How AI Reduces Bias in Employee Performance Reviews

The use of AI in HR, particularly for employee performance evaluations, has sparked important conversations about fairness and objectivity. We've explored the potential benefits and challenges of integrating AI into performance management, and even discussed the broader impact of AI on workplace culture. Today, we're zeroing in on a critical aspect of AI in performance reviews: how to avoid bias.

Introduction to Performance Evaluations

It's indisputable that stakeholders rely on employee performance evaluations —reviewing individual and team contributions to strategic business goals—for critical talent development insights. Why? They foster employee retention and engagement as ROI optimizers, depending on three crucial things:

1. Reviewers and employees mutually agreeing on defined objectives at the start of each evaluation period. 
2. Unbiasedly connecting employee contributions to the agreed objectives.
3. Adjusting evaluation processes to fast-changing workplace dynamics demanding our attention. These include:
   • Remote and hybrid work options.
   • Employee wellness and mental health.
   • Unprecedented worker emotional and cognitive needs.

Unfortunately, according to a 2022 Forbes Report, a disappointing and meager 14% of employees feel inspired by their performance reviews. A 2023 Gallup Poll aligns with this, pinpointing that only 33% of US employees think their employer tries to engage them. Finally, according to the US Bureau of Labor Statistics, stakeholders can expect a 47% annual voluntary and involuntary staff turnover, with replacement costs going as high as 200% of salary. 

The primary reason for these worrying metrics is reliance on human skills (misdirected by several biases) and traditional (outdated) tools that have lost touch with the optimal employee experience. As a result:

• One of HR’s severest challenges is mitigating performance evaluation bias (i.e., distortions in connecting employee contributions to goals). 
• Employees disadvantaged by unfairly skewed reviewer (mainly manager) ratings lose trust in the corporate culture, feel alienated, and develop stress symptoms as their EX aspirations dissolve.

An innovative and game-changing response is crucial to reverse these anti-retention, pro-churn employee influences disrupting businesses and costing them millions of dollars. This article presents a strategic approach to reducing bias in performance evaluations. How? With generative AI performance management tools that include sophisticated bias detection techniques.

Traditional Bias Types in Performance Evaluations

So, what exactly is bias in employee performance reviews? It's when personal opinions or prejudices unfairly influence how an employee's work is evaluated.

Traditional performance evaluation methods raise memorability/recall issues. An excellent example of this is when managers focus on recent performance, discounting earlier contributions—referred to as Recency Bias. It's not the only obstructive type in performance evaluations; consider the following:

• The Halo Effect: What's this? A bias emerges when positive first impressions or charismatic character traits deflect attention away from subpar performance behavior.
• The Horn Effect is the opposite of halo bias. It's where employees' negative personality traits (e.g., reserved demeanor or ineloquent communication) unfairly influence evaluation downgrades.
• Gender Bias is a persistent problem that often makes headlines. Many lawsuits have accused companies of undervaluing the contributions of women and transgender employees, highlighting the harmful impact of gender stereotypes in the workplace.

The four above are only the tip of a performance evaluation "bias iceberg" that includes the following:

• Proximity Bias: This occurs when favorable appraisals attach to those physically closer to the reviewers (i.e., in the same building or floor), with remote workers faring less favorably only because they're miles away.
• Primacy Bias: Reviewers pressed for time frequently weigh contributions against the goals they see first on the list (at the top) more heavily than those in the middle or bottom. 
• Central Tendency Bias results in an abundance of "average" or “neutral” ratings. Why? Some managers are reluctant to risk undesirable reactions to extreme scoring—demotivation (to low ratings) or complacency (to high ratings)—so they stick to the middle sections of the scale.
• Leniency and Strictness Biases skew results when managers go too easy on employees who must improve and assess too harshly on performances that primarily meet the required standards. The reasons for this often circle back to other biases mentioned above and below.
• Idiosyncratic Rater Bias is explained best with the following examples:
  • A manager who's great at accounting might give the admin team higher scores than they deserve, simply because they don't fully understand their role and its complexities.
  • Or, an experienced sales executive might be too hard on newly recruited sales personnel, scoring them lower because they're comparing them to their own high standards.
• Confirmation Bias (demonstrated with another example): Let's say Jim (the manager) has a preconceived notion that Sarah, an employee, is not a team player. During performance reviews, he might focus more on instances where Sarah worked independently, even if she collaborated effectively on other projects. This bias leads him to overlook evidence that contradicts his initial belief.
• The Law of Small Numbers Bias is in play when the reviewer laser-focuses on one or only a few isolated instances where contributions were outside the norm, ignoring the many that met expected standards. Again, the reasons for this may overlap other biases like Primacy and Recency.  
  
  

Understanding Generative AI in HR

What is Generative AI in HR?

Generative AI in HR is a technology that uses artificial intelligence to automate and enhance various HR processes. By seamlessly reducing human bias, it provides objective insights and a complete employee performance overview. The AI-driven performance management tools that fall under this category can efficiently scan massive volumes of raw input from team members, surveys, customer interactions, self-evaluations, and learning & development phases in a fraction of human time, leaving no gaps. 

