Although recent developments in Artificial Intelligence (AI) and machine learning (ML) aim to enhance the fairness and transparency of decision-making systems, research has found that neural networks (or other similar AI techniques) are still effected by human cognitive biases due to the training datasets. In this study, we focus on order effects, i.e., when the input of information impacts human perception and the decisions resulting from this information. We propose the Order Effect Removal Method (OERM) for handling the order effect which leads to bias and for helping organizations remove these biases from their training datasets and, therefore, from automated decision-making systems. Using design science principles to theoretically create, test, and validate the method, we can eliminate the order bias even in basic classification systems. Furthermore, the method can be applied in a multidisciplinary context, where an AI-based algorithm substitutes for manual work.

尽管人工智能 (AI) 和机器学习 (ML) 的最新发展旨在提高决策系统的公平性和透明度，但研究发现，由于以下原因，神经网络（或其他类似的 AI 技术）仍会受到人类认知偏差的影响训练数据集。在这项研究中，我们关注顺序效应，即信息输入何时影响人类感知和由此信息产生的决策。我们提出了顺序效应去除方法 (OERM) 来处理导致偏差的顺序效应，并帮助组织从他们的训练数据集中去除这些偏差，从而从自动决策系统中去除这些偏差。使用设计科学原理从理论上创建、测试和验证该方法，我们甚至可以在基本分类系统中消除顺序偏差。