深度不透明性让AI难以解释，加剧隐私风险。
Deep opacity and AI: A threat to XAI and to privacy protection mechanisms
- 区分三类不透明：用户不知、分析者未知、根本无法知晓
- 在深度不透明下，无法实现知情同意与匿名保护
- 适合关注AI伦理与隐私保护的研究者阅读
大数据分析与人工智能对隐私构成威胁，部分源于AI的“黑箱问题”。本文指出，在判断与行动正当性语境中，这种不透明性带来严重后果。进一步提出三种不透明类型：1）数据主体不了解系统行为（浅层不透明）；2）分析者不了解系统行为（标准黑箱不透明）；3）分析者根本无法理解系统可能行为（深度不透明）。当决策者与数据主体均处于不透明状态时，将无法提供必要理由以维护隐私，如无法实现真正知情同意或保证匿名性。因此，大数据分析使隐私问题恶化，且现有应对措施效力下降。最后，简要展望技术上缓解该困境的可能路径。
原文摘要 · Abstract (English)
It is known that big data analytics and AI pose a threat to privacy, and that some of this is due to some kind of "black box problem" in AI. I explain how this becomes a problem in the context of justification for judgments and actions. Furthermore, I suggest distinguishing three kinds of opacity: 1) the subjects do not know what the system does ("shallow opacity"), 2) the analysts do not know what the system does ("standard black box opacity"), or 3) the analysts cannot possibly know what the system might do ("deep opacity"). If the agents, data subjects as well as analytics experts, operate under opacity, then these agents cannot provide justifications for judgments that are necessary to protect privacy, e.g., they cannot give "informed consent", or guarantee "anonymity". It follows from these points that agents in big data analytics and AI often cannot make the judgments needed to protect privacy. So I conclude that big data analytics makes the privacy problems worse and the remedies less effective. As a positive note, I provide a brief outlook on technical ways to handle this situation.
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