A Blog by Jonathan Low


Mar 5, 2018

Artificial Intelligence Is Coming After High Skill Jobs - But Penetration Slower Than Predicted

The available technologies have been complementary rather than revolutionary. So far. JL

Sascha Eder reports in Venture Beat:

Medicine, recruiting, and law require advanced degrees and generally net impressive salaries. But another thing they have in common is that AI has become sophisticated enough to severely threaten their very existence. Startups are offering AI services that can replace entire professions with lower prices and more precise results. (But) machines trained through precedent will invariably hold onto human biases.
When automated technologies first emerged, there was a gap between the laborers they replaced and the decision-makers who implemented them. But as AI has matured, it has begun to climb the corporate ranks.
Now disruptive startups are offering AI services that can replace entire professions with lower prices and more precise results.

The incumbents: recruiting, medicine, and law

Medicine, recruiting, and law require advanced degrees and generally net impressive salaries. But another thing they have in common is that AI has become sophisticated enough to severely threaten their very existence.

1. Medicine

In 2013, the FDA approved Johnson & Johnsons’ AI anesthesiologist. The machine, named Sedasys, costs only $150- $200 per procedure (as compared to the $2000 it costs with an anesthesiologist). Anesthesiology is one of medicine’s highest-paying jobs — the median salary is $352,518 per year, and it requires four years of training beyond medical school. It’s not particularly surprising, then, that anesthesiologists were staunchly against the newborn machine. The American Society of Anesthesiologists campaigned aggressively against it until its uses were limited to routine procedures, such as colonoscopies. Then, in 2016, the machine was discontinued altogether due to poor sales. Despite the myriad cost and precision benefits it offered, hospitals were reluctant to replace their highly valued anesthesiologists.
This is not the only instance of an insurgent machine attempting to replace a high-paying position in medicine. Machine learning algorithms have been able to detect the presence or absence of TB in X-rays with 96 percent accuracy, which is higher than any human radiologist. Researchers with Google trained AI to detect the spread of breast cancer on microscopic images as accurately or better than human pathologists. A recent article published by the Journal of the American College of Radiology (JACR) envisioned a future in which AI becomes a routine part of radiologists’ daily lives. And just last week, Alphabet’s health company Verily announced that it had developed an AI eye scan that can predict cardiovascular risk factors as accurately as a blood test. While none of these machines has been widely implemented, and certainly none has replaced humans (yet), the ability for them to do so is certainly there.

2. Recruiting

If Netflix can suggest personalized shows for its users, Uber can match drivers with passengers, and Facebook can personalize its News Feed to users’ interests, then the recruiting industry can use AI to match jobs with candidates. And yet, despite the relative simplicity of this task, recruitment is still a highly manual field, with new hires costing upwards of $4,000 over a six-week hiring period. What this means is that there’s a massive market for AI-powered recruitment disruptors. Companies around the world are currently wasting money on an inefficient process that results in high turnover and wasted time. In fact, over half of talent acquisition leaders say the hardest part of recruitment is identifying the right candidates from a large applicant pool, a task that is extremely easy to automate. Companies such as the much-hyped Woo have adopted this solution, using matching algorithms to cut down the time to first candidate profiles to under 24 hours. Notably, the company’s most high-profile customers are all similar disruptors, like Uber, Lyft, WeWork, and Wix.
But even systems like Woo work in tandem with more manual recruiting software, such as that provided by Lever and Greenhouse. In fact, it does not replace recruiters so much as streamline one aspect of their jobs. While this is threatening, it’s not so extreme that recruiters would refuse to use it.

3. Law

AI lawyers have already begun infiltrating the legal profession. There are myriad robo lawyers for hire, from the fairly mundane LawGeex, which automates contract review, to IBM’s truly threatening Ross, which combs through legal documents, offers hypotheses with citations to back them up, and keeps up to date with relevant legal developments. And many of them sound a lot like junior associates.
DoNotPay — a chatbot that gives free legal advice to help consumers resolve issues with parking tickets, landlords, and retailers, among others — set out to bypass the legal profession. “The legal industry is more than a 200 billion dollar industry, but I am excited to make the law free,” said the chatbot’s creator. “Some of the biggest law firms can’t be happy!” Another AI-powered law tool was able to predict the verdicts for hundreds of cases heard at the European Court of Human Rights with 79 percent accuracy.
That said, while AI may be able to replace the need for lawyers when it comes to parking tickets, it’s unlikely that it will eradicate the profession. This is particularly true considering that interpretations of the law change over time, and without human training data, a machine would not be able to adapt. Furthermore, machines trained through precedent will invariably hold onto human biases, such as racism and sexism. In fact, in 2016 multiple news outlets revealed that a software that predicted the likelihood of repeat offenses was mistakenly flagging black defendants at almost twice the rate of white defendants.

Why disruptors haven’t gotten market penetration

Those with the most power to adopt automation are also those with the most to lose by adopting it. Despite a machine’s ability to administer anesthesia with greater precision than a human, no anesthesiologist would ever encourage the use of this machine, especially considering that many doctors have extremely burdensome student loans.
This is indicative of a larger problem with automation. Processes that save time on menial tasks will be successful; those that position themselves as robo versions of professionals with the power to make decisions will likely not.
In the end, whether a company succeeds at infiltrating highly skilled jobs with automation will depend upon messaging. DoNotPay was successful only because it went straight to consumers. If it had used the same messaging with firms, it would likely not have been implemented. If more companies choose to follow this direct-to-consumer route, there will need to be policy changes to protect targeted professions. After all, a world without doctors, lawyers, or recruiters would be lacking far more than just these professions — we would see a ripple effect in graduate programs, professorships, and general employment rates. As AI becomes increasingly sophisticated, we will either need to curb its applications via policy or we will have to completely overhaul our income and employment infrastructures.


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