutional neural networks, which allow machines to analyze images. Every pixel is labeled and tagged before the system starts running convolutions to determine if its predictions are accurate.

In the beginning, accuracy is generally low, as the system is only capable of identifying simple shapes and prominent outlines. However, as it continues to evaluate new images, it begins to get more and more accurate until it's capable of recognizing objects with extreme precision.

For a more detailed exploration of how AI promotes a proactive safety culture, see our whitepaper.‍

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2. Convolutional Neural Networks

A convolutional neural network is an algorithm used for deep learning. It takes an image, assigns specific biases to the objects within, and then learns to distinguish between different input images.

The name is derived from how neurons are connected within the brain, as the architecture of a convolutional neural network follows a similar architecture.

Convolutional neural networks capture temporal and spatial dependencies from an image by applying different filters, allowing them to identify things that might not be obvious to the human eye.

Over time, convolutional neural networks become much faster and more accurate than the human eye, as they can be deployed in larger areas and can focus on many objects at once.

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3. Video Content Analysis

The footage captured by a conventional CCTV camera can be processed through video content analysis. With the help of VCA, companies can implement specific safety rules, such as identifying if anyone crosses into restricted areas.

Objects in the footage can be detected and tracked through video content analysis, as it can identify spatial and temporal events in real time. VCA can also be used for face recognition and object discovery, classification, and segmentation.

Video content analysis allows companies to gather crucial analytical information about work processes, and it can help safety personnel identify patterns they hadn't previously focused on.

Section 3: 6 Benefits of Using AI to Improve Safety in the Workplace

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Many companies are already using AI safety tools to make workplaces safer and to reduce the burden on their safety personnel while also being compliant with regulations.

To understand how to convince senior management of the benefits of AI safety software, read our whitepaper.

Here are 6 of the many benefits that AI safety tools offer.

1. Automation

Arguably, the most significant benefit of using AI tools in the workplace is automation. This doesn't just mean automating dangerous tasks that pose a more substantial threat of injury, but also repetitive tasks.

For instance, AI safety tools can monitor all workers and ensure they wear protective equipment, contributing to a safer work environment. AI safety tools can also prevent people from walking into an exclusion zone by observing behavior and sending alerts when someone approaches within a defined limit.

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2. Reduced Risk of Human Error

AI safety systems get more accurate and more innovative as more and more information is fed into the system. There's a risk that a human may miss a minor detail, but AI safety systems aren't prone to human error.

This means your workplace will only get safer as time passes and the system continues to process and analyze new data.

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3. Improved Equipment Control

Safety personnel can define specific rules for taking appropriate steps before using dangerous machinery. Equipment control can ensure that only trained employees can use specialized machines.

More importantly, they can be configured to operate based on specific rules, such as if a qualified individual is present for supervision. This ultimately helps improve safety outcomes and prevents any mishaps.

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4. Predictive Insights with Real-Time Monitoring 

AI's role in workplace safety is transformative, offering predictive insights and real-time monitoring to prevent accidents. For example, this study demonstrates AI's potential in hazard identification and mitigation. Additionally, AI-driven training tools can adapt to users' learning styles, enhancing safety protocol compliance.

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5. Improved Employee Monitoring

Employers have a responsibility to ensure that they regularly train and educate employees about the importance of using proper safety equipment. However, if these standards aren't enforced, there's a risk that employees may not take it seriously. 

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This is where workplace safety monitoring technologies like wearable sensors come into play, integrating with existing systems to enhance compliance and safety.

Safety officers in fast-moving environments, such as a construction site, can only do so much. They cannot monitor every employee on the site without causing disruptions.

This also increases the risk of human error, as a safety officer may miss critical details. With AI systems, this isn't an issue.

AI safety systems connect with existing CCTV networks. They can process multiple data streams in real time and send alerts whenever rules are breached.

AI safety systems can be used to monitor:

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• Employee location
• Use of PPE
• Presence of environmental hazards
• Exclusion zones
• Fatigue monitoring

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Improved Decision Making

AI safety systems help break down complex data into easily understandable insights. They empower safety teams with the information required to make critical decisions.

Companies don't need a data scientist to understand important information. AI safety systems highlight specific changes and allow safety managers to isolate trends that could dictate the company's safety guidelines.

It offers an unparalleled insight into the level of risk in the workplace, allowing EHS (environment, health, and safety) teams to make decisions based on quantifiable data and then analyze their outcomes.

