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Beyond success: predicting the likelihood of a case eventually contributing to a shift in case law

  • Full or part time
  • Application Deadline
    Tuesday, January 22, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Uncertainty: whether it be developing tools to help communicate it in lay-professional encounters, tools that take into account human uncertainty (and the consequent need for continuing change in a legal system), or methods to flag up / explain uncertainty to professional (or public policy) users of data science tools.

Beyond success: predicting the likelihood of a case eventually contributing to a shift in case law

There is a risk that a focus on efficiency –for instance through increasingly performant prediction tools- will make us blind to the fact that increased automation is changing the very nature of the legal system. Given their impressive accuracy (Aletras, Tsarapatsanis, Preoţiuc-Pietro, & Lampos, 2016; Katz, Bommarito, & Blackman, 2017; Sulea, Zampieri, Vela, & van Genabith, 2017), for instance, it is highly likely that lawyers will increasingly refer to prediction tools to advise clients on whether their claim is worth pursuing. This may seem like a welcome innovation, except for the fact that this will insidiously contribute to a growing degree of conservatism, since cases with a low success prediction are unlikely to be heard in court. This in turn makes organic changes within case law less likely: shifts in case law often depend upon an accumulation of previous, unsuccessful cases that trigger a growing number of dissenting voices (both within and without the judiciary). Now, there may be ways of developing tools that not only predict the chances of success in court, but also the likelihood that a particular case will eventually contribute to some organic evolution within case law (particularly when judges’ dissenting opinions are recorded). Such tools could be a vital complement to ‘success prediction’ tools.

This particular PhD project requires a strong background in natural language processing.

The successful candidate will have a first class undergraduate and Masters degree in any of the disciplines relevant to the above project (this includes applied mathematics, computer science, philosophy, law, medicine, psychology, public policy) together with demonstrable expertise in statistical / data science. The successful candidate also needs to demonstrate a genuine interest in professional ethics and/or public policy.

Funding Notes

To support students the Turing offers a generous tax-free stipend of £20,500 per annum, a travel allowance and conference fund, and tuition fees for a period of 3.5 years.

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