Artificial intelligence (AI) is gaining momentum, and its importance for the
future of work in many areas, such as medicine and banking, is continuously
rising. However, insights on the effective collaboration of humans and AI are
still rare. Typically, AI supports humans in decision-making by addressing
human limitations. However, it may also evoke human bias, especially in the
form of automation bias as an over-reliance on AI advice. We aim to shed light
on the potential to influence automation bias by explainable AI (XAI). In this
pre-test, we derive a research model and describe our study design.
Subsequentially, we conduct an online experiment with regard to hotel review
classifications and discuss first results. We expect our research to contribute
to the design and development of safe hybrid intelligence systems.

本研究旨在探讨可解释人工智能对自动化偏见的潜在影响。利用一个酒店评价分类的在线实验，实验证明可解释 AI 可以降低自动化偏见，从而帮助设计和开发安全的混合智能系统。

可解释 AI 对自动化偏见的影响

On the Influence of Explainable AI on Automation Bias

Recent technological advances, especially in the field of machine learning,
provide astonishing progress on the road towards artificial general
intelligence. However, tasks in current real-world business applications cannot
yet be solved by machines alone. We, therefore, identify the need for
developing socio-technological ensembles of humans and machines. Such systems
possess the ability to accomplish complex goals by combining human and
artificial intelligence to collectively achieve superior results and
continuously improve by learning from each other. Thus, the need for structured
design knowledge for those systems arises. Following a taxonomy development
method, this article provides three main contributions: First, we present a
structured overview of interdisciplinary research on the role of humans in the
machine learning pipeline. Second, we envision hybrid intelligence systems and
conceptualize the relevant dimensions for system design for the first time.
Finally, we offer useful guidance for system developers during the
implementation of such applications.

本文提供了一个交叉学科的观点，以发展由人与机器组成的社会技术群体，通过结合人工智能和人类智能，共同实现复杂目标，并从彼此学习中不断提高。因此，需要为这些系统提供结构化的设计知识，并展示了使这些应用程序的系统开发人员有用的指导。