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AI-Driven Employee Evaluations

Artificial intelligence can help reduce errors and bias in employee performance reviews by: 1) Conducting non-biased, data-driven reviews using seamlessly collected data from multiple sources and teams. 2) Enabling consistent, real-time assessments of employee contributions instead of only annual reviews. 3) Supporting personalized learning and helping identify which skills need reinventing before becoming obsolete. However, AI may still provide limited feedback and could yield biased results if programmed using biased metrics or allowed to learn from new biased data without testing. Close monitoring is needed to prevent exacerbating issues in performance reviews.

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Arvind Kumawat
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159 views2 pages

AI-Driven Employee Evaluations

Artificial intelligence can help reduce errors and bias in employee performance reviews by: 1) Conducting non-biased, data-driven reviews using seamlessly collected data from multiple sources and teams. 2) Enabling consistent, real-time assessments of employee contributions instead of only annual reviews. 3) Supporting personalized learning and helping identify which skills need reinventing before becoming obsolete. However, AI may still provide limited feedback and could yield biased results if programmed using biased metrics or allowed to learn from new biased data without testing. Close monitoring is needed to prevent exacerbating issues in performance reviews.

Uploaded by

Arvind Kumawat
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
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Many company publicly toss aside the annual employee performance review in favour of a

system of “ongoing feedback”. Yet, no matter how frequent the review or feedback, human
error and bias can compromise the integrity of the employee review process.  When these
errors are present, a performance review may hurt employee morale and job performance,
rather than function as a helpful evaluation tool.  This article talks about how artificial
intelligence can reduce the error and the biasness and streamline performance evaluation
process.
Applications:
1. Non-biased data driven performance review:
There are various challenges in evaluating the performance of an employees. One such
challenge mentioned in the article is if an employee is associated with multiple teams and/or
departments from time to time then it gets tricky to collect information from all the key
stakeholders. Thus, managers tend to rely on only one source of information missing out on
valuable aspects of the employee’s contributions. If these other achievements are not taken
into account, performance reviews may not be accurate leading to employees being
demotivated. Thus, AI can assist in collecting data seamlessly from various sources, extract
the information from the data and evaluates the performances by eliminating all the common
psychological biases.
2. Consistent real time assessments:
Traditional performance reviews have limited opportunity to assess an employee’s
contribution as they usually occur once a year and can become a low priority in a high-
volume workplace. With AI, employee can monitor its performance on real time basis and get
feedback instantaneously.
3. AI in L&D:
AI enables personalized learning experience for each and every employee in the organization
using advanced data analytics. Also, we are aware that today skills have a shorter shelf life
than ever before. Artificial Intelligence can help the system identify which employees need to
reinvent their skills much before they become obsolete or get replaced with better
technologies.

Potential AI Pitfalls:
Though the given article suggests that AI may automate some aspects of the performance
review cycle, but its ability to provide useful feedback is still limited. 
AI technologies are vigorously tested for biased results prior to implementation, however, the
beauty of AI technology is that it learns and grows based on new data received as time goes
on.  Since new data is not tested for bias, it can yield biased results.  If AI is programmed to
evaluate biased metrics, then performance reviews based on that criteria will lead to biased
results, which exacerbate issues already inherent in performance reviews. This major pitfall
of AI’s ability to learn and grow by itself which is what making it difficult to implement in
HR activities.
Conclusion:
Thus, the benefits of AI in performance evaluation are clear – limited human intervention
thereby reducing biases and boosting employee morale, collects data from various sources to
evaluate the performance, monitoring the performance on real time basis thereby allowing for
efficient management and dual-success for employees as well as the organization.

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