This also allows companies to conduct more effective safety audits, including using video evidence to determine specific trends and patterns. Over time, this information can help businesses determine how safety performance has evolved in the company.

Section 4: AI Safety – The Risks

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While AI safety offers many advantages, it's also equally important for companies to analyze the downsides and make sure they mitigate the risks.

These are sophisticated systems, and employers must ensure they understand the risks. Here are three main areas of concern.

1. Human Controlled with Benign Intent

Human-controlled AIs can be configured for specific purposes, such as detecting the use of PPE in the workplace. AI systems with benign intent are primarily used for supervision.

Such AI systems are used primarily for evaluating safety performance, and the data gathered can be used to improve decision-making. These can be further divided into:

• Non-robust: This is possible if the AI system works well on test data, but there's a significant difference in performance on other data sets.
• Privacy violating: AI systems must be designed to ensure that they do not violate the privacy concerns of stakeholders, including exposing any private or identifying information.
• Biased: The risk of biases is possible where the AI system exhibits bias towards specific objects.
• Inability to explain: The algorithm should be easy to interpret, with defined rules that govern its performance.

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2. Autonomous Learning, Benign Intent

AI safety tools are intelligent and autonomous, learning as more data is fed into the system. It's often difficult to determine how such systems will respond in practice, especially if a supervisor is absent.

In some instances, an interrupting agent may affect the ability of the system to be able to detect objects. It often takes more work to predict how the system might respond in dynamic environments.

There's also the risk of the system being hacked and tampered with, affecting its ability to perform tasks.

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3. Human Controlled, Malicious Intent

AI can be used for malicious purposes, so companies must take appropriate steps for data safety and security. Policies must be instituted to ensure that the data gathered is not misused.

Malicious intent, for instance, mass surveillance, does pose a risk as it can be misused in many ways. It's vital to devise specific governance policies and for companies to take steps to prevent this. ‍

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5 tips for managing and mitigating risks associated with AI in the workplace

Organizations need to take different steps to manage and mitigate the risks associated with AI systems in the workplace. Here are some critical risks and tips on how to mitigate them:

1. Cybersecurity Risks:

One of the most significant risks associated with AI in the workplace is cybersecurity. As AI technologies become more prevalent, cyber attackers increasingly target them as potential entry points to access sensitive data.

To minimize the risk of cyber attacks, it is essential to work with the IT team to implement strong security measures. This includes monitoring access to data, implementing multi-factor authentication, and encrypting sensitive information.

2. Ethical Risks:

AI can also create ethical risks in the workplace. For instance, AI tools may be designed to make decisions that impact employees, such as performance evaluations or hiring decisions.

As an EHS professional, it is essential to ensure that AI tools are designed and used in a way that is fair and unbiased. This could involve conducting regular audits of algorithms and making necessary modifications, as well as creating guidelines for the ethical use of AI in the workplace.

3. Health and Safety Risks:

Certain types of AI, such as cobots (collaborative robots), have the potential to improve health and safety in the workplace. However, they can also introduce new risks to employees, such as mechanical hazards, ergonomic issues, and exposure to hazardous materials.

It is crucial to conduct a risk assessment before introducing AI into the workplace to determine potential hazards and develop appropriate controls to mitigate them.

4. Privacy Risks:

AI technologies often require access to a significant amount of data, which can create privacy risks. Employees may feel uncomfortable with their personal information being gathered and analyzed by AI tools.

To address these concerns, it is essential to create clear policies for the collection, storage, and use of data. This includes obtaining employee consent and implementing robust data security measures to reduce the risk of data breaches.

5. Training and Awareness Risks:

Finally, AI in the workplace requires a high level of knowledge and skill to operate effectively. Without proper training, employees may not know how to use AI tools safely and effectively and may inadvertently introduce risks into the workplace. 

It is essential to provide ongoing training and awareness programs to ensure employees have the necessary skills to work effectively with AI tools.‍

Section 5: How to Integrate Artificial Intelligence in the Workplace to Improve Safety

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Companies have various options to integrate artificial intelligence in their workplaces. For instance, they can consider IoT (Internet of Things), which deploys micro-sensors to monitor machines, production lines, and employees.

However, this requires a significant upfront investment and may cause disruptions in work environments. 

In some instances, workplaces might have to be adapted before these sensors can be fully deployed, ensuring compliance with Occupational Safety and Health Administration (OSHA) guidelines for a safer workplace.

