On the 16th of July 2021, I was featured in a VentureBeat article by Kyle Wiggers, titled: AI Weekly: Can AI predict labor market trends?. The article discusses the growing area of labour market analytics. It covers the common methods and datasets that we use in the industry to make job market predictions. The article also discusses the various shortcomings of these approaches, where I’m quoted:
“The challenge with predicting anomalies is simply that they’re hard to predict! An anomaly is something that deviates from the norm. So, when you train machine learning models on historic data, the future predictions are a product of that past information,” Dawson said. “This is [especially] problematic when ‘black swan’ events occur, like COVID-19 … Supply-side data are important for understanding what’s actually going on with workers, but they’re lagging indicators — it takes time for the data to reflect the crises that have occurred.”
There is also a discussion of the risks of bias and discrimination in predicting trends from labour market data:
“As Dawson notes, the risks are high when it comes to bias in labor market predictions. In HR settings, prejudicial algorithms have informed hiring, career development, and recruitment decisions. There are ways to help address the imbalances — for example, by excluding sensitive information like race, gender, and sexual orientation from training datasets. But even this isn’t a silver bullet, as these characteristics can be inferred from a combination of other features.”
The article concludes with future opportunities for labour market analytics, where I’m quoted discussing my optimism about Reinforcement Learning applications:
Dawson said he’s optimistic about what reinforcement learning might add to the mix of labor market predictions. Not only does it better reflect how job mobility actually occurs, but it also lessens the risks of bias and discrimination in job predictions because it’s less reliant on aggregated historic training data, he asserts:
“[Reinforcement learning is a] goal-oriented approach, where an agent (say, an individual looking for a job) navigates their environment (e.g. job market) and performs actions to achieve their goal (e.g. takes a course to upskill for a target career),” Dawson said. “As the agent interacts with their environment, they learn and adjust their actions to better achieve their goal; they also respond to an environment that dynamically adjusts (e.g. a labor market crisis). This approach balances ‘exploitation’ of an individual’s current state (e.g. recommending jobs strongly aligned with their skills and previous occupations) with ‘exploration’ of new paths that are different to an individual’s state (e.g. recommending jobs that are new career paths).”
Thanks, Kyle for the interview! Check out the full article here.