XRRF: An eXplainable Reasonably Randomised Forest algorithm for classification and regression problems

Jain, Nishant; Jana, Prasanta K.

doi:10.1016/j.ins.2022.09.040

Cited by 8 publications

(1 citation statement)

References 32 publications

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“…If one wishes to analyze articles related to employee attrition that apply predictive models and emphasize interpretability (TS = 'XAI' OR 'INTERPRETABLE' OR 'EXPLAIN-ABLE') to said models, from the list, the following study is found which delves into the ML algorithm's intrinsic interpretability models [26]. Furthermore, there is the following article from the 2022 International Conference on Decision Aid Sciences and Applications (DASA) [36], which delves into the global agnostic interpretability models Shapley and local LIME.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making

Marín Díaz,

Galán Hernández,

Galdón Salvador

2023

Mathematics

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Employee attrition and high turnover have become critical challenges faced by various sectors in today’s competitive job market. In response to these pressing issues, organizations are increasingly turning to artificial intelligence (AI) to predict employee attrition and implement effective retention strategies. This paper delves into the application of explainable AI (XAI) in identifying potential employee turnover and devising data-driven solutions to address this complex problem. The first part of the paper examines the escalating problem of employee attrition in specific industries, analyzing the detrimental impact on organizational productivity, morale, and financial stability. The second section focuses on the utilization of AI techniques to predict employee attrition. AI can analyze historical data, employee behavior, and various external factors to forecast the likelihood of an employee leaving an organization. By identifying early warning signs, businesses can intervene proactively and implement personalized retention efforts. The third part introduces explainable AI techniques which enhance the transparency and interpretability of AI models. By incorporating these methods into AI-based predictive systems, organizations gain deeper insights into the factors driving employee turnover. This interpretability enables human resources (HR) professionals and decision-makers to understand the model’s predictions and facilitates the development of targeted retention and recruitment strategies that align with individual employee needs.

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Section: Related Workmentioning

confidence: 99%