Retention Is All You Need
- URL: http://arxiv.org/abs/2304.03103v2
- Date: Sat, 26 Aug 2023 21:06:13 GMT
- Title: Retention Is All You Need
- Authors: Karishma Mohiuddin, Mirza Ariful Alam, Mirza Mohtashim Alam, Pascal
Welke, Michael Martin, Jens Lehmann, Sahar Vahdati
- Abstract summary: We propose the HR-DSS approach, which stands for Human Resource (HR) Decision Support System.
The system is designed to assist HR departments in interpreting the predictions provided by machine learning models.
- Score: 9.570332155350055
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Skilled employees are the most important pillars of an organization. Despite
this, most organizations face high attrition and turnover rates. While several
machine learning models have been developed to analyze attrition and its causal
factors, the interpretations of those models remain opaque. In this paper, we
propose the HR-DSS approach, which stands for Human Resource (HR) Decision
Support System, and uses explainable AI for employee attrition problems. The
system is designed to assist HR departments in interpreting the predictions
provided by machine learning models. In our experiments, we employ eight
machine learning models to provide predictions. We further process the results
achieved by the best-performing model by the SHAP explainability process and
use the SHAP values to generate natural language explanations which can be
valuable for HR. Furthermore, using "What-if-analysis", we aim to observe
plausible causes for attrition of an individual employee. The results show that
by adjusting the specific dominant features of each individual, employee
attrition can turn into employee retention through informative business
decisions.
Related papers
- On the Modeling Capabilities of Large Language Models for Sequential Decision Making [52.128546842746246]
Large pretrained models are showing increasingly better performance in reasoning and planning tasks.
We evaluate their ability to produce decision-making policies, either directly, by generating actions, or indirectly.
In environments with unfamiliar dynamics, we explore how fine-tuning LLMs with synthetic data can significantly improve their reward modeling capabilities.
arXiv Detail & Related papers (2024-10-08T03:12:57Z) - WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks [85.95607119635102]
Large language models (LLMs) can mimic human-like intelligence.
WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents.
arXiv Detail & Related papers (2024-07-07T07:15:49Z) - Explain To Decide: A Human-Centric Review on the Role of Explainable
Artificial Intelligence in AI-assisted Decision Making [1.0878040851638]
Machine learning models are error-prone and cannot be used autonomously.
Explainable Artificial Intelligence (XAI) aids end-user understanding of the model.
This paper surveyed the recent empirical studies on XAI's impact on human-AI decision-making.
arXiv Detail & Related papers (2023-12-11T22:35:21Z) - Evaluating the Utility of Model Explanations for Model Development [54.23538543168767]
We evaluate whether explanations can improve human decision-making in practical scenarios of machine learning model development.
To our surprise, we did not find evidence of significant improvement on tasks when users were provided with any of the saliency maps.
These findings suggest caution regarding the usefulness and potential for misunderstanding in saliency-based explanations.
arXiv Detail & Related papers (2023-12-10T23:13:23Z) - Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes [72.13373216644021]
We study the societal impact of machine learning by considering the collection of models that are deployed in a given context.
We find deployed machine learning is prone to systemic failure, meaning some users are exclusively misclassified by all models available.
These examples demonstrate ecosystem-level analysis has unique strengths for characterizing the societal impact of machine learning.
arXiv Detail & Related papers (2023-07-12T01:11:52Z) - Designing Closed-Loop Models for Task Allocation [36.04165658325371]
We exploit weak prior information on human-task similarity to bootstrap model training.
We show that the use of such a weak prior can improve task allocation accuracy, even when human decision-makers are fallible and biased.
arXiv Detail & Related papers (2023-05-31T13:57:56Z) - Predicting and Understanding Human Action Decisions during Skillful
Joint-Action via Machine Learning and Explainable-AI [1.3381749415517021]
This study uses supervised machine learning and explainable artificial intelligence to model, predict and understand human decision-making.
Long short-term memory networks were trained to predict the target selection decisions of expert and novice actors completing a dyadic herding task.
arXiv Detail & Related papers (2022-06-06T16:54:43Z) - Investigations of Performance and Bias in Human-AI Teamwork in Hiring [30.046502708053097]
In AI-assisted decision-making, effective hybrid teamwork (human-AI) is not solely dependent on AI performance alone.
We investigate how both a model's predictive performance and bias may transfer to humans in a recommendation-aided decision task.
arXiv Detail & Related papers (2022-02-21T17:58:07Z) - Individual Explanations in Machine Learning Models: A Survey for
Practitioners [69.02688684221265]
The use of sophisticated statistical models that influence decisions in domains of high societal relevance is on the rise.
Many governments, institutions, and companies are reluctant to their adoption as their output is often difficult to explain in human-interpretable ways.
Recently, the academic literature has proposed a substantial amount of methods for providing interpretable explanations to machine learning models.
arXiv Detail & Related papers (2021-04-09T01:46:34Z) - Leveraging Expert Consistency to Improve Algorithmic Decision Support [62.61153549123407]
We explore the use of historical expert decisions as a rich source of information that can be combined with observed outcomes to narrow the construct gap.
We propose an influence function-based methodology to estimate expert consistency indirectly when each case in the data is assessed by a single expert.
Our empirical evaluation, using simulations in a clinical setting and real-world data from the child welfare domain, indicates that the proposed approach successfully narrows the construct gap.
arXiv Detail & Related papers (2021-01-24T05:40:29Z) - DECE: Decision Explorer with Counterfactual Explanations for Machine
Learning Models [36.50754934147469]
We exploit the potential of counterfactual explanations to understand and explore the behavior of machine learning models.
We design DECE, an interactive visualization system that helps understand and explore a model's decisions on individual instances and data subsets.
arXiv Detail & Related papers (2020-08-19T09:44:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.