解析算法黑箱：技术复杂与商业保密双重导致的透明度困境
Explainability of Algorithms
- 区分技术性黑箱与人为隐藏两种不透明成因
- 现有可解释AI方法仍面临诸多实际挑战
- 适合关注AI伦理与系统透明性的研究者阅读
许多复杂机器学习算法的不透明性常被视为人工智能伦理发展的主要障碍。所谓不透明，是指高度复杂的算法如人工神经网络在多层互联节点中并行处理海量数据，使包括设计者在内的所有人无法理解其内部运作，成为所有利益相关者的‘黑箱’。然而，不透明并非技术复杂性的必然结果；有时算法运行方式被有意隐藏，出于商业机密考量，尤其在自动化决策系统中，形成另一种类型的不透明。本文第一部分分析这两种不透明的理解方式及其伦理影响。第二部分探讨计算机科学中为克服技术不透明而发展出的多种解释方法。研究表明，可解释人工智能（XAI）仍面临诸多挑战。
原文摘要 · Abstract (English)
The opaqueness of many complex machine learning algorithms is often mentioned as one of the main obstacles to the ethical development of artificial intelligence (AI). But what does it mean for an algorithm to be opaque? Highly complex algorithms such as artificial neural networks process enormous volumes of data in parallel along multiple hidden layers of interconnected nodes, rendering their inner workings epistemically inaccessible to any human being, including their designers and developers; they are "black boxes" for all their stakeholders. But opaqueness is not always the inevitable result of technical complexity. Sometimes, the way an algorithm works is intentionally hidden from view for proprietary reasons, especially in commercial automated decision systems, creating an entirely different type of opaqueness. In the first part of the chapter, we will examine these two ways of understanding opacity and the ethical implications that stem from each of them. In the second part, we explore the different explanatory methods that have been developed in computer science to overcome an AI system's technical opaqueness. As the analysis shows, explainable AI (XAI) still faces numerous challenges.
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