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An HR Algorithm Can Tell How Often You Will Change Jobs

BY Team PredictiveHire


Job-hopping algorithm – Assessing Job-Hopping Attitudes From Chat Interview

7:29. 149 TECHRADÓ NÓRA | Qubit.hu

We have no survivable and sustainable future without science, just as we do not have you without it. 

Since the start of the coronavirus epidemic, many companies have turned to smart algorithms to find out who is the best candidate for open positions. Most often, face-finding programs, games, quizzes, and software that examines other visual or linguistic patterns are used to decide who is included in the interview circle.

An Australian company called PredictiveHire, founded in October 2013, appears to have gone much further: it has developed a machine-learning algorithm to assess the likelihood of frequent job changes for a given candidate, the MIT Technology Review wrote this week.

According to Barbara Hyman, CEO of HR, their clients are employers who have to process a lot of applications and are active in the areas of customer service, retail, sales or healthcare, among others.

In the first round, a chatbot decides on the applicants

When someone applies for a job through an HR company, they must first “convince” a chatbot of their values. The algorithm asks a series of open-ended questions and analyzes personality traits such as initiative, intrinsic motivation, or resilience.

Moreover, the algorithm may examine the likelihood of frequent job changes in the future – or, as advertised on the PredictiveHire website, the “ risk of escape ” – even for fully career candidates. The focus of the HR company’s latest study is to develop a machine learning algorithm that specifically seeks to predict this. The research examined 45,899 candidates who had previously answered 5-7 open-ended questions about their experiences and situational awareness through the PredictiveHire chatbot.

They asked for personality traits that, based on previous research, such as PredictiveHire’s own research, may be closely related to frequent job changes, such as greater openness to new experiences or lack of practicality.

Algorithms against wage increases

Nathan Newman, an associate professor at John Jay College of Criminal Justice in New York who wrote a study in 2017 on how large-sample data analysis can be used to break wages in addition to discriminating against employees, told MIT Technology Review Recent work by PredictiveHire

This includes the increasingly popular personality tests based on machine learning, which seek to screen out potential workers who are more likely to support unionisation or are more likely to ask for wage increases. All this by saying, according to the MIT Technology Review, employers are increasingly keeping an eye on their employees ’emails, online chats, and any data they can use to filter out whether a particular colleague is about to leave and calculate what the minimum wage increase is. where appropriate, they may be allowed to remain.

Uber’s algorithm-based management systems are said to seek to keep employees away from offices and digital locations in a way that they can’t even accidentally organize and collectively demand better pay or treatment.

PredictiveHire has found a relationship between the language people use and their attitudes towards job-hopping.

If a simple automated chat interview can infer a candidate’s likelihood of job-hopping, it presents significant opportunities, especially when assessing candidates with no prior work history.

This work shows that the language one uses when responding to interview questions related to situational judgment and past behaviour is predictive of their likelihood to job hop. This paper explores:

  • Research around self-initiated job hopping
  • Correlation between language and job-hopping likelihood
  • NLP methods that can be used to represent language

Find out how you can identify job-hopping attitudes before you hire.  To get your copy of the Research Paper click here.

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