This article expands on recent studies of machine learning or artificial intelligence (AI) algorithms that crucially depend on benchmark datasets, often called 'ground truths.' These ground-truth datasets gather input-data and output-targets, thereby establishing what can be retrieved computationally and evaluated statistically. I explore the case of the Tumor nEoantigen SeLection Alliance (TESLA), a consortium-based ground-truthing project in personalized cancer immunotherapy, where the 'truth' of the targets-immunogenic neoantigens-to be retrieved by the would-be AI algorithms depended on a broad technoscientific network whose setting up implied important organizational and material infrastructures. The study shows that instead of grounding an undisputable 'truth', the TESLA endeavor ended up establishing a contestable reference, the biology of neoantigens and how to measure their immunogenicity having slightly evolved alongside this four-year project. However, even if this controversy played down the scope of the TESLA ground truth, it did not discredit the whole undertaking. The magnitude of the technoscientific efforts that the TESLA project set into motion and the needs it ultimately succeeded in filling for the scientific and industrial community counterbalanced its metrological uncertainties, effectively instituting its contestable representation of 'true' neoantigens within the field of personalized cancer immunotherapy (at least temporarily). More generally, this case study indicates that the enforcement of ground truths, and what it leaves out, is a necessary condition to enable AI technologies in personalized medicine.

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人工智能的基础：在个性化癌症免疫治疗中执行新抗原预测的基准。

本文扩展了机器学习或人工智能 (AI) 算法的最新研究，这些算法主要依赖于基准数据集（通常称为“基本事实”）。这些真实数据集收集输入数据和输出目标，从而确定可以通过计算检索和统计评估的内容。我探讨了肿瘤新抗原选择联盟 (TESLA) 的案例，这是一个基于联盟的个性化癌症免疫治疗实地验证项目，其中目标的“真实性”——免疫原性新抗原——将由未来的人工智能算法检索在广泛的技术科学网络上，其建立意味着重要的组织和物质基础设施。该研究表明，特斯拉的努力并没有建立一个无可争议的“真相”，而是最终建立了一个有争议的参考，新抗原的生物学以及如何测量其免疫原性随着这个为期四年的项目而略有发展。然而，即使这场争议淡化了 TESLA 事实真相的范围，也没有损害整个事业的声誉。TESLA 项目启动的技术科学努力的规模及其最终成功满足科学和工业界的需求抵消了其计量不确定性，有效地在个性化癌症免疫治疗领域建立了其“真正”新抗原的有争议的代表。至少暂时）。更一般地说，本案例研究表明，执行基本事实及其遗漏的内容是在个性化医疗中实现人工智能技术的必要条件。