Instead, the best way to integrate AI into workplace safety is to connect an AI safety solution with your existing CCTV infrastructure. A video processing box can be connected to the feed, allowing for simple plug-and-play usage.

This ensures secure processing on-premises, allowing companies to take necessary steps to ensure the safety and security of the data. Once integrated, companies can define specific safety rules to start monitoring.

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Section 6: Ethical Considerations and Impacts Of AI In The Workplace

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‍In the evolving realm of workplace technology, AI's integration necessitates careful ethical consideration. AI's impact on workplace safety and security extends beyond compliance to upholding organizational trust and integrity.

Ethical AI deployment spans critical issues such as bias, privacy, transparency, job displacement, and reskilling. Addressing these challenges is crucial for responsibly leveraging AI's benefits and promoting a fair and secure workplace.

Bias and Discrimination

One of the significant ethical concerns around AI is the potential for bias and discrimination. AI systems are only as unbiased as the data they are trained on, and if that data is biased, the AI system will be biased as well.

For example, if an AI system is used to screen job applicants and is trained on data biased against certain groups (e.g., women, minorities, etc.), that bias will be reflected in the system's decision-making.

It's vital to ensure that any AI systems used in the workplace are trained on unbiased data and regularly audited to identify and address potential biases.

Privacy and Security

Another major ethical issue surrounding AI in the workplace is privacy and security. AI systems often collect and analyze large amounts of data. If that data falls into the wrong hands, it could be used to harm individuals or the company as a whole.

It's essential to ensure that any AI systems used in the workplace are designed with data privacy and security in mind and that appropriate measures are put in place to safeguard sensitive data.

Transparency and Explainability

As AI systems become more complex and sophisticated, they can be challenging to understand and explain. This lack of transparency can make it difficult to hold AI systems accountable for their decisions, which can be problematic from an ethical standpoint.

To address this issue, it's essential to strive for transparency and explainability in AI systems so that users can understand how the system is making decisions and how to interpret their results, thereby mitigating health risks associated with occupational hazards.

Job Displacement and Reskilling

Another ethical concern around AI in the workplace is the potential for job displacement. As AI systems automate more tasks, there is a risk that some jobs will become obsolete, potentially leaving workers without employment. 

To address this issue, it's important to consider job reskilling and other measures to help workers transition into new roles as the nature of work changes.

Fairness and Accountability

Finally, it's vital to ensure that any AI systems used in the workplace are designed with fairness and accountability in mind. This means ensuring that the system is transparent and explainable, as well as developing appropriate mechanisms for recourse if the system makes a mistake or behaves unfairly.

By ensuring that AI systems are designed and implemented in a responsible, ethical way, companies can ensure that they are maximizing the benefits of these technologies while minimizing the potential harm.

Regulation and compliance issues related to AI in the workplace

Deploying AI in the workplace involves data management and analytic capabilities. It is recommended that organizations conduct an internal assessment of regulatory compliance regarding AI deployment in their facilities to avoid compliance-related risks.

For instance, The EU General Data Protection Regulation (GDPR) and local privacy laws make it mandatory for employers to protect the personal data of their employees from disclosure to unauthorized entities.

AI programming and the related procedures must meet the security and privacy requirements of employee data.

Beyond this, the U.S. Equal Employment Opportunity Commission (EEOC) guidelines recommend vigilant scrutiny from an HR perspective when using decision-making algorithms for recruitment, selection, and performance evaluation.

AI algorithms use the personal data and behavioral patterns of employees in their decision-making process.

As per GDPR, individuals have the "right to be forgotten," which means that they can request that their data be deleted from all databases/programs that involve their data, which applies to AI as well.

So, data privacy should be addressed while incorporating AI in the workplace. Employers should take the utmost care while implementing AI in their organizations and conduct the necessary privacy assessments to guarantee that the AI system complies with all relevant data privacy regulations.‍

Addressing the Potential Displacement of Jobs Due to AI

AI forms the core of many technological innovations we see around us, such as chatbots, self-driving cars, and algorithms used in financial trading. These systems improve productivity and reduce the costs of various industries.

However, this also means that AI systems can automate many repetitive and routine tasks humans previously performed. This leads to a significant effect on job displacement, particularly in areas where these tasks are prevalent.

While we need to acknowledge the potential job displacement caused by AI, it must be noted that the impact will depend on how we choose to implement AI technologies.

