Safety technologies: what planet are we on?

For decades researchers have reported the difficulties workers encounter when identifying risks and controls in the field. In response, miners have increased their investments in training alongside a plethora of related initiatives.

Despite these efforts, the most recent research suggests even highly trained workers and experienced superintendents cannot identify all relevant risks and controls.

Of greater concern is there’s a lack of correlation between training and experience and risk identification.

Which raises the question – if the problem is only human, why don’t training and experience solve it? Because the problem is not just human, it is also mathematical.

Workers must continuously navigate infinite and dynamic in-field scenarios. If a worker can only encounter a maximum of 20 risks and employ a maximum of 20 controls, they must safely navigate more than 1 trillion possible in-field scenarios (combinations of risks and controls).

Of course, workers regularly encounter many more risks and controls and most employers have identified hundreds of each.

To put these numbers in perspective, our galaxy has about 300 billion stars. When we ask workers to select the correct combination of risks and controls, we are asking them to select the single safest star from amongst all stars across at least three galaxies – and reselect when conditions change.

At the same time, compliance inspectors have more than one trillion chances of finding a safer star. This is not just food for thought in terms of how we manage non- compliance, it’s a safety challenge that’s mathematically astronomical.

The implications for today’s safety systems are deep and wide. As just one example, it means our long-held suspicions about the inadequacies of some in-field safety templates were accurate. Pre-work checklists not only generate tick and flick responses, they fail to prompt or support workers to consider the domino effects of risk correlations across tasks, environments and change. The greater the reliance on pre-work checklists, the greater the need to consider every possible in-field scenario.

Similarly, the greater the reliance on blank templates populated in-field (such as risk assessments) the greater the need for real-time intelligence that prompts and supports workers to select the correct combination of risks and controls.

The implications for the future nature of work and resulting safety systems are even more profound.

For AI to replace workers, a minimum amount of data is required per in-field scenario. In contrast to the assumption technology will soon remove people from harm’s way, it’s much more likely we’ll see increasing concentrations of workers in unpredictable environments. Indeed, in June 2018 the McKinsey Global Institute forecast that demand for occupations involving unpredictable environments would continue to grow until at least 2030.

Over and above this, given the social impacts of workplace deaths and injuries, society will hold AI-based safety systems accountable to the highest standard of data integrity and a zero tolerance for ‘black box’ algorithms.

On the upside, miners are increasingly collaborating. As this collaboration matures it will standardise those industry processes with the least competitive differentiation (such as the design of safety templates) and consolidate the technologies and data used to manage those risks with asymmetrical outcomes (including safety).

In light of the above, miners have three clear safety priorities.

First, seek out technologies that support and enable workers to navigate infinite and dynamic in-field scenarios; they’ll add the greatest value in the short term and beyond 2030. Second, avoid bespoke safety templates and the technologies designed to build and distribute them. It’s unlikely you’ll recover development costs – far less supplier induction costs – before safety templates are standardised, and you’ll be left carrying unnecessary maintenance costs and a lone-ranger dataset.

Finally, don’t wait for AI. Get ahead of the concentration curve by collaborating with suppliers and competitors to standardise templates and technologies and to share data. Remember, collaboration doesn’t require industry-wide agreement – just two parties can halve risks and costs.

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