User Driven Model Adjustment via Boolean Rule Explanations

AI solutions are heavily dependant on the quality and accuracy of the input training data, however the training data may not always fully reflect the most up-to-date policy landscape or may be missing business logic. The advances in explainability have opened the possibility of allowing users to interact with interpretable explanations of ML predictions in order to inject modifications or constraints that more accurately reflect current realities of the system. In this paper, we present a solution which leverages the predictive power of ML models while allowing the user to specify modifications to decision boundaries. Our interactive overlay approach achieves this goal without requiring model retraining, making it appropriate for systems that need to apply instant changes to their decision making. We demonstrate that user feedback rules can be layered with the ML predictions to provide immediate changes which in turn supports learning with less data

Interpretable Differencing of Machine Learning Models

Understanding the differences between machine learning (ML) models is of interest in scenarios ranging from choosing amongst a set of competing models, to updating a deployed model with new training data. In these cases, we wish to go beyond differences in overall metrics such as accuracy to identify where in the feature space do the differences occur. We formalize this problem of model differencing as one of predicting a dissimilarity function of two ML models' outputs, subject to the representation of the differences being human-interpretable. Our solution is to learn a Joint Surrogate Tree (JST), which is composed of two conjoined decision tree surrogates for the two models. A JST provides an intuitive representation of differences and places the changes in the context of the models' decision logic. Context is important as it helps users to map differences to an underlying mental model of an AI system. We also propose a refinement procedure to increase the precision of a JST. We demonstrate, through an empirical evaluation, that such contextual differencing is concise and can be achieved with no loss in fidelity over naive approaches.Comment: UAI 202

On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach

Interpretable and explainable machine learning has seen a recent surge of interest. We focus on safety as a key motivation behind the surge and make the relationship between interpretability and safety more quantitative. Toward assessing safety, we introduce the concept of maximum deviation via an optimization problem to find the largest deviation of a supervised learning model from a reference model regarded as safe. We then show how interpretability facilitates this safety assessment. For models including decision trees, generalized linear and additive models, the maximum deviation can be computed exactly and efficiently. For tree ensembles, which are not regarded as interpretable, discrete optimization techniques can still provide informative bounds. For a broader class of piecewise Lipschitz functions, we leverage the multi-armed bandit literature to show that interpretability produces tighter (regret) bounds on the maximum deviation. We present case studies, including one on mortgage approval, to illustrate our methods and the insights about models that may be obtained from deviation maximization.Comment: Published at NeurIPS 202

Reply to : On powerful GWAS in admixed populations

Non peer reviewe

Determining the Food Choice Motivations of Irish Teens and Their Association with Dietary Intakes, Using the Food Choice Questionnaire

During adolescence, teens start making their own food choices. While health and nutrition are important, practical and social concerns are also influential. This study aims to determine factors that motivate the food choices of Irish teens (using Food Choice Questionnaire), using data from the National Teens\u27 Food Survey II (N = 428, 50% male, 13-18 years), and to identify how these motivations relate to dietary intakes (4-day semi-weighed food diaries). Data analysis used PCA to determine the food choice motivation subscales, and correlation and comparative statistical tests (t-test, ANOVA). Eight motivating factors were identified for Irish teens: Sensory Appeal, Price & Availability, Health & Natural Content, Familiarity, Ease of Preparation, Mood, Weight Control, and Ethical Concerns. Health and practical aspects to food choice (Price, Availability, Ease of Preparation) are important for teens, but taste (Sensory Appeal) remains a key influence. Food choice motivations vary by sex and by age, BMI status and weight perception, where girls were more motivated by health, weight control, mood and ethical concerns, and older teens were more influenced by mood and ease of preparation. Both those classified as overweight and those who perceived they were overweight were motivated more by weight control and mood for their food choices, whereas those who perceived their weight to be correct placed more importance on health and natural content. Those motivated by weight control had lower energy and higher protein intakes, and those motivated by health and natural content had more health promoting behaviours, with higher physical activity, lower screen time, and higher protein intakes. Understanding the motivations of teens\u27 food choice can help understand why they struggle to meet dietary recommendations, and help to develop more effective health promotion messages by capitalising on the key motivations in the population