Organizations need to implement AI systems responsibly, always considering the ethical and social impact of their adoption.

This means that companies must be mindful of the potential consequences on their employees and be proactive in finding ways to retrain those employees to work in other areas.

It also means that policymakers must set regulations that promote responsible AI implementation.

With automation and AI technology becoming increasingly common, organizations must train employees on new and emerging technologies to work alongside them.

There is a rapidly increasing demand for workers with the skills to design, maintain, interpret, and improve AI systems.

Therefore, organizations should provide ample opportunities for their workforce to learn and develop new skills that align with their future needs.

Along with this, governments must also create education and training programs that enable people to reskill and upskill appropriately.

While the rise of AI does come with the potential for job displacement, it also brings the opportunity for job enhancement.

AI can automate mundane tasks, allowing workers to focus on more critical, creative, and value-adding work.

This means we must shift the focus from job displacement to job enhancement – AI and automation can supplement human labor to make work more efficient.

Incorporating Human Oversight and Decision-Making in AI Systems

One of the main reasons why human oversight is crucial in AI systems is the potential for bias. Many AI algorithms are trained on datasets that contain preferences or incomplete information, resulting in decisions that perpetuate those biases.

For example, facial recognition software is less accurate at identifying people of color and women than white men. Human oversight can help to identify and correct these biases by providing feedback and monitoring the algorithm's performance over time.

AI systems often need to be recalibrated to ensure accuracy and transparency, which is why human oversight is essential. In case an inherent bias is detected or if the AI system isn't working as intended, human oversight can prove to be critical in identifying and resolving the problem in its initial stages.

Addressing security concerns and protecting against adversarial attacks on AI systems

AI systems are designed to make decisions using complex algorithms and vast data. One of the biggest challenges with AI is the potential for bias that can lead to errors in decision-making.

The algorithms can be manipulated or attacked by adversaries to exploit these biases, causing inaccurate decisions or, even worse, malicious outcomes. To address this concern, it's essential to implement a rigorous machine learning process that considers potential attack vectors, including data poisoning, model inversion, or evasion attacks.

You can use adversarial robustness tools, such as TensorFlow, which helps detect and mitigate these attacks and strengthens the security of machine learning models.

Another way to protect against adversarial attacks is to use multi-factor authentication( MFA) methods.

These methods require multiple forms of identification, such as a password and fingerprint verification, to access the system. This makes it difficult for attackers to access critical data, even if they can guess passwords.

To further strengthen the system, the biometric information used in MFA must be carefully selected to prevent reconstruction of the authentication database or fake image replication by the adversary.

Furthermore, organizations should conduct regular security assessments and cyber drills on their AI systems to identify possible weak points.

These assessments should include penetration testing and auditing software codes, network infrastructure, and data storage.

The results of these assessments should be used to improve system configurations and to address vulnerabilities or potential attack areas.

If organizations can identify these risks and address them before an attack occurs, they will be better equipped to prevent or mitigate the damages caused.

Addressing Societal and Economic Impacts of AI in the Workplace

AI is transforming many industries that were previously heavily reliant on human labor, such as manufacturing, logistics, and transportation.

While this transformation might lead to the loss of jobs, it also presents an opportunity for new job creation in other areas. The increased efficiency could also increase productivity and quality, leading to better products and services.

The economic impact of AI is significant, with the potential to increase productivity, growth, and employment rates.

However, the impact could vary widely between different industries and geographic regions, leading to polarization in the labor market.

It is essential to consider the broader social and economic implications of AI beyond the workplace to ensure that benefits are shared equitably in society.

Companies need to make sure that they address any concerns that employees may have and highlight the benefits that AI safety solutions offer.

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Section 7: The Best AI Safety Solution

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Protex AI is a workplace safety solution that leverages the power of artificial intelligence to help safety professionals make effective safety decisions.

It connects seamlessly with all modern camera systems. It can be customized based on your requirements, letting you define workplace risk.

Its plug-and-play nature means it can efficiently work with CCTV networks, big or small. Protex AI empowers EHS teams by providing them with essential insights about safety performance.

Safety events or rule breaches are recorded, tagged, and stored for review by teams, offering them evidence-based insights about the performance of safety protocols contributing to a safer environment.

It auto-generates safety reports and can automatically tag stakeholders or specific team members. The storyboard functionality allows EHS teams to create automated email workflows, add documents, or even record commentary to brainstorm and implement corrective actions.