Artificial intelligence takes on white-collar duties
Maybe it’s unfair that some people think tax lawyers have the personality of a robot, but Benjamin Alarie considers that to be a plus.
Source: DAVID ISRAELSON
A Yale-trained lawyer himself, Mr. Alarie’s Toronto firm, Blue J Legal, harnesses artificial (or augmented) intelligence (AI) to help lawyers and their clients work their way through the complications of tax law.
“It’s a way to supercharge the legal system. We take hundreds of cases on different legal questions and train AI on how the courts make those decisions, so users can run predictions on how the courts might decide a new case,” he says.
Blue J Legal is at the cutting edge of a wave of new uses for AI. Robots, which have already taken over manual labour and factory work, are finding their way quickly into white-collar and professional jobs that require judgment and thinking.
“I think the nature of most white-collar jobs will drastically change in the future because of AI,” says Henry Kim, associate professor of operations management and information systems at York University’s Schulich School of Business in Toronto.
“It’s not to say that all the professional jobs will go away, they’ll just be different,” he says. AI is not only worming its way into law, but also finance, medicine and complex areas such as the development of new pharmaceuticals.
In finance, “Artificial intelligence can help people make faster, better and cheaper decisions. But you have to be willing to collaborate with the machine, and not just treat it as either a servant or an overlord,” says Anand Rao, a partner at PwC Analytics and expert in AI.
“Each sector applies AI differently,” PwC’s Financial Services Institute says in a recent report.
“For example, insurance leaders use AI in claims processing to streamline process flows and fight fraud. Banks use chatbots to improve customer experience. In asset and wealth management, AI adoption has been sporadic, but robo-advisors are rapidly changing that,” the PwC report says.
In the pharma sector, “computational drug discovery has actually existed since the 1970s. It’s not necessarily new thinking, but with the advent of AI there are unique opportunities,” says Naheed Kurji, chief executive officer of Cyclica Inc., a Toronto-based company that is harnessing AI.
“We believe that the old way of discovering medicines is inefficient and largely broken. There is opportunity to use computational powers to get better medicines in the hands of consumers faster and at lower cost.”
He points out that since the 1970s, the power of computers has increased more than a hundred-millionfold, or eight orders of magnitude.
“Even the iPhone 4 – already superseded by three newer iPhones – has more than double the computing power of the Cray-2, the world’s fastest supercomputer back in 1985,” Mr. Kurji says.
AI is taking this computational clout even further by adding a wider range of intuitive thinking to robotics than the traditional binary, yes-no deductions.
For the drug industry, “to put it into perspective, the medicines we take interact with many aspects of our biology – some intended, accepted and understood, some not. We all know the latter as side effects,” Mr. Kurji says.
AI can look at multiple, sometimes unanticipated, side effects and develop the formulas for drugs faster.
“We need quicker, more robust decisions when it comes to regulatory approval,” Mr. Kurji says. “It keeps taking longer to determine the safety and efficacy of medicines, outside of those for ‘blockbuster’ diseases like cancer and heart disease.
“There has been an eightyfold decline in productivity [the time it takes for a drug to be approved] between 1950 and today,” Mr. Kurji notes.
Like Mr. Alarie and Dr. Kim, Mr. Kurji believes AI is augmenting, not supplanting, traditional professional insight and advice. Cyclica’s scientists are not robots’ custodians: “We are experts in biophysics, bioinformatics and computational biology, with strong supporting capabilities in machine learning.”
Rather than giving up, Dr. Kim thinks that professionals will need to adjust. Doctors, for example, already use computational knowledge to look at patients’ symptoms and see where they fit in the spectrum of previous patients who have shown up in waiting rooms with similar problems.
“When AI analyzes collective knowledge from medical journals without bias and this is accessible, then the doctor knowing more may not be as much of an advantage. Creativity, empathy, flexibility, common sense and thinking on your feet become more important,” Dr. Kim says.
“These are all things that AI will not be able to do well for quite a while.”
Similarly, he thinks there are many aspects of law that even the most sophisticated AI today will miss.
“The law has a lot of subtleties. In contract law, for example, people try to get to the intention of the contract – what did the two sides really mean when they signed?” he says.
This requires intuitive skills that are beyond the range of AI – for now.