Book Review: Ulrike Schultz, Gisela Shaw, Margaret Thornton & Rosemary Auchmuty (eds). 2021. Gender and Careers in the Legal Academy. (1st ed), Oxford, UK; New York, NY: Hart Publishing, ISBN: 9781509923113 2021, 569 pp.
Bias can result in partiality in historical accounts and confirmation bias results in statements and characterisations being accepted as factual without reliance on critical tests. This paper provides an example of how a qualitative research technique called ‘nominal group technique’ (NGT) was used in a legal history project to control bias. The observations and conclusions may have a bearing on the part that NGT could play in other areas of legal research and historical research.
Artificial intelligence is an alluring technology which companies and governments hope to benefit from. In many circumstances a condition of its use is that humans can understand an explanation of why the action of an AI system took place. This has encouraged the development of a field of “explainable artificial intelligence”, or XAI. Much of the work in this field has been encouraged by the US Defense Advanced Research Projects Agency (DARPA), through its XAI program initiated in 2016. This paper argues that an underacknowledged challenge of XAI is that unlike most traditional technology, many AI systems contain inherent uncertainty. These systems are widely described as “black boxes”, and can be described only through their behavior, a technique described in the literature as post-hoc, rather than through an understanding of their functioning. Explaining such systems is akin to explaining the functioning of the natural world, rather than explaining the functioning of a known technology. While extensive work has been undertaken to explain the behavior of black box AI systems, there are limitations to the certainty that a post-hoc method can bring. Recognizing this is an important part of understanding the limitations of post-hoc reasoning in the use of advanced AI systems. Far simpler technologies have been seen to cause significant social damage: the UK Post Office Horizon system, and the Australian federal government Robodebt program. Coming to advanced AI system examples, two recent prestigious reports on AI systems and law display an unreasoned enthusiasm for AI explainability. AI researchers should be acknowledging that many advanced AI systems remain black boxes, that post-hoc explanations of these are inferences describing how the AI system may function, not how it does function, and the application of these technologies should be managed accordingly. Otherwise, the search for explanations may simply become a reason to believe.
Book Review: Simon Chesterman.. We, the Robots? Regulating Artificial Intelligence and the Limits of the Law. UK: Cambridge University Press, 2021, pp. 289 ISBN 978-1-316-51768-0 Hardback, https://doi.org/10.1017/9781009047081
Book Review: Philip Ayers (ed) The Washington Diaries of Owen Dixon 1942-1944, Alexandria, NSW: Federation Press, 2020 - 392pp. incl index 19pp. ISBN: 9781760022549-