In summary, modern AI software delivers insightful under- and over-performance patterns. It covers a vast field of quantitative and qualitative data related to employee behavior, sorting and scoring it with evolving algorithms. In so doing, AI-driven data analytics saves valuable time and simultaneously ensures accuracy untainted by human bias. Nothing delivers more peace of mind when making actionable decisions than knowing the evidence supporting them is practically irrefutable.

Examples of AI in the workplace

At the cutting edge of the generative AI curve, savvy stakeholders use their tools to streamline the entire performance evaluation process. It includes:

• Assisting managers to set realistic goals
• Accurately connecting contributions to objectives with relevant feedback
• Monitoring performance progress.
• Recommending training solutions to fill gaps as detected. 
  
  

• AI seamlessly and automatically generates reports with a holistic take on employees’ unique goals, tasks, and accountability. Thus, it provides:

• The detailed, comprehensive overviews employees and managers expect.

• An optimal balance of past, current, and future performance metrics.

• A clear and consistent format that reflects professionalism.

As a result, employees are more likely to cooperate, leading to faster and more effective improvements that benefit both the business and its team members.

• Personalized feedback mechanisms structure reports how employees want to receive them, deviating substantially from traditional formats. For example, graphs and charts may suit some recipients, whereas others prefer text, image, or audio feedback. What are the benefits of this?

• Firstly, it signals recognition of employees’ unique needs.

• Secondly, performance evaluation (a somewhat stressful interaction) anticipated with fear transforms into learning and development (L&D) - a proactive, rewarding, and welcomed one. 
  
  

• Performance evaluations with unrealistic goals are dead on arrival. So, AI algorithms intuitively fuse realism into their intellectual DNA. How? They do it by:

• Absorbing incredible volumes of data input.

• Looking back at each employee’s goals achieved and missed and, more crucially, analyzing why.

• Aligning objectives systematically with employees’ potential and abilities.

• Projecting goals into the future alongside continuous training recommendations to keep performance improvement on track and relevant.
  
  

• AI's speed and versatility allow it to collect and analyze data in real-time, making continuous learning a reality. In this regard, Macorva's AI performance management tools are a prime example of a review system that:

• Tracks and monitors employee performance more dynamically and responsively than what can be achieved with traditional methods.

• Immediately detects slowdowns, hesitation, derailed goal contributions, and skill gaps as they're happening. 

• Recommends relevant, actionable learning and training upgrades to address performance issues proactively, leading to more positive feedback during formal reviews.
  
  

• AI tools highlight gaps between employees' goals and what the company expects of them, recommending how to close them with training and mediation. They also auto-generate OKRs and SMART goal first drafts, serving as a start point for discussions that lead to more effective goal-setting.
  
  
  

The Role of Generative AI in Mitigating Bias

We've highlighted how AI significantly reduces human bias in performance evaluations, but it's crucial to appreciate that technological biases can creep into the results. To offset this obstruction, input data must:

1. Accurately represent the employee demographic, behavioral, and emotional profiles. Otherwise, the patterns emerging will be misleading.
2. Be error-free to avoid AI misinterpretation.  

Data cleansing or “scrubbing” is a massive subject in its own right. Trained data engineers apply structured techniques that address such inconsistencies as duplicate records, incorrect entries at source (e.g., wrong numbers, typographical and syntax errors), and incomplete or conflicting data. Specialized tools like RingLead, SAS Data Quality, Informatica, and others can help automate and streamline this process, ensuring your AI system receives the most accurate and reliable data possible. In short, it minimizes the risk of "garbage in, garbage out" and helps keep technological bias at a minimum. 

Macorva's AI programs are trained to further enhance fairness and objectivity in performance insights. Our AI risk analysis feature flags potentially biased language or unsupported statements for manager review, providing an additional layer of scrutiny. By combining robust data cleansing practices with AI-powered checks and balances, you can create a performance review process that's both efficient and equitable.

Algorithmic Transparency and Explainability

Organizations should not expect their employees to blindly trust AI-driven evaluations without any explanation. Thus, AI transparency and the ability to explain certain decisions are crucial, even if  the technical details might be complex for some employees to fully grasp.

The first critical step is transparently establishing your AI program’s authenticity. This relies on the AI software's "Ethical Considerations and Compliance," a topic that deserves its own discussion (see Section E below). When you present AI thoughtfully and transparently, it'll go a long way in easing employee concerns as you transition from old-school methods to cutting-edge technology. Besides that:

1. You could try explaining the technical details, like decision trees, code configurations, or using special techniques to simplify complex models. But even then, it might still be a bit too technical for most people.
2. AI’s best proponent is when it speaks for itself. What do we mean by this? How the system delivers answers to questions creates trust. 

For example, suppose an employee asks:

• “Why did you rate me low on cooperation with my peers?” 
• Consider the following as an engaging answer: 
  • “Your team involvement with three projects in the last year solicited significant feedback from other project members. ‘Lack of patience’ emerged as a constant comment that detracted from your otherwise respected skills in meeting team objectives. As you are recently out of the onboarding phase, team interaction data previous to these projects was scanty.” 
• If the software's AI logic resonates with the employee, it'll gain traction as a trustworthy, credible, and viable resource. 

