Artificial intelligence (AI) in human resource functions is rewriting the way organisations evaluate
employee performance. Or traditional performance evaluation techniques are subjective in nature, time
consuming & biased which add to inaccurate measures [1]. AI-driven predictive models offer a data
driven way to assess employees in organizations to leverage large datasets, identify patterns and drive
manager decisions. These models employ complex methodologies that are machine learning, deep
learning, and natural language processing to evaluate employee productivity, engagement, and potential
[2].
In this paper, the work done on AI based predictive models in evaluation of employee performance is
presented as a detailed survey. It studies numerous AI methodologies, ranging decision trees up to
neural networks and sentiment analysis to learn more on how these tools make assessment of workplace
better [3,4]. The other part of our paper describes use-cases in practice of AI nowadays, from talent
acquisition to performance monitoring and employee retention etc [5,6]. The paper also brings to light
some of the ethical issues (bias in algorithms, transparency, etc…) and challenges related to evaluation
driven by AI systems [7].
And our literature review maps where AI for employee performance evaluation is heading, flagging the
need for explainable AI (XAI), federated learning and approaches to mitigate bias [11]. We find that
despite these benefits of AI in workforce management, it is important ethical considerations and
transparency are considered to achieve fair, responsible AI adoption [8]. The takeaways from our paper
will be to help organizations leverage AI as critical enabler with different capabilities from making
more informed decisions to uplifting employee engagement and workforce potential.