Due to the opacity of machine learning technology, there is a need for explainability and fairness in the decision support systems used in public or private organizations. Although the criteria for appropriate explanations and fair decisions change depending on the values of those who are affected by the decisions, there is a lack of discussion framework to consider the appropriate outputs for each stakeholder. In this paper, we propose a discussion framework that we call "stakeholder-in-the-loop fair decisions." This is proposed to consider the requirements for appropriate explanations and fair decisions. We identified four stakeholders that need to be considered to design accountable decision support systems and discussed how to consider the appropriate outputs for each stakeholder by referring to our works. By clarifying the characteristics of specific stakeholders in each application domain and integrating the stakeholders' values into outputs that all stakeholders agree upon, decision support systems can be designed as systems that ensure accountable decision makings.

翻译：由于机器学习技术的不透明性，公共或私营组织中使用的决策支持系统亟需可解释性和公平性。尽管适当的解释标准和公平决策的准则会因受决策影响群体的价值观不同而变化，但目前尚缺乏能够针对各利益相关者考虑适当输出的讨论框架。本文提出了一种名为"利益相关者在环公平决策"的讨论框架，旨在探讨适当解释和公平决策的要求。我们识别了设计可问责决策支持系统时需要考虑的四类利益相关者，并结合已有工作讨论了如何针对每类利益相关者生成适当输出。通过明确各应用领域中特定利益相关者的特征，并将其价值观整合为所有利益相关者共同认可的决策输出，决策支持系统可被设计为确保可问责决策的系统。