The combination of machine learning (ML) and AI in salary management could help HR to determine the “perfect salary” for employees. 

Pay equity legislation and the risk of equal pay claims is forcing employers to review the way in which compensation decisions are made. They also want salary decisions to reflect individual skills rather than the compensation level of a specific job, particularly where talent shortages exist.

As companies begin to explore the benefits of machine learning for compensation, we examine the role of ML and AI in establishing employee salaries. 

What is machine learning? 

IBM defines machine learning as: “a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.”

Machine learning drives modern technologies, including facial recognition software, virtual assistants such as Amazon’s Alexa, and shopping recommendations on websites.

The global machine learning market is expected to grow from $26.03B in 2023 to $225.91B by 2030. In comparison, The global AI market is predicted to rise from $164.99B in 2023 to $1,591.03B by 2030. 

Why use machine learning for fair compensation? 

The US still faces a talent shortage. Latest figures from the US Chamber of Commerce reported 9.9 million job openings, but only 5.8 million unemployed workers. 

Achieving pay equity can motivate employees and is essential to attract talent and boost your brand reputation. From building trust to attracting more qualified candidates to job listings, a commitment to fair compensation offers numerous benefits to your organization, while helping to improve employee engagement and retention. 

Determining what constitutes fair pay and internal equity, however, is a key issue for employers who struggle with challenges including: 

  • Difficulty in gathering and analyzing compensation data.
  • Unintentional bias in compensation decisions, resulting in pay disparities based on factors such as gender or race.
  • A lack of salary transparency. Two-thirds of workers would change jobs to work for an organization with greater transparency than their current employer. 

Using ML models for salary prediction can help to create a culture of pay equity. 

AI and supervised machine learning 

The algorithm used by AI in salary management is supervised machine learning. According to IBM, supervised ML is:

“…defined by its use of labeled datasets to train algorithms [that] to classify data or predict outcomes accurately. As input data is fed into the model, it adjusts its weights until the model has been fitted appropriately, which occurs as part of the cross validation process. Supervised learning helps organizations solve for a variety of real-world problems at scale.”

Exploring ML models for salary prediction 

ML models for salary prediction ensure that, if variables change, such as inflation, salaries are adjusted accordingly. The following studies demonstrate the potential of machine learning for fair compensation:

  • A 2022 study Machine Learning Based Method for Deciding Internal Value of Talent notes the importance of pay equity for employers and concludes that machine learning can mitigate conscious and unconscious bias as it is based on objective data. The result is a methodology that mitigates the gender pay gap and other diversity gap factors, while preserving internal equity and offering a more acceptable salary to individual employees. ML thus enables employers to make the most equitable and least biased data-driven decisions around compensation. 
  • A further study examined three supervised ML models for salary prediction, namely linear regression, random forest, and neural networks. Neural network (NN), a method in AI that teaches computers ttao process data in a way inspired by the human brain, outperformed other ML models in this example. 
  • A 2021 study also used neural network models to predict annual wages by estimating the value of skills for skill-based compensation systems. It aimed to help employers determine how much to compensate for different combinations of skills and also to help employees estimate what their salary should be. These NN models could also be used to support the development and analysis of labor policies and understand the connection between skills and wages. 

Benefits of AI in compensation management 

AI and ML offers several advantages to employers in setting salaries:

  • AI in salary management can analyze vast amounts of data to identify patterns, trends, and anomalies. Employers can then make informed decisions about pay ranges, bonuses, and other forms of compensation. Pay equity trends and disparities are identified, allowing employers to take action to mitigate potential issues. 
  • Fair pay is ensured as the risk of errors in compensation decisions is reduced. In evaluating compensation, AI can consider factors including an individual’s specific skill sets, experience, and the typical salary for their role so compensation packages are personalized. By staying on top of compensation trends, employees receive the “perfect” salary. 
  • AI could become a significant tool to ensure compliance with pay data reporting. Analyzing employee data helps employers to create equitable, explainable and competitive salaries, while complying with pay equity legislation. 
  • AI’s predictive capabilities help employers to compete in a volatile labor market. By analyzing trends in compensation across different sectors and locations, AI enables HR to anticipate change and adjust compensation accordingly. 

Ensuring ethical AI in pay equity 

The rapid rise of AI, while offering significant opportunities, also raises concerns. An IBM study found that nearly three quarters of businesses fail to reduce unintentional bias in their AI solutions. In a move to ensure the ethical use of AI, EEOC recently updated Title VII guidance. Employers can ensure compliance and ethical AI in pay equity by partnering with a trusted pay equity software provider

What’s next for AI in salary management? 

In the light of concerns over AI bias, Gartner predicts that human-centered AI (HCAI) could benefit people and society by improving transparency and privacy. “HCAI enables vendors to manage AI risks, and to be ethical, responsible, and more efficient with automation, while complementing AI with a human touch” and removing limitations, biases, and blind spots. 

For now, the use of ML and AI in salary management remains largely unchartered territory. While challenges are expected, the benefits in areas including pay equity are clear. 

Pay equity software carries out an audit on your compensation structure to identify pay disparities and trends. Carrying out an intersectional pay equity analysis with Trusaic PayParity® enables your company to identify the root causes of pay equity and carry out remediation. Those insights enable HR to make better informed decisions around salary, promotions and pay raises. 

Speak to one of our pay equity experts today. 

Download: Pay Equity Definitive Guide