We are living in an algorithmic age where mathematics and computer science are coming together in powerful new ways to influence, shape and guide our behaviour and the governance of our societies. As these algorithmic governance structures proliferate, it is vital that we ensure their effectiveness and legitimacy. That is, we need to ensure that they are an effective means for achieving a legitimate policy goal that are also procedurally fair, open and unbiased. But how can we ensure that algorithmic (...) governance structures are both? This article shares the results of a collective intelligence workshop that addressed exactly this question. The workshop brought together a multidisciplinary group of scholars to consider barriers to legitimate and effective algorithmic governance and the research methods needed to address the nature and impact of specific barriers. An interactive management workshop technique was used to harness the collective intelligence of this multidisciplinary group. This method enabled participants to produce a framework and research agenda for those who are concerned about algorithmic governance. We outline this research agenda below, providing a detailed map of key research themes, questions and methods that our workshop felt ought to be pursued. This builds upon existing work on research agendas for critical algorithm studies in a unique way through the method of collective intelligence. (shrink)

Anti-selection occurs when information asymmetry exists between insurers and applicants. When an applicant knows they are at high risk of loss, but the insurer does not, the applicant may try to use this knowledge differential to secure insurance at a lower premium that does not match risk.

The science and technology of genetic testing is rapidly advancing with the consequences that genetic testing may well offer the prospect of being able to detect the onset of future disabilities. Some recent research also indicates that certain behavioural profiles may have a strong genetic basis, such as the determination to succeed and win or the propensity for risk-taking, which may be of interest to third parties. However, as this technology becomes more prevalent there is a danger that the genetic (...) information may be misused by entities such as employers, insurance companies, educational facilities, and finance companies and that people with particular genetic profiles may be discriminated against.This book explores the different types and the potential uses of genetic testing. The book highlights the ethical and legal dilemmas and the challenges arising as a result of emerging and rapidly advancing genetic science. Chapters in the book assess the importance and impact of the US Genetic Information Non-Discrimination Act (GINA) in the employment and health insurance contexts asking whether it is proving to be an effective tool in addressing the issue of genetic discrimination and alleviating individuals' fears of discrimination. The book then goes on to make the case for regulation at the European Union level in order to protect the privacy of genetic information and to prevent the discriminatory use of genetic information in Europe. It considers what can be learnt from the experience of the US in addressing ethical and legal issues as well as the impact that the UN Convention on the Rights of Persons with Disabilities is having on this debate within the EU. (shrink)