Ten professional jobs threatened by advances in big data and machine learning
1) Mental Health and Substance Abuse Social Worker (Chance of being automated: 0.3 per cent) If you're working in a healthcare job that requires a lot of direct interaction with patients, there's probably no need to ing over your shoulder. be look- Sure, there are surgical assistive robots, but a surgeon's job is pretty safe, according to Oxford University. There are a lot of legal issues that would arise from putting a patient’s life in the hands of a medical robot that might malfunction and make a wrong decision. (Chance of being automated: 0.4 per cent) Again, there are just elements of healthcare that robots just aren’t capable of handling: bedside manner, making tough decisions from incomplete patient data, dealing with human psychology, etc. (Chance of being automated: 0.4 per cent) Some healthcare jobs that are being automated more include hospital delivery and pharmacy technicians. (Chance of being automated: 0.4 per cent) The key here comes when teaching subjects that aren’t as objective as science and math. Would a computer be able to understand the nuances of music, art and literature, let alone sophisticated databases can use big data techniques including syntactic analysis and keyword recognition to accomplish the same tasks in much less time. In fact, it’s likely that a Watson-style machine learning system could be legally “trained” to review precedent and case history and even draft legal briefs — which has traditionally been the job of lower level law firm associates. But don’t think it’s only the lowly junior associates whose jobs are at risk: lawyers are well paid now to predict the outcome of major cases, but a statistical model created by researchers at Michigan State University and South Texas College of Law was able to predict the outcome of almost 71 per cent of U.S. Supreme Court cases. That ability to predict outcomes is possibly the most valuable (and lucrative) service lawyers provide, and it was easily matched by a computer.
10. Law Enforcement
Predictive policing is a hot-button topic. Many critics say that predictive policing is an infringement of civil liberties, but it’s not all as “Minority Report” as many people believe. In 2003, the same sorts of algorithms retailers like Wal-Mart use to predict demand for products was used to predict demand for police presence in New York City on New Year’s Eve, and the results were striking: 47 per cent fewer random gunfire incidents, and a $15,000 sav- teach it in a subjective manner? (Chance of being automated: 0.5 per cent) Psychology is a profession where human touch is certainly preferable, especially in school settings. Working with robots might not be soothing, which is why psychologists are safe. (Chance of being automated: 0.5 per cent) The purpose of a medical scientist is to discover new methods of enhancing human health. This requires running clinical trials, interviewing patients and going through their medical histories. (Chance of being automated: 0.6 per cent) This job requires near- constant collaboration with others, which would make it near-impossible for robots to perform. And computer systems analysts tailor technology needs to the company at hand, so the job is never the same. ings in personnel costs during the 8-hour period. Better risk prediction could decrease the number of officers needed at any given time and for any given department.
Reality
Computers threaten more than low-skill jobs like factory workers, retail clerks, and waiters. As computers become exponentially more sophisticated, it naturally follows that they will be able to perform more sophisticated work. This will be a boon in many industries with increased accuracy and productivity. Any doctor would tell you that more accurate diagnostics are a good thing, and any lawyer would agree that faster, more comprehensive discovery is a benefit to the legal process. The problem, however, lies in the fact that these technological revolutions might not create as many jobs as they eliminate. Certainly we will need more programmers, statisticians, engineers, data analysts and IT personnel to create and manage these sophisticated computers but it might be difficult to tell a factory line worker or taxi driver to shift gears and become a data analyst. How we fill the gaps when jobs are replaced will be the deciding factor as to whether all this automation is good for humanity or not.
(Extracts from an article by Bernard Marr, best-selling author, keynote speaker and leading business and data expert)