The legal Concept of Fraud in Machine Learning
In legal terms fraud is not mere lying; it is seeking to obtain advantage, usually monetary, or to put someone else at a disadvantage by lies and deceit. In order to prove a person has acted fraudulently, it is necessary prove that that person was either deliberately or recklessly intended to defraud. There are fine lines to be drawn between 'negligent', 'reckless' and 'deliberate' acts and only the last two suffice to prove fraud. Whilst training machines to detect 'fraud' such fine lines must be observed. Otherwise, framing an action as a 'fraud' whereas there was no intention to 'defraud' might have serious consequences especially for consumers and the relevant actor who is responsible for such misclassification might be found liable to compensate losses suffered by many consumers who have been affected by it. I therefore look forward to discovering more at Berlin Summit how experts define and design 'fraud detection' in their machine training processes.
Özlem Gürses is Professor of Commercial Law at King’s College London. She specialises in insurance and reinsurance law. Özlem is the author of Reinsuring Clauses (Informa), Marine Insurance Law (Routledge), Insurance of Commercial Risks (Sweet and Maxwell), and The Compulsory Motor Vehicle Insurance (Informa) as well as numerous articles published on insurance and reinsurance related topics. Özlem sits in the British Insurance Law Association Committee and the Presidential Council of the International Insurance Law Association (AIDA). She is Vice-Chair of the Reinsurance Working Party of AIDA. Özlem teaches insurance and reinsurance law at King’s College London and abroad, including National University of Singapore, University of Hamburg and World Maritime University, Malmö