- Bias in promotion decisions. More than 75% of employees believe their leaders show bias in promotion decisions and 60% believe their leaders only promote people who share their same opinions.
- Artificial intelligence helps reduce bias. AI can identify and eliminate discriminatory or irrelevant factors in the employee evaluation process, such as age, gender, race or appearance.
- AI isn’t error-proof. Because it’s developed by humans, AI has room for error, including programming it based on internal biases.
There are opportunities and use cases for organizations to lean on AI tools to improve their compensation and rewards processes. When it comes to promotion decisions, AI — if leveraged correctly — could actually reduce bias in decision making.
Data from eLearning Industry found that more than 75% of employees believe their leaders show bias in promotion decisions and 60% believe their leaders only promote people who share their same opinions.
Additionally, a study by Amdocs found that nearly 60% of employees between age 25 to 34 report bias in talent mobility, while 41% of employees between age 45 to 54 report the same experience.
Pay transparency has been found to mitigate some of these employee-mobility concerns, as a survey from beqom found that 60% of employees would consider leaving their organization for a more transparent employer.
The Role of AI
AI has emerged as a tool in reducing bias and promoting fairness in the promotion process, said Aisling Teillard, chief customer officer at beqom.
By leveraging advanced algorithms and machine learning techniques, it can help organizations overcome their inherent biases and create a more equitable workplace environment.
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“AI can provide real-time feedback and analyze employee performance data, among other capabilities,” Teillard said. “Reducing bias has been a challenge that HR leaders have always looked for ways to overcome, especially when it comes to compensation and promotion decisions.”
That’s why employers are hoping to use AI to ensure all pieces of performance feedback and compensation decisions are fair and data driven, plus avoid bias that can arise from interpersonal relationships or favoritism, Teillard added.
AI could also cut through the inherent bias that could fester in promotion decisions in a remote work environment. With the workforce more dispersed, preexisting relationships that were fostered in the office could outweigh actual performance in a remote setting.
This dynamic is especially concerning in hybrid work arrangements, as those employees who make their way into the office on a regular basis could be better positioned to receive bonuses or promotions from more in-person interactions with managers and leaders.
“It’s unfortunate that the physical presence in the office often holds more weight than it should,” Teillard said. “It leads to face time with colleagues, creating an advantage for employees working from the office regarding promotion decisions.”
To overcome this proximity bias, Teillard recommends that organizations focus on objective measures of performance and the candidate’s skills and experiences. This emphasis allows employers to make promotion decisions based on a candidate’s merit rather than where their desk is located.
AI Risks
It’s important to note that while AI can potentially reduce bias, it isn’t free from its own biases. That’s because these systems are built by humans who can inadvertently incorporate their societal biases used when developing it.
“AI doesn’t know it all and shouldn’t be relied on entirely when determining organizational changes,” Teillard said. “Instead, employers must use AI to help with decision making and provide important perspectives during any compensation and hiring decision.”
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