Understanding judicial discretion

The main aim of this paper is to clarify the dispute over judicial discretion by distinguishing the different senses in which claims about judicial discretion can be understood and by examining the arguments for these various interpretations. Three different levels of dispute need to be recognized. The first concerns whether judges actually do exercise discretion, the second involves whether judges are entitled to exercise discretion, and the third is about the proper institutional role of judges. In this context, the views of Dworkin, Raz, Perry, Greenawalt, and Sartorius are examined. Finally, it is suggested that a resolution of the judicial discretion controversy requires a satisfactory theory of the justification of judicial decisions.     

When Is Science Used in Science Policy? Examining the Importance of Scientific and Technical Information in National Research Council Reports

A frequent lament among researchers is that public policy makers should pay more attention to scientific and technical information (STI). If there is any single area where one might expect STI to be used in public policy making and agenda setting it is in science and technology policy. Many of the policy makers in science and technology policy are themselves scientists or researchers and presumably would prove especially receptive to STI. However, STI is only one of many types of information used in policy making and policy actors often differ in the extent to which they view STI as credible, particularly compared to other types of potentially policy‐relevant information. Research on credibility (the believability of information, information types, and media) has shown variance and policy makers’ “credibility maps.” Thus, some policy makers have preference for formal information generally and STI specifically, but others privilege raw data, personal experience, authority, history and anecdote, analogical reasoning, or conformance to ideology, to name just a few of the information choices. Here, we build on the current researchers’ previous bibliometrics‐based work and use data from 41 semi‐structured elite interviews with National Research Council (NRC) executives and staff and NRC committee members concerning the use of STI in reports issued by the NRC. Findings show that the use of STI in NRC reports varies according to the nature of the inquiry and the sponsor. Information used in the reports is based on not only the assessed credibility of information but also its perceived direct relevance and the availability of STI as compared to other types of information. In general, the amount of STI in the NRC reports tends to have modest effects on the likelihood that reports will be used in policy making or by the mass media. More important factors include the timing of the report with respect to political agendas, the party requesting the report, and the enacted roles of NRC staff members and committee chairs.     

The ICO and artificial intelligence: The role of fairness in the GDPR framework

The year 2017 has seen many EU and UK legislative initiatives and proposals to consider and address the impact of artificial intelligence on society, covering questions of liability, legal personality and other ethical and legal issues, including in the context of data processing. In March 2017, the Information Commissioner's Office (UK) updated its big data guidance to address the development of artificial intelligence and machine learning, and to provide (GDPR), which will apply from 25 May 2018.This paper situates the ICO's guidance in the context of wider legal and ethical considerations and provides a critique of the position adopted by the ICO. On the ICO's analysis, the key challenge for artificial intelligence processing personal data is in establishing that such processing is fair. This shift reflects the potential for artificial intelligence to have negative social consequences (whether intended or unintended) that are not otherwise addressed by the GDPR. The question of ‘fairness’ is an important one, to address the imbalance between big data organisations and individual data subjects, with a number of ethical and social impacts that need to be evaluated.