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Abstract：
At a time when developments in computational approaches, often associated with the now much-vaunted terms Machine Learning (ML) and Artificial Intelligence (AI), face increasing challenges in terms of fairness, transparency and accountability, the temptation for researchers to apply mainstream ML methods to virtually any type of data seems to remain irresistible. In this paper we critically examine a recent proposal to apply ML to polygraph screening results (where human interviewers have made a conclusion about deception), which raises several questions about the purpose and the design of the research, particularly given the vacuous scientific status of polygraph-based procedures themselves. We argue that in high-stake environments such as criminal justice and employment practice, where fundamental rights and principles of justice are at stake, the legal and ethical considerations for scientific research are heightened. Specifically, we argue that the combination of ambiguously labelled data and ad hoc ML models does not meet this requirement. Worse, such research can inappropriately legitimise otherwise scientifically invalid, indeed pseudo-scientific methods such as polygraph-based deception detection, especially when presented in a reputable scientific journal. We conclude that methodological concerns, such as those highlighted in this paper, should be addressed before research can be said to contribute to resolving any of the fundamental validity issues that underlie methods and techniques used in legal proceedings.
摘要：
在计算方法发展的时候，通常与现在备受吹捧的机器学习(ML)和人工智能(AI)术语联系在一起，在公平方面面临越来越多的挑战，透明度和问责制，研究人员将主流ML方法应用于几乎任何类型的数据的诱惑似乎仍然无法抗拒。在本文中，我们批判性地研究了最近提出的将ML应用于测谎仪筛查结果的建议（其中人类采访者已经得出了关于欺骗的结论），这对研究的目的和设计提出了几个问题，特别是考虑到基于测谎仪的程序本身的空虚科学地位。我们认为，在刑事司法和就业实践等高风险环境中，在基本权利和正义原则受到威胁的地方，科学研究的法律和道德考虑得到了加强。具体来说,我们认为，模糊标记的数据和临时机器学习模型的组合不符合这一要求。更糟糕的是,这样的研究可能会不恰当地合法化，否则在科学上是无效的，实际上是伪科学方法，如基于测谎仪的欺骗检测，特别是当在一个著名的科学期刊上发表时。我们得出的结论是，方法论上的关注，如本文所强调的，在研究可以说有助于解决法律诉讼中使用的方法和技术的任何基本有效性问题之前，应该予以解决。
