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Certification systems for machine learning: Lessons from sustainability
Regulation & Governance ( IF 3.375 ) Pub Date : 2021-06-09 , DOI: 10.1111/rego.12417
Kira J.M. Matus, Michael Veale

Concerns around machine learning’s societal impacts have led to proposals to certify some systems. While prominent governance efforts to date center around networking standards bodies such as the Institute of Electrical and Electronics Engineers (IEEE), we argue that machine learning certification should build on structures from the sustainability domain. Policy challenges of machine learning and sustainability share significant structural similarities, including difficult to observe credence properties, such as data collection characteristics or carbon emissions from model training, and value chain concerns, including core-periphery inequalities, networks of labor, and fragmented and modular value creation. While networking-style standards typically draw their adoption and enforcement from functional needs to conform to enable network participation, machine learning, despite its digital nature, does not benefit from this dynamic. We therefore apply research on certification systems in sustainability, particularly of commodities, to generate lessons across both areas, informing emerging proposals such as the EU’s AI Act.

中文翻译:

机器学习认证系统:可持续发展的经验教训

对机器学习的社会影响的担忧导致了对某些系统进行认证的提议。虽然迄今为止突出的治理工作围绕着电气和电子工程师协会 (IEEE) 等网络标准机构,但我们认为机器学习认证应该建立在可持续性领域的结构上。机器学习和可持续性的政策挑战具有显着的结构相似性,包括难以观察的可信性属性,例如数据收集特征或模型训练的碳排放,以及价值链问题,包括核心边缘不平等、劳动力网络以及碎片化和模块化创造价值。虽然网络风格的标准通常从功能需求中获得采用和执行,以符合实现网络参与,但机器学习尽管具有数字性质,但并没有从这种动态中受益。因此,我们应用对可持续性认证系统的研究,特别是商品,以在这两个领域产生经验教训,为欧盟的人工智能法案等新兴提案提供信息。
更新日期:2021-06-10
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