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Ecological risk assessment embodied in an adaptive management framework is becoming the global standard approach for formally assessing and managing the ecological risks of technology and development. Ensuring the continual improvement of ecological risk assessment approaches is partly achieved through the dissemination of not only the types of risk assessment approaches used, but also their efficacy. While there is an increasing body of literature describing the results of general comparisons between alternate risk assessment methods and models, there is a paucity of literature that post hoc assesses the performance of specific predictions based on an assessment of risk and the effectiveness of the particular model used to predict the risk. This is especially the case where risk assessments have been used to grant consent or approval for the construction of major infrastructure projects. While postconstruction environmental monitoring is increasingly commonplace, it is not common for a postconstruction assessment of the accuracy and performance of the ecological risk assessment and underpinning model to be undertaken. Without this "assessment of the assessment," it is difficult for other practitioners to gain insight into the performance of the approach and models used and therefore, as argued here, this limits the rate of improvement of risk assessment approaches.

<b><i>Background:</i></b> The rapidity of technological change in genetics is not always matched by the uptake of this new knowledge into practice. Increasing genetic knowledge has already led to legal liability for those who have not used it properly, such as not informing patients or their families of potential genetic risk. A similar outcome is also of concern in the case of risk prediction models used for hereditary breast cancer. <b><i>Results:</i></b> No legal case has directly addressed the use of risk prediction models. However, as genetic medicine and risk prediction models become more widely used, the prospect of a lawsuit will also increase. Current case law is instructive on the circumstances under which medical liability actions could be pursued and circumstances under which liability is unlikely, such as the provision of faulty family history information by a patient. <b><i>Conclusions:</i></b> There is existing case law on family history and genetics that parallels in many respects the use of risk prediction models. However, the idea of a bad 'prediction' is a difficult legal concept. Outside of a plain misuse or failure to use a risk prediction model when circumstances clearly required it, there is little legal guidance presently available to determine the risk for medical liability.

As more and more governments adopt algorithms to support bureaucratic decision-making processes, it becomes urgent to address issues of responsible use and accountability. We examine a contested public service algorithm used in Danish job placement for assessing an individual's risk of long-term unemployment. The study takes inspiration from cooperative audits and was carried out in dialogue with the Danish unemployment services agency. Our audit investigated the practical implementation of algorithms. We find (1) a divergence between the formal documentation and the model tuning code, (2) that the algorithmic model relies on subjectivity, namely the variable which focus on the individual's self-assessment of how long it will take before they get a job, (3) that the algorithm uses the variable "origin"to determine its predictions, and (4) that the documentation neglects to consider the implications of using variables indicating personal characteristics when predicting employment outcomes. We discuss the benefits and limitations of cooperative audits in a public sector context. We specifically focus on the importance of collaboration across different public actors when investigating the use of algorithms in the algorithmic society.

<i>Background:</i> Medical risk prediction models estimate the likelihood of future health-related events. Many make use of information derived from analysis of the genome. Models predict health outcomes such as cardiovascular disease, stroke and cancer, and for some conditions several models exist. Although risk models can help decision-making in clinical medicine and public health, they can also be harmful, for example, by misdirecting clinical effort away from those who are most likely to benefit towards people with less need, thus exacerbating health inequalities. <i>Discussion:</i> Risk prediction models need careful assessment before implementation, but the current approach to their development, evaluation and implementation is inappropriate. As a result, some models are pressed into use before it is clear whether they are suitable, while in other cases there is confusion about which model to use. This paper proposes an approach to the appraisal of risk-scoring models, based on a conference of UK experts. <i>Summary:</i> By specifying what needs to be known before a model can be judged suitable for translation from research into practice, we can ensure that useful models are taken up promptly, that less well-proven ones undergo further evaluation and that resources are not wasted on ineffective ones.

With the aging of western society the contribution to morbidity of diseases of the elderly, such as dementia, will increase exponentially. Thorough preventative and curative strategies are needed to constrain the increasing prevalence of these disabling diseases. Better understanding of the pathogenesis of disease will enable development of therapy, prevention and the identification of high-risk groups in the population. Here, we review the genetic epidemiology of Alzheimer's disease, the most common cause of dementia in the western world. The search for genetic risk factors, though far from completed, has been of major importance for understanding the pathogenesis of Alzheimer's disease. Although effective therapy is still awaited, these findings have led to new avenues for the development of drugs.

The best predictor of sexual offense recidivism is believed to be past sexual offending. However, clinical observations of treated individuals often contradict this view. Consequently, this relationship was evaluated, using a minimum follow-up period of 18 months. The samples were adult men and consisted of 127 treatment noncompleters who terminated treatment because of inadequate motivation/unacceptable behavior, legal transfer, or by request and 150 treatment completers who underwent a minimum of 6 months of 32-35 hours per week of therapy. Sexual offense conviction recidivism and prior sexual offense convictions were significantly related in noncompleters (r = .31, p < .05) but were not significantly related in completers (r = .03, p > .05). These two correlations differed significantly (two-tailed p < .05). There was no difference (p > .05) in the average number of prior sexual offense convictions between completers and noncompleters. The findings are supportive of treatment interventions, although they question the validity of prior sexual offenses in treated offenders for release decisions and/or risk prediction.

One hundred thirty child sexual abusers were diagnosed using each of following four methods: (a) phallometric testing, (b) strict application of Diagnostic and Statistical Manual of Mental Disorders (4th ed., text revision [DSM-IV-TR]) criteria, (c) Rapid Risk Assessment of Sex Offender Recidivism (RRASOR) scores, and (d) "expert" diagnoses rendered by a seasoned clinician. Comparative utility and intermethod consistency of these methods are reported, along with recidivism data indicating predictive validity for risk management. Results suggest that inconsistency exists in diagnosing pedophilia, leading to diminished accuracy in risk assessment. Although the RRASOR and DSM-IV-TR methods were significantly correlated with expert ratings, RRASOR and DSM-IV-TR were unrelated to each other. Deviant arousal was not associated with any of the other methods. Only the expert ratings and RRASOR scores were predictive of sexual recidivism. Logistic regression analyses showed that expert diagnosis did not add to prediction of sexual offence recidivism over and above RRASOR alone. Findings are discussed within a context of encouragement of clinical consistency and evidence-based practice regarding treatment and risk management of those who sexually abuse children.