纠正神经符号AI中条件独立性有害的误解，指出确定性偏差实为误用所致。
Independence Is Not an Issue in Neurosymbolic AI
- 通过稀疏逻辑图约束神经网络输出，构建概率模型
- 发现确定性偏差源于不当应用而非条件独立性本身
- 适用于希望避免误判模型缺陷的研究者
神经符号人工智能的一种常见方法是将神经网络最后一层的输出（如softmax激活值）输入到一个编码特定逻辑约束的稀疏计算图中。这会诱导出一组随机变量的概率分布，这些变量在许多常用神经符号模型中表现为条件独立。以往研究认为这类条件独立变量有害，因其常与一种称为确定性偏差的现象共现——系统倾向于确定性地选择解空间中的某一有效解而非其他。本文提供证据反驳此结论，表明确定性偏差实为神经符号人工智能使用不当所致，而非条件独立性的必然后果。
原文摘要 · Abstract (English)
A popular approach to neurosymbolic AI is to take the output of the last layer of a neural network, e.g. a softmax activation, and pass it through a sparse computation graph encoding certain logical constraints one wishes to enforce. This induces a probability distribution over a set of random variables, which happen to be conditionally independent of each other in many commonly used neurosymbolic AI models. Such conditionally independent random variables have been deemed harmful as their presence has been observed to co-occur with a phenomenon dubbed deterministic bias, where systems learn to deterministically prefer one of the valid solutions from the solution space over the others. We provide evidence contesting this conclusion and show that the phenomenon of deterministic bias is an artifact of improperly applying neurosymbolic AI.
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