What is Machine Learning Safety?

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Businesses are turning to machine learning at an ever-increasing rate in order to automate tasks, boost efficiency and improve workplace safety. 

But as with any new technology, there are risks associated with its implementation. Machine learning can be used to ensure safety in the workplace by learning about potential hazards and alerting workers. 

However, to ensure machine learning safety, the model must be accurately trained. But, to understand machine learning safety better, it’s important to first know how machine learning improves safety in the workplace.

How EHS Teams Use Machine Learning

Machine learning is a subset of artificial intelligence which uses data to essentially “learn” certain things. Algorithms can be trained to identify, for instance, whether employees on a construction site are wearing safety headgear or not. 

For instance, companies that have CCTV networks can connect a visual processing tool that uses machine learning to detect if employees are wearing PPE or not. 

This is a more efficient option, as the system continues to get better and more accurate over time. More importantly, it negates the need for companies to hire safety officers, as the AI is more scalable and resource-efficient. 

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Ensuring Machine Learning Safety

While machine learning offers plenty of benefits, there are some important safety risks that companies need to be aware of, as discussed below. 

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Removing Bias and Improving Data Quality 

One of the potential hazards of machine learning is the possibility of bias creeping into algorithms. This can happen when data sets used to train algorithms are themselves biased. 

For example, if a data set used to train the system to detect hard hats on a construction site isn’t fed a diverse set of images, it might not be able to identify headgear of different colors or shapes. 

This can have serious implications for workplace safety, as biased algorithms may fail to identify potential safety hazards in high-risk situations. 

If the data that's being fed into the system is biased or inaccurate, the results of the machine learning algorithm will be, too. 

As such, it's important to cleanse and validate data before feeding it into a machine learning system. This will help to ensure that any biases are removed and that only accurate data is used.

To mitigate this issue, businesses should ensure that their data sets are as diverse as possible. Another way to reduce the risk of bias is to use what's known as "algorithmic debiasing." 

This is a process by which bias is deliberately removed from data sets before they're used to train algorithms.

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Predictive Errors

Another potential hazard of machine learning is errors in predictions made by algorithms. These errors can have serious consequences in the workplace if they go undetected. 

For example, imagine a predictive maintenance algorithm that's been trained on a data set containing faulty sensor readings. 

If the algorithm goes into production without being properly tested, it may make erroneous predictions about when equipment needs to be serviced. 

This could lead to unexpected downtime and increased costs for businesses. To avoid this type of situation, businesses should always thoroughly test their machine learning models before putting them into production.

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Security and Privacy Risks

Another consideration for businesses using machine learning is security and privacy risks. Since machine learning involves analyzing large amounts of data, there's a risk that sensitive information could be leaked if proper security measures aren't put in place. 

For instance, when using machine learning models to monitor workers in a facility, it’s important that the recordings are encrypted and stored in a secure environment. 

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How Protex AI Mitigates Machine Learning Safety

Protex AI is a workplace safety tool that’s been trained using a very diverse dataset, ensuring that it’s capable of detecting all types of unsafe events custom to your business facilities.

Companies can also create custom safety rules, using their own knowledge of risks in the workplace to teach the cameras what to detect and report. This helps mitigate risk further and gives organizations complete control over safety reports. 

Since the definition of risk generally varies for different companies and in different departments, this gives organizations the flexibility to define acceptable risk accordingly.