The complete guide to AI safety in the workplace

How can AI help protecting your workforce? In this guide, we cover all aspects of workplace safety, and provide insights as to how Health and Safety managers can rely on AI and computer vision to ensure every worker can operate safely.

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November 3, 2022
5 mins


Section 1: Introduction to AI Safety

Workplace safety is an important consideration for modern businesses. 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 largely to the economic disruption caused by the global pandemic. The highest number of 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: 

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

As you can imagine, this isn’t an exhaustive list. In order to improve workplace safety, many companies are now using artificial intelligence to their advantage. 

The popularity of AI safety is largely 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 that 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 very real possibility. It’s also quite difficult for organizations 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, and can be used to identify risks or hazards in the workplace

Section 2: AI Safety – A Technical Overview 

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

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

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

Over time, they are able to 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. 

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

They can then send alerts, including notifications or even a text message 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 that exists 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.

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 based on their race, gender identity, or ethnicity.

It is vital to ensure that A.I. systems are designed to protect individuals' privacy, avoid unintended consequences, and promote fairness. Ensuring fairness in A.I. systems promotes 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 losing confidence in the technology.

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. 

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 the use of 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.

Here are some key technologies that play an important role in AI safety.

Computer Vision

Computer vision involves the use of artificial intelligence to enable systems and computers to extract critical information from digital imagery and videos, much like how 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 labelled 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.

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 the way neurons are connected within the brain, as the architecture of a convolutional neural network follows a similar architecture. 

Convolutional neural networks simply 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.

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 important analytical information about work processes, and it can help safety personnel identify patterns they hadn’t previously focused on. 

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

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. 

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

1. Automation

Arguably the biggest benefit of using AI tools in the workplace is automation. This doesn’t just mean automating high-risk tasks which pose a greater threat of injury, but many others. 

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

2. Reduced Risk of Human Error

AI safety systems get more accurate and smarter 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 that your workplace will only get safer as time passes and the system continues to process and analyse new data.

3. Improved Equipment Control 

Safety personnel can define specific rules for taking appropriate steps before using dangerous machinery. Equipment control can ensure that only specific employees with proper training are allowed to use certain machines. 

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

4. 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. 

Safety officers in fast-moving environments, such as a construction site, can only do so much. It’s virtually impossible for them to monitor every employee on the site without causing disruptions. 

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

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

AI safety systems can be used to monitor:

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

5. Improved Decision Making

AI safety systems help break down complex data into easily understandable insights. They empower safety teams with the information that they require to make key decisions. 

Companies don’t need to bring on a data scientist to understand important information. AI safety systems highlight specific changers 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. 

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Section 4: AI Safety – The Risks

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 it’s important for employers to 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 a possibility 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 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.

2. Autonomous Learning, Benign Intent

AI safety tools are intelligent and autonomous, and they learn 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 isn’t present. 

In certain cases, an interrupting agent may affect the ability of the system to be able to detect objects. It’s often difficult 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.  

3. Human Controlled, Malicious Intent

AI can be used for malicious purposes, which is why it’s important for companies to 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 important to devise specific governance policies and for companies to take steps to prevent this. 

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Managing and mitigating risks associated with AI in the workplace

It’s important for organizations to take different steps to manage and mitigate the risks associated with AI systems in the workplace. Here are some important 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 are increasingly targeting them as potential entry points to gain access to 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 important 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 consent from employees and implementing strong 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 that employees have the necessary skills to work effectively with AI tools.

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

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

However, this requires a significant upfront investment, and may cause disruptions in work environments. In certain cases, workplaces might have to be adapted before these sensors can be fully deployed. 

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 important steps to ensure the safety and security of the data. Once integrated, companies can define specific safety rules to start monitoring. 

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

Understanding and dealing with various ethical considerations and impacts of AI in the workplace is vitally important. Here are a few:

Bias and Discrimination

One of the major 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 that is biased against certain groups (e.g. women, minorities, etc.), then that bias will be reflected in the system's decision making. It's important to ensure that any AI systems used in the workplace are trained on unbiased data and regularly audited to identify and address any biases that may arise.

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, and 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 difficult 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.

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 important 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 harms.

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 compliances 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 the employer 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 personal 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 that 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 are capable of automating many repetitive and routine tasks that were previously performed by humans. 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 taking into account 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 their employees on new and emerging technologies to be able to work alongside them. 

There is a rapidly increasing demand for workers who possess the skills necessary 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 with it the opportunity for job enhancement. 

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

This means that we need to 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 biases or incomplete information, resulting in decisions that perpetuate those biases. 

For example, facial recognition software has been shown to be 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 important. 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 amounts of 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 takes into account potential attack vectors, including data poisoning, model inversion, or evasion attacks. 

You can use adversarial robustness tools, such as TensorFlow, which helps in detecting and mitigating 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 more than one form of identification, such as a password and fingerprint verification, to access the system. This makes it difficult for attackers to gain access to critical data even if they can guess a password.

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 in the system. 

These assessments should include penetration testing and auditing of 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 result in increased 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 tha they address any concerns that employees may have, and highlight the benefits that AI safety solutions offer.

Section 7: The Best AI Safety Solution

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

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

It’s plug and play nature means that it can easily work with CCTV networks big or small. Protex AI empowers EHS teams by providing them important 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. 

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

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