Thanks to popular media, Artificial Intelligence (AI) has earned the reputation of being a job killer. However, AI is nothing but a tool and the end result depends on the intent of the user. For most of us, we choose careers because of passion, but drudge work becomes a barrier between us and creative control. Artificial Intelligence, on the other hand, can tear down those walls and empower workers.

If you’re curious about how AI works, and its subsets Machine Learning (ML) and Deep Learning (DL), you can read our primer here.

Let’s look at 6 examples of how Artificial Intelligence empowers workers.

Medical Industry: Prediction and Diagnosis

Source: Pixabay

Prevention is better than cure and what better way to prevent diseases than to predict the probability of it manifesting. With the ability to process through bigger data sets, machines could pick up symptoms quicker than doctors and it wasn’t long before they capitalised on AI. Recently, the Veteran Administration of the United States worked with Google’s parent company Alphabet’s DeepMind unit to create software that helps predict which patients are likely to develop Acute Kidney Injury.

In addition to that, researchers from the University of Alberta, Canada used an ML model to diagnose and seek for patterns from 81 patients who were diagnosed with schizophrenia but were yet to be treated with antipsychotic drugs. The model known as EMPaSchiz (read as ‘emphasis’) had a 90% accuracy rate, the best model so far and the only model that used data from patients that hadn’t used antipsychotics before.

When repetitive diagnosis is taken over by AI, doctors are now able to prescribe treatment to patients before the conditions become chronic and build interpersonal relationships with them.

Social Sciences & The Humanities: Analysing and Deriving Meaning From Literature

Source: Ars Technica | Depiction of a trial in London’s Old Bailey Courthouse (1809)

In the humanities, reading vast amounts of literature is part of the job. This, unfortunately, requires either extensive training or time-consuming research. Researchers parse through and cross-reference old documents in order to derive meaning but with ML they can now look for similarities thematically.

Historian Tim Hitchcock from the University of Sussex did that when assessing 200 years of London’s crime records. His goal was to see the patterns of how people spoke of different crimes and how authorities treated suspects.

Stanford University historian Caroline Winterer analysed the letters of Benjamin Franklin to create unseen connections between the American founding father and the rest of the political world. Rather than just spending time reading materials that might not lead to satisfactory conclusions, academicians can now focus on reconstructing history or test social theories.

Legal Industry: Drafting, Managing and Reviewing Contracts



Source: Forbes

A large portion of the work in the legal industry is reading through contracts. This meant that lawyers have less time to give legal advice. With the help of software companies like Kira Systems, LawGeex and eBrevia, lawyers are able to search vague or questionable terms and assess if contracts containing those terms are worth signing. The lighter the burden of a lawyer, the easier the learning curve to contract law.

In 2017, four Harvard Law School students developed a search engine named Evisort with the goal of using Artificial Intelligence to simplify the labour-intensive contract review process. In reaction to the search engine, lawyers said “In six seconds they can review a 30-page contract and pull out information for you. Why did I spend 10 years of my life doing that?”

Journalism: Transcription and Template Automation

Source: Pixabay

Journalism as a career seems fancy and creative from the outside, but in reality, journalists aren’t free from repetitive drudge work like transcribing interviews and writing mundane procedural reports. Popular publications like Associated Press uses Artificial Intelligence to automate templates based on information gathered by journalists. By the end of 2019, they aim to create more than 40,000 stories. This frees up time for journalists to focus on long forms.

Due to a large number of stories out there, a huge chunk of a journalist’s job is to gamble on what stories are worth chasing. Editors need to be selective about where they should deploy journalists. While some may have developed a sixth sense, not everyone has the luxury of time to learn that.



With the help of ML and DL, editors are now able to see the probability of what stories are worth pursuing. Sites like FiveThirtyEight and Vox heavily incorporate data journalism to tell large stories, that regular reporting could not.

Public Policy: Prioritising of Resources

There are two ways to govern; by asking the people what they want, or by collecting the information on what the people need. Unfortunately, when there is low political participation from disadvantaged groups, governments have to rely on intense, resource heavy information gathering. Here’s where data prediction can come in and save time for census work.

Researchers at Stanford University navigated around this lack of data by mapping the presence of night light. Researchers are able to map out as areas with access to electricity and by proxy, the existence of economic activity. The Artificial Intelligence software contrasted day and night satellite images and transferred the information to create a poverty map. Without AI, the scarcity of information would mean this would take years of census work. Politicians and policymakers can now make decisions on what issues to prioritise, with what little information they have.

Banking & Finance: Risk Assessment

Source: Pixabay

The problem with lending money is the risk involved. While there are methods like asking for employment records or checking credit scores that help people make decisions, some bad borrowers still slip through the cracks. With quicker and better methods from ML, banks are able to detect defaulters with greater probability.

The world of stock markets requires quick decision-making skills. Normally stockbrokers have to keep an eye on the tiniest details on what happens in the market and move money accordingly. However, this involves a lot of risks as humans are prone to error in judgement. Sometimes, a hundred other stockbrokers might follow the wrong decision of a single rogue lead due to fear of missing out. Artificial Intelligence on the other hand base everything on past data and probabilities, so investments are safer. This is exactly what companies like “I Know First” does. As their name aptly implies, the company uses software that predicts leads or follows other leads according to probability. For now, stockbrokers can choose whether to put their money on predictions, but it isn’t hard to imagine a future where automation is the norm. From there, businesses could move forward, plan strategic partnerships, and improve their business models rather than constantly worrying whether they’ve made the right investment.