Abstract

Contemporary discussions and disagreements about the deployment of machine learning, especially in criminal justice contexts, have no foreseeable end. Developers, practitioners and regulators could however usefully look back one hundred years to the similar arguments made when polygraph machines were first introduced in the United States. While polygraph devices and machine learning operate in distinctly different ways, at their heart, they both attempt to predict something about a person based on how others have behaved. This paper, through an historical perspective, examines the history of the polygraph within the justice system – both in courts and during criminal investigations - and draws parallels to today’s discussion. It can be argued that the promotion of lie detectors supported a reforming legal realist approach, something that continues today in the debates over the deployment of machine learning where ‘public good’ aims are in play, and raises questions around how key principles of the rule of law can best be upheld. Finally, this paper will propose a number of regulatory solutions informed by the early lie detector experience.

Highlights

  • If machine learning and artificial intelligence were people, they would be teenagers, and young teenagers at that

  • While polygraph devices and machine learning operate in distinctly different ways, at their heart, they both attempt to predict something about a person based on how others have behaved

  • It can be argued that the promotion of lie detectors supported a reforming legal realist approach, something that continues today in the debates over the deployment of machine learning where ‘public good’ aims are in play, and raises questions around how key principles of the rule of law can best be upheld

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Summary

Northumbria Research Link

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Introduction
Prediction and categorisation of human behaviour
Societal aims
Accuracy and the human in the loop
Reformist legal realism
The lie detector growing up
Regulatory lessons for machine learning
Findings
Conclusion

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