Database

Artificial intelligence and liability insurance. After a brief introduction to AI, some of the liability issues are introduced in this brief report. AI simulations Essentially AI systems are structured in the following way. The computer is supplied with digital information, coded with a set of statistical tools and a set of purposes that the customer would like to work with. Once data, methods and purposes are properly coded, the AI generates useful simulations of the data and can be used to identify the degree to which new situations meet the set of purposes. Missing data is effectively imputed from the overall model. As a very much simplified example. An example helps set the scene for the discussion of legal liability. Intended purpose: A liability claims manager wants to identify claims which are suited to making an immediate offer, and those which are best examined more closely. Process: Having coded ten thousand claims of the relevant sort, AI generates a statistically weighted s

Does mobile phone radiation cause cancer? Principle findings Following detailed lab experiments, researchers at the US National Toxicity Program (NTP[1]) have published[2] data which provides evidence of a statistically significant association between high exposure to radio frequency emfs (RF emfs) and malignant schwannoma of the heart in male rats. The evidence revealed thus far is consistent with a causal explanation. Extrapolating to human disease is however unclear; as this depends on the mechanism behind this association. It is hoped that in the final report, NTP will be as specific about this mechanism as they can be. As it is, there are signs in the data that the mechanism may be unusual, so much so that a new challenge could be made to the norms of cancer compensation. The final NTP report is due before the winter of 2018. As a draft for peer review, the NTP report may not be quoted. A challenge to the norms of cancer compensation The courts, and therefore insurers, nearly alwa

  Managing liability ENIDs using Radar Radar The Radar service provides an expert view of science-based changes to liability insurance exposure and provides quantitative estimates of that exposure. Some examples from the Radar database are appended here: American football and brain disease, LED lights and eye injury, wood dust and nasal cancer and, 3D printing. History The Radar service was first developed under contract to ABI[1] as a collective work on emerging liability risks. Radar was then commercialised by Re: Liability (Oxford) Ltd[2]. As ABI evolved, the service was passed to liability insurers to take up individually. Liability emerging risks are now more often referred to as liability ENIDs[3], for example in Solvency II guidance. Aim The aim is to inform judgement of factors that could drive changes to liability insurance exposure and to estimate the resulting size of that change. Method This follows the familiar method of identify, evaluate and take action. Identificat

Glyphosate Glyphosate[1] (GP) was introduced into the market in the early 1970s. It is a widely approved and widely used herbicide. If it caused injury during foreseeable use then liability insurance would be involved. Given both professional and amateur usage, the foreseeable uses would be many and non-compliant standards of use would be unsurprising. Generic and specific causation would be the key tests of liability. News Recent interest in causation includes requests to regulators to label foods as containing traces of glyphosate on the grounds that it may cause cancer. This would have a significant impact on sales of food and glyphosate. This follows on from a 2015 decision made by the International Agency for Research on Cancer (IARC) to classify glyphosate as a probable human carcinogen. Californian law requires that it adopts IARC decisions[2]. The DeWayne Johnson (aged 46) claim for non-Hodgkin lymphoma (NHL) recently[3] reached a key step in awarding damages. The claimant made

Mania from cured meat? New research from the USA identifies an association between the consumption of cured meat products and mania. The evidence comes from both human medical cases and from studies of rats. Mania is a component of bipolar disorder and is managed by medication such as lithium. It is a recurrent problem often associated with poor decision-making. Liability for first party loss caused by legally defective cured meat products could extend to the financial decisions made during an episode of mania. Third parties might include the victims of road traffic accidents. What did the research show? Compared with controls, a history of eating cured meat preparations was significantly associated with being in the mania group (mean age 34, 66% female); adjusted odds ratio = 3.49 (2.24–5.45). There was no significant association with undercooked meat, raw meat, undercooked fish or raw fish. A history of eating cured meat preparations was not associated with a diagnosis of schizophren

Modelling of emerging liability risks can facilitate product launches, improve insurance policies and improve financial sustainability   Emerging risks have, by definition, always been a challenge for companies and the insurance industry. Being new, there’s relatively little data with which to assess the probability of such risks materialising and the severity and frequency of ill-effects if they do. As companies and industries change ever-more quickly, the pace of innovation also increases, and so arguably does the emergence of new risks. History teaches us that the products, trends and technologies we assume to be safe and adopt as part of our everyday lives are not without their risks. For example, while the toxic properties of asbestos are now well-known, it was once considered a ‘wonder material’. Businesses could be healthier and more sustainable if they could identify significant new liability risks sooner – in other words, if they knew what the next asbestos would be. Unfo