The underlying role of food guilt in adolescent food choice : a potential conceptual model for adolescent food choice negotiations under circumstances of of conscious internal conflict

Food choice decisions are challenging to conceptualise, and literature is lacking specific to adolescent food choice decisions. Understanding adolescent nutrition and food choice is becoming increasingly important. This research aims to understand what influences the food choices of Irish adolescents, and the mental negotiations occurring in food-based decisions. Additionally, it aims to develop a holistic conceptual model of food choice, specific to adolescents. A qualitative study was conducted in N = 47 Irish adolescents, via focus group discussions using vignettes to introduce discussion topics around food and eating habits. Data were analysed using reflexive thematic analysis, involving both semantic and latent analysis. Thirteen distinct factors related to adolescent food choices were discussed, forming one main theme and three inter-linking subthemes. The main theme relates to food choice being multi-factorial in nature, needing a balance of priorities through internal negotiations for food choice with the aim of reducing food guilt. This can change depending on the social setting. Social concerns and food guilt appear to play a strong role in adolescent food choice, with adolescents feeling guilty for eating unhealthy food, wasting food, or spending/wasting money on food. A conceptual model for food choice in adolescents was developed, named a “Food Choice Funnel”, incorporating a specific “Food Guilt Matrix”. While we should encourage healthy eating and a healthy lifestyle, it is important to understand the value placed on the social component to eating among adolescents, since they have increasing social interactions and occasions where choosing health-promoting foods may be more challenging. Healthy eating messages should be designed in a balanced manner to support healthy growth and development, while limiting the potential to induce feelings of guilt among adolescents

Rebound activation of 5-HT neurons following SSRI discontinuation

Cessation of therapy with a selective serotonin (5-HT) reuptake inhibitor (SSRI) is often associated with an early onset and disabling discontinuation syndrome, the mechanism of which is surprisingly little investigated. Here we determined the effect on 5-HT neurochemistry of discontinuation from the SSRI paroxetine. Paroxetine was administered repeatedly to mice (once daily, 12 days versus saline controls) and then either continued or discontinued for up to 5 days. Whereas brain tissue levels of 5-HT and/or its metabolite 5-HIAA tended to decrease during continuous paroxetine, levels increased above controls after discontinuation, notably in hippocampus. In microdialysis experiments continuous paroxetine elevated hippocampal extracellular 5-HT and this effect fell to saline control levels on discontinuation. However, depolarisation (high potassium)-evoked 5-HT release was reduced by continuous paroxetine but increased above controls post-discontinuation. Extracellular hippocampal 5-HIAA also decreased during continuous paroxetine and increased above controls post-discontinuation. Next, immunohistochemistry experiments found that paroxetine discontinuation increased c-Fos expression in midbrain 5-HT (TPH2 positive) neurons, adding further evidence for a hyperexcitable 5-HT system. The latter effect was recapitulated by 5-HT1A receptor antagonist administration although gene expression analysis could not confirm altered expression of 5-HT1A autoreceptors following paroxetine discontinuation. Finally, in behavioural experiments paroxetine discontinuation increased anxiety-like behaviour, which partially correlated in time with the measures of increased 5-HT function. In summary, this study reports evidence that, across a range of experiments, SSRI discontinuation triggers a rebound activation of 5-HT neurons. This effect is reminiscent of neural changes associated with various psychotropic drug withdrawal states, suggesting a common unifying mechanism

Phomopsis bougainvilleicola prepatellar bursitis in a renal transplant recipient

Prepatellar bursitis is typically a monomicrobial bacterial infection. A fungal cause is rarely identified. We describe a 61-year-old man who had received a renal transplant 21 months prior to presentation whose synovial fluid and surgical specimens grew Phomopsis bougainvilleicola, a pycnidial coelomycete