The Rise of Human-Led Automation

Data is the most important resource an organization has. Data allows us to make informed decisions. It provides important insights to our customers and the experiences we provide. Helps create operational efficiencies that lower costs and increase profit margins.

But for now, we’re overwhelmed with data. We have so much that it becomes difficult to sort out the good, relevant data we need from the noise we don’t. We spend a fortune collecting, managing, and analyzing data across the company, but we don’t see a return on investment.

Fortunately, automation powered by artificial intelligence (AI) and machine learning (ML) helps us better engage with our data. The software can now search large data sets to identify relevant data for each purpose. It doesn’t matter if we’re drowning in data – machines will tell us what’s good and what’s bad.

Or so we thought. Perhaps automation is not the magic bullet we think it is.

Machines problem

At the most basic level, automation employs a machine to perform repetitive tasks by heart at a cost that is lower than a human. Whether it’s a press cut on thousands of identical circuits or the AI ​​recommends the following video, the principle is the same.

The digital age has brought trivial conveniences like reminders to order more laundry detergent for life-saving operations like matching donors. None of this is possible without automation. But machines can only get us there 90% of the time. They are great at consuming and analyzing large amounts of data but still have trouble in high-end cases. Sure, we can continue to train algorithms to cover more of these exceptions, but at some point, the number of resources being developed starts to outpace the benefits.

This ability to easily and seamlessly apply known principles and standards to sophisticated situations is what distinguishes humans from machines. We are meticulous thinkers. We can look at an instance and make the best judgment decision that is almost always correct. Convergence machines. They look at the whole and decide based on how similar use cases have been handled previously, often with poor results.

And herein lies the paradox of AI: the more we automate data analytics, the more work will be required of humans to cover cutting-edge cases, provide high-level screening, and put meaning behind insights.

The rise of human-led automation

To drive AI in a reasonable, efficient, and ethical way, companies need to allow machines to do what they excel at while making sure that humans are there to provide oversight. Based on interpretable AI, which is the idea that outcomes must be understood and explained by humans, this is an ongoing process cycle that requires participation in every stage of AI from problem definition and development to ongoing data governance.

Here are three considerations for bringing the human touch back into AI-powered solutions:

1. Set company values

AI is as good as the data you feed it to. If existing processes are implicitly biased, any algorithm based on these historical precedents will transfer these biases to automated processes. Companies must first identify the values ​​they care about, ensure human compliance, and then apply those values ​​to automated processes.

2. Putting the human being at the source of education

In machine learning, artificial intelligence creates and trains an algorithm without human intervention. Machines have neither morals nor morals, and they cannot make judgments. All they know is what they have been taught, and like the game of telephone, these lessons tend to lighten the further away from a human. Making human training algorithms is mutually beneficial. Humans can identify and mark sophisticated states of machines while machines take on many of the tedious manual tasks.

3. Ensuring human-led governance

AI models need to be constantly monitored, measured, and recalibrated. Left alone, these models can shift inadvertently based on external factors. These shifts, called drift, can lead to unintended and undesirable outcomes. Likewise, ethical AI, a component of interpretable AI, ensures that machines operate under an ethical system or principles defined by developers. If the models drift far enough, they may lose their ability to behave as intended. While machines can monitor the drift, any issues that arise must be escalated to a human who can make a judgment on whether or not to intervene. Subsequent training must also be handled by humans, ensuring that the algorithm is recalibrated to return optimal results. It is clear that humans – with appropriate subject matter expertise – are the best judges of the drift model. Only they, not machines, have a high level of experience, cognitive ability, and understanding of the nuances of making these judgment decisions.

Keeping machines honest requires a human touch

AI has the potential to change the way we work, live and play, but we still need humans to instill the common sense and stewardship that only people can provide. Bringing the human touch back to automation requires a commitment that begins with defining and instilling company values ​​and continues through algorithm development, training, and ongoing governance. Machines will someday play a much bigger role in our daily lives, but we still need humans to keep their faith.


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