What happens when there is not enough data to train machine learning algorithms? In recent years, so-called ‘synthetic data’ have been increasingly used to add to or supplement the training regimes of various machine learning algorithms. Seeking to read the notion of supplementarity differently through an engagement with the work of Jacques Derrida, I propose that the nascent emergence of synthetic data embodies what I call the logic of the synthetic supplement in algorithmic societies. I argue, on the one hand, that the synthetic supplement promises and claims to resolve the ethico-political tensions, frictions, and intractabilities of machine learning. On the other hand, it always falls short of these promises because it necessarily intervenes in that which it claims to merely augment. Ultimately, this means that the gaps and frictions of machine learning cannot be completely filled, supplemented, or resolved.

中文翻译：

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算法社会中综合补充的逻辑

当没有足够的数据来训练机器学习算法时会发生什么？近年来，所谓的“合成数据”越来越多地用于添加或补充各种机器学习算法的训练机制。为了通过雅克·德里达的工作以不同的方式解读补充性的概念，我提出合成数据的新生体现了我所说的算法社会中合成补充的逻辑。一方面，我认为合成补充承诺并声称可以解决机器学习的伦理政治紧张、摩擦和棘手问题。另一方面，它总是达不到这些承诺，因为它必然会干预它声称只是增强的东西。最终，这意味着机器学习的空白和摩擦无法完全填补、补充或解决。