Polygraph-based deception detection and Machine Learning. Combining the Worst of Both Worlds?

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.

Keywords

• Polygraph screening
• Machine learning
• Research methodology
• Legal process
• Classification
• Inference structures