As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, then an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target environments. Yet the latter part of the contract depends on human inductive predictions or generalisations, which infer a uniformity between the trained ML model and the targets. The article asks how we justify the contract between human and machine learning. It is argued that the justification becomes a pressing issue when we use ML to reach “elsewhere” in space and time or deploy ML models in non-benign environments. The article argues that the only viable version of the contract can be based on optimality (instead of on reliability, which cannot be justified without circularity) and aligns this position with Schurz's optimality justification. It is shown that when dealing with inaccessible/unstable ground-truths (“elsewhere” and non-benign targets), the optimality justification undergoes a slight change, which should reflect critically on our epistemic ambitions. Therefore, the study of ML robustness should involve not only heuristics that lead to acceptable accuracies on testing sets. The justification of human inductive predictions or generalisations about the uniformity between ML models and targets should be included as well. Without it, the assumptions about inductive risk minimisation in ML are not addressed in full.

中文翻译：

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机器学习中的人类感应

随着我们认知野心的增长，共同的科学努力越来越依赖机器学习 (ML)。该领域基于一个单一的实验范式，其中包括将可用数据拆分为训练和测试集，并使用后者来衡量经过训练的 ML 模型对未见样本的泛化程度。如果模型达到可接受的精度，则后验的合同在人和模型之间生效，据说允许其部署到目标环境。然而，合同的后半部分依赖于人类的归纳预测或概括，从而推断出训练的 ML 模型和目标之间的一致性。这篇文章询问我们如何证明人类和机器学习之间的契约是合理的。有人认为，当我们使用 ML 在空间和时间上到达“其他地方”或在非良性环境中部署 ML 模型时，正当性成为一个紧迫的问题。文章认为，唯一可行的合同版本可以基于最优性（而不是基于可靠性，没有循环性就无法证明这一点），并将这一立场与 Schurz 的最优性证明保持一致。结果表明，在处理无法访问/不稳定的基本事实（“其他地方”和非良性目标）时，最优性证明会发生轻微变化，这应该批判性地反映我们的认知野心。因此，ML 鲁棒性的研究不仅应该涉及能够在测试集上获得可接受的准确度的启发式方法。还应包括对 ML 模型和目标之间一致性的人类归纳预测或概括的证明。没有它，关于 ML 中归纳风险最小化的假设将无法完全解决。还应包括对 ML 模型和目标之间一致性的人类归纳预测或概括的证明。没有它，关于 ML 中归纳风险最小化的假设将无法完全解决。还应包括对 ML 模型和目标之间一致性的人类归纳预测或概括的证明。没有它，关于 ML 中归纳风险最小化的假设将无法完全解决。