导航人工智能伦理:在有争议的人工智能实践和技术中,ANN和ANFIS提供透明和负责任的项目评估
Sandeep Wankhade1, Manoj Sahni1, Ernesto León-Castro2
1Department of Mathematics, Pandit Deendayal Energy University, Gandhinagar, India.
Frontiers in artificial intelligence
|May 19, 2025
概括
本研究引入了使用人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS) 来量化人工智能项目的伦理标准的新框架,确保负责任的AI部署和培养信任.
科学领域:
- 人工智能伦理学 人工智能伦理学
- 计算智能是一种计算智能.
- 负责的人工智能
背景情况:
- 人工智能的快速演变需要强大的道德框架来实现负责任的部署.
- 在人工智能项目中量化道德标准是具有挑战性的,原因是复杂的因素.
- 人工智能的社会影响是由沟通实践,组织结构和技术塑造的.
研究的目的:
- 为评估人工智能项目绩效和建模伦理不确定性提出一个新的框架.
- 整合人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS) 进行伦理评估.
- 量化关键的伦理维度,包括透明度,公平,问责,隐私,安全,可解释性,人类参与和社会影响.
主要方法:
- 利用一个整合ANN和ANFIS的框架进行AI项目评估.
- 采用模糊逻辑和模糊加权平均方法来模拟伦理不确定性.
- 在AI项目中量化了关键的伦理维度.
主要成果:
- 开发了人工智能项目的结构化评估框架,提高了透明度和问责制.
- ANN用于评估绩效指标,而ANFIS用于模拟不确定性.
- 在复杂的条件下提供全面的道德评估.
结论:
- 联合ANN和ANFIS的方法促进了对人工智能的伦理维度的理解.
- 提供了一个可扩展的方法来开发可问责的AI系统.
- 通过在技术和结构背景中嵌入道德,促进信任和社会协调,为负责任的人工智能创新做出贡献.
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