论文标题
可以解释的AI是反对模型复杂性的种族吗？
Is explainable AI a race against model complexity?
论文作者
论文摘要
随着模型的规模和复杂性的增长，解释智能系统的行为将变得越来越多地具有挑战性。我们可能无法期望对大脑规模模型做出的每个预测进行解释，也无法期望解释能保持客观或非政治性。我们对这些模型的功能主义理解比我们想象的要少。模型先于解释，即使模型和解释都是不正确的，也可以很有用。解释性可能永远不会赢得与复杂性的竞赛，但这比看起来要小的问题。
Explaining the behaviour of intelligent systems will get increasingly and perhaps intractably challenging as models grow in size and complexity. We may not be able to expect an explanation for every prediction made by a brain-scale model, nor can we expect explanations to remain objective or apolitical. Our functionalist understanding of these models is of less advantage than we might assume. Models precede explanations, and can be useful even when both model and explanation are incorrect. Explainability may never win the race against complexity, but this is less problematic than it seems.
