摘要
随着人工智能技术的快速发展,算法正逐步替代人类管理者,广泛用于组织管理决策.不过,现有文献在算法(相对于人类管理者)决策对员工公平感知的影响上尚未得到一致结论.本文基于刻板印象内容模型,探索了在不同的组织管理场景下,员工对这两种不同决策主体的公平感知差异及其内在机制.情景实验结果显示,在面对不利结果时,人们普遍认为算法比人类管理者更为公平,并且这一现象背后的影响机制受到任务类型的调节:对于低主观性任务,算法被认为更公平的原因是人们认为人类管理者(相比算法)的能力更差;对于高主观性任务,人们则认为人类管理者(相比算法)更为冷漠.本文拓展了算法公平的相关文献,为探究算法决策对组织管理的影响提供了新的视角.
Abstract
This paper investigates the influence of algorithmic decision-making on employees'perceptions of fairness within the context of organizational management,particularly in light of the rapid advancements in artificial intelligence(AI).Leveraging the Stereotype Content Model(SCM),the study explores differential fairness perceptions between algorithmic and human deci-sion-makers,particularly in adverse outcome scenarios.Findings suggest that algorithms are gen-erally viewed as fairer than human managers,with perceptions influenced significantly by the type of task being evaluated.As AI technologies continue to permeate various business operations,organizations increasingly de-ploy algorithms for diverse managerial functions,including human resources management,task al-location,and performance evaluations.This shift raises critical questions about how employees perceive the fairness of decisions made by algorithms as opposed to human managers.Given that fairness perceptions are pivotal to employee satisfaction,organizational commitment,and perform-ance,understanding these dynamics is essential for effective organizational management.Existing research on algorithmic decision-making offers mixed insights into its impact on perceived fairness.Some studies argue that algorithms,by relying on data and objective models,minimize human biases and enhance fairness.Others,however,highlight potential shortcomings such as neglect of qualitative information and lack of transparency,which can lead to perceived unfairness.Additionally,the literature suggests a gap in understanding the effects of decision outcomes on fair-ness perceptions,particularly when outcomes are unfavorable,thus forming the basis of this study's inquiry.This study employs scenario-based experiments to examine how employees perceive fairness when confronted with unfavorable decisions executed by either algorithms or human managers.These experiments are designed to cover a range of task subjectivities,from highly objective,data-driven tasks to those requiring significant human judgment and intuition.The experimental results reveal that decision-maker type and task nature significantly affect fair-ness perceptions.In scenarios involving objective tasks,algorithms are perceived as fairer due to their presumed impartiality and lack of human error.For subjective tasks,algorithms are still viewed more favorably,but this is attributed to human managers being perceived as potentially in-different or lacking empathy.This dichotomy underscores the complexity of fairness perceptions and suggests that while algorithms may excel in objectivity,they may fall short in areas requiring emotional intelligence.This research adds depth to the discussion on AI's role in management by delineating how task type and outcome influence fairness perceptions differently under algorithmic versus human deci-sion-making.It offers insights that could help organizations better integrate AI into their manage-ment practices,ensuring that fairness perceptions are carefully managed to maintain employee satis-faction and performance.Future researches could broaden the investigation into other organizational contexts and include longitudinal studies to assess how fairness perceptions evolve with long-term exposure to algorith-mic decision-making.Moreover,further studies could explore how increased transparency and employee involvement in algorithm development might enhance trust and fairness perceptions,fos-tering a more equitable organizational environment.
NETL
NSTL
维普
万方数据