May 11, 2018
Analysis predicting major societal problems caused by artificial intelligence (AI) surfaces every other day: how AI could be used to manipulate elections and launch drone attacks. The major fear seems to be that AI is set to make humans a redundant force in the workplace.
Yes, AI, like any evolving technology, is set to change our jobs, but could it also be the key to unlocking creativity and productivity in the business sector?
It’s clear that nothing is holding AI back. Apart from, perhaps, clear business cases and speed of adoption. Replacing an existing business process requires a clear investment case. The strongest cases are currently around big data management (e.g. fraud and risk management, security, financial trading) – essentially cases where someone is trying to make decisions with a large number of data points.
AI will change jobs. However, there are new jobs being produced by this change – for people that design, implement, and manage new disruptive technologies. These are highly skilled jobs that are better compensated than the ones being lost.
It’s also important to remember that any successful AI implementation requires a mix of technology, people, and processes. AI won’t replace the human workforce; it will just make humans more efficient. Some may choose to focus on AI as a threat – whether a robot can do the humans’ job better, faster, and cheaper – but the other approach is to figure out how artificial intelligence can give one’s business and clients an edge.
It’s widely accepted that repetitive decision-making is best handled by machines, partly because of the efficiency gains; but also, because the human brain is not designed to work in a ‘9 to 5’ mode. In the ‘always on’ financial payments world, with its panoply of channels and payment types, ‘9 to 5’ is simply not good enough. Internally conducted research with payments and fraud services shows that machines help to improve decision-making significantly and often find anomalies that highly skilled analysts don’t.
Even in sectors where non-stop workloads are not such an issue, machine learning is being adopted and embraced. For example, recruitment software makes use of AI in every part of the recruitment funnel. There are some very good reasons why recruiters and hiring managers are embracing AI, including reducing bias and improving candidate experience, by focusing on the human elements. Other use cases include commuting (traffic management), education (spotting plagiarism), social networking (combating fake news), and retailing (online shopping).
Like any technology, along with the positives, there will be negative concerns, including the possibility of the machine causing real harm due to fallibility in their design. For instance, if an automated vehicle were to be hacked and used in a harmful manner, or if a medical robot were to misdiagnose an illness, harming the patient. The list goes on, especially in the current political climate, where it has been claimed that political parties are utilizing personalized persuasion, taking advantage of publicly-available information, to target an individual’s opinions.
Some of these concerns are legitimate. Yet, there is no doubt in my mind that supervised machine learning tackles tasks in a more consistent manner than humans. However, as things stand, supervision (by humans) is still required.
Intelligent data analytics requires a balanced combination of both unsupervised and supervised learning. This makes it possible to learn in the best overall way, enabling the system to be applied to new challenges it has never encountered, within new domains, while applying the same underlying functions and methods it acquired at the start of its learning journey.
Research into behavioral economics shows that as humans, certain biases (decision fatigue, intentional blindness, and herd instinct) impact our ability to operate perfectly. As I suggested above, expecting our fraud teams to work through thousands of pieces of data and be spot on every time, is simply not practical. Using best of breed machine learning technology, many businesses I work with have significantly reduced the amount of time it takes to analyze data in addition to increased accuracy in fraud detection and a reduction in false positive rates, meaning fewer declines and more transactions.
The role of the human interpreting, analyzing, and understanding is still key to that process. As a result, I believe that there is a role for both humans and machines in the world of business. Instead of man versus machine, I believe the approach should be man AND machine.
I firmly believe that AI and machine learning will release human creativity and that freedom will, in turn, allow humans to realize the potential of the technology and its application.