In today's competitive and fast-evolving business environment, it is a
critical time for organizations to rethink how to make talent-related decisions
in a quantitative manner. Indeed, the recent development of Big Data and
Artificial Intelligence (AI) techniques have revolutionized human resource
management. The availability of large-scale talent and management-related data
provides unparalleled opportunities for business leaders to comprehend
organizational behaviors and gain tangible knowledge from a data science
perspective, which in turn delivers intelligence for real-time decision-making
and effective talent management at work for their organizations. In the last
decade, talent analytics has emerged as a promising field in applied data
science for human resource management, garnering significant attention from AI
communities and inspiring numerous research efforts. To this end, we present an
up-to-date and comprehensive survey on AI technologies used for talent
analytics in the field of human resource management. Specifically, we first
provide the background knowledge of talent analytics and categorize various
pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant
research efforts, categorized based on three distinct application-driven
scenarios: talent management, organization management, and labor market
analysis. In conclusion, we summarize the open challenges and potential
prospects for future research directions in the domain of AI-driven talent
analytics.

在人力资源管理领域，我们提供了一份关于人工智能技术在人才分析方面的综合调查，首先提供了背景知识和分类相关研究努力，然后根据三种应用驱动场景对其进行了综合分类，最后总结了 AI 驱动的人才分析在未来研究方向的挑战和潜在前景。

人才分析的人工智能技术综述

A Comprehensive Survey of Artificial Intelligence Techniques for Talent  Analytics

Employee attrition is an important and complex problem that can directly
affect an organisation's competitiveness and performance. Explaining the
reasons why employees leave an organisation is a key human resource management
challenge due to the high costs and time required to attract and keep talented
employees. Businesses therefore aim to increase employee retention rates to
minimise their costs and maximise their performance. Machine learning (ML) has
been applied in various aspects of human resource management including
attrition prediction to provide businesses with insights on proactive measures
on how to prevent talented employees from quitting. Among these ML methods, the
best performance has been reported by ensemble or deep neural networks, which
by nature constitute black box techniques and thus cannot be easily
interpreted. To enable the understanding of these models' reasoning several
explainability frameworks have been proposed. Counterfactual explanation
methods have attracted considerable attention in recent years since they can be
used to explain and recommend actions to be performed to obtain the desired
outcome. However current counterfactual explanations methods focus on
optimising the changes to be made on individual cases to achieve the desired
outcome. In the attrition problem it is important to be able to foresee what
would be the effect of an organisation's action to a group of employees where
the goal is to prevent them from leaving the company. Therefore, in this paper
we propose the use of counterfactual explanations focusing on multiple
attrition cases from historical data, to identify the optimum interventions
that an organisation needs to make to its practices/policies to prevent or
minimise attrition probability for these cases.

首次提出面向多个员工的反事实解释技术，通过历史数据，找到组织的最佳干预措施，解决员工离职问题。