Remember, machine learning algorithms enable your AI to improve responses over time, which brings us to the technology’s next critical benefit.

Continuous Learning and Adaptation

Continuous learning is at the crux of AI performance software. Many AI experts call it lifelong or incremental learning, a feature that signifies the technology has no limits as to how many angles and trends it can find in the same data and how much data it can include in its evaluations. 

How do the AI algorithms do it?

1. Their memory retention is second to none: Unlike humans, they never forget any of the content previously developed, data sources accessed, or methods deployed, no matter how much time lapses.
2. By applying a proven, continuous learning system similar to humans, only thousands of times faster and more efficient in a series of steps where they: 

• Step 1: Examine the data.

• Step 2: Develop possible solutions, which they test.

• Step 3: Evaluate the results.

• Step 4: Adapt the best insights to update an evaluation.

• Step 5: Return to Step 1 in a continuous feedback loop.

Underlying all these steps is generative AI’s ability to monitor performance data in real-time, alerting itself and stakeholders of potential biases as they occur. More on this under Real-Time Monitoring and Alerts (see F below).

In summary, an AI-driven system’s output changes significantly as it progressively monitors data expansion in real-time, learning more, adapting to data expansion, and delivering better performance review insights while improving its own capabilities.

Nothing has been at the receiving end of workplace shifting more than the HR function, impacting productivity and employee engagement throughout the company. Generative AI automation has already impacted many companies' administration work and traditional performance evaluations, reducing manual or rater involvement by up to 70 percent. The saving of human time and effort creates the breathing room for HR to:

1. Strategize phasing out cumbersome outdated systems by replacing them seamlessly with new agile ones.
2. Train and help employees adapt to AI-generated feedback and continuously evolving job descriptions.
3. Support team members as they interface automation dashboards.
4. Erase touchpoints that obstruct trust in newly introduced methodologies.
5. Investigate and invest in innovative AI-centric software streaming into the arena.

According to McKinsey, the generative AI options give managers "superpowers when it comes to HR topics," including performance evaluation. There's less tactical intervention, more guidance, less static same-old repetition, and more personalized proactive involvement. 

In summary, it paves the way for a more strategic HR focus, underlined by improved, streamlined processes to support the business's AI aspirations. Finally, the association with professional generative AI providers like Macorva strengthens management's efforts to stay in touch with rapid industry advances and accelerated sophistication. 

To offset employee criticism and skepticism severely obstructing the transition of one’s performance evaluation framework to AI technologies, stakeholders must meet or exceed all the industry's best security privacy protections. Also, they must prioritize showing the software’s pedigree—licenses, certificates, and relevant industry regulatory recognition—as vibrant, robust, professional, and secure to ensure non-discrimination and fairness from end to end in employee review processes. 

Here are some common sense ethical AI in HR suggestions that go a long way to establishing credibility and adding significantly to employees accepting the shift from traditional methods: 

• Distribute a newsletter-type communication, podcast, or YouTube video leveled at the average employee audience (which you should assume are non-AI specialists). 

• Reference other reputable users and advisors behind the selected software.
• Monitor these backup credentials, update them, and maintain their exposure to your audience. 
  
  

Under various sections above, we've emphasized generative AI's continuous learning capabilities, the need for data cleansing, and the potential threat of technological biases as information conflicts impact results. One of the generative AI software's robust features (supported by machine learning) is that it doesn't allow these detected feedback obstructions to reoccur. 

Excellent examples are Macorva and other software providers' successful efforts to: 

• Fine-tune their technologies to "not make the same error twice" as they monitor data inputs in real-time with alerts the instant a bias emerges. 
• Scan for repeated, incomplete, and conflicting entries with software-integrated features that enable it to self-cleanse the data in many respects, thus supporting the data engineer's vigilance processes.
  
  

Implementing Generative AI in Performance Evaluations—The Challenges and Limitations

While generative AI holds great promise for transforming performance evaluations, it's crucial to approach its implementation thoughtfully and strategically. To fully understand the potential benefits and challenges of integrating AI into your HR practices, we invite you to explore our additional insights on this topic:

• Implementing Generative AI: A Roadmap to Successful Adoption: This resource offers a strategic guide to planning and executing a successful rollout of generative AI tools.

• The Application of AI in Performance Reviews: Delve deeper into the specific ways AI is being utilized in performance evaluations today.

• Five Ways to Revolutionize Performance Management: Discover how AI can transform your approach to performance management beyond just reviews

• AI's Impact on HR: Will it Strengthen or Strain Team Dynamics?: Explore the broader implications of AI in HR, including its impact on company culture and employee engagement.

Conclusion

The insights provided in this article show that employee performance evaluation and the generative AI revolution have merged with exceptional results. So, the question isn't if AI technology will enter your employee performance review strategies but how seamlessly and effectively you can integrate it. 

To achieve this seamless integration, it's essential to partner with solutions that prioritize both technological innovation and responsible AI practices.

So, contact Macorva to request a demo and discover how our technology can help you meet your HR needs, delivering the exceptional support they demand with every employee interaction.