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可解释的人工智能 (XAI) 用于提高组织区域性.

Niusha Shafiabady1, Nick Hadjinicolaou2, Nadeesha Hettikankanamage3

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组织使用人工智能 (AI) 来提高灵活性和弹性. 这项研究整合了像SHAP这样的可解释AI (XAI) 技术,以揭示决策因素,增强信任并指导企业竞争力的改进.

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科学领域:

  • 企业管理 企业管理
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 随着COVID-19的流行,组织的敏捷性和弹性需求加快了.
  • 人工智能 (AI) 越来越多地被采用,以提高企业的适应能力和决策能力.
  • 缺乏灵活性会导致重大业务风险,包括财务损失和市场份额减少.

研究的目的:

  • 研究可解释的人工智能 (XAI) 在预测组织敏捷性和弹性方面的整合.
  • 使用XAI技术识别影响组织敏捷性和弹性的主要特征.
  • 加强对人工智能驱动的决策的透明度和信任,以实现战略业务改进.

主要方法:

  • 利用先前的AI研究来预测组织敏捷性.
  • 综合式可解释的人工智能 (XAI) 方法,特别是沙普利添加式解释 (SHAP).
  • 分析了特征的重要性,以了解AI模型决策过程.

主要成果:

  • 确定了影响组织敏捷性和弹性预测的关键特征.
  • 展示了XAI技术的能力,以消除AI模型决策的神秘性.
  • 提供了对推动组织敏捷性和弹性因素的见解.

结论:

  • 可解释的人工智能 (XAI) 对于理解和改进人工智能在提高组织敏捷性和弹性方面的作用至关重要.
  • 识别关键的预测特征引导组织集中精力进行战略改进.
  • 通过确保人工智能系统的透明度,可解释性和信任,XAI促进道德AI部署.