A New Kind of Data Science: The Need for Ethical Analytics

Ethics can no longer be regarded as an add-on in data science and analytics. This paper argues for the necessity of formalizing a new, practically-oriented sub-discipline of AI ethics by outlining the needs, highlighting shortcomings in current approaches, and providing a framework for ethical analytics, which is concerned with the study of the ethical issues surrounding the development, deployment, and/or dissemination of ML/AI systems and data science research, as well as the development of tools and procedures to mitigate ethical harms. While data science and machine learning are primarily concerned with data from start to finish, ethical analytics is concerned primarily with people – moral agents, the groups and societies they comprise, and the world they inhabit. Ethical analytics should be seen as complementary to the more techno-abstracted analytic disciplines, interfacing with the nuanced, ethical issues that stem from ill-defined or vague, socially-relative normative concepts. It studies the issues that arise in this holistic sociotechnical environment, and it seeks to develop concrete solutions or interventions where possible – from mathematics and algorithms to procedures and protocols

Integrated Gradients is a Nonlinear Generalization of the Industry Standard Approach to Variable Attribution for Credit Risk Models

In modern society, epistemic uncertainty limits trust in financial relationships, necessitating transparency and accountability mechanisms for both consumers and lenders. One upshot is that credit risk assessments must be explainable to the consumer. In the United States regulatory milieu, this entails both the identification of key factors in a decision and the provision of consistent actions that would improve standing. The traditionally accepted approach to explainable credit risk modeling involves generating scores with Generalized Linear Models (GLMs) - usually logistic regression, calculating the contribution of each predictor to the total points lost from the theoretical maximum, and generating reason codes based on the 4 or 5 most impactful predictors. The industry standard approach is not directly applicable to a more expressive and flexible class of nonlinear models known as neural networks. This paper demonstrates that an eXplainable AI (XAI) variable attribution technique known as Integrated Gradients (IG) is a natural generalization of the industry standard to neural networks. We also discuss the unique semantics surrounding implementation details in this nonlinear context. While the primary purpose of this paper is to introduce IG to the credit industry and argue for its establishment as an industry standard, a secondary goal is to familiarize academia with the legislative constraints – including their historical and philosophical roots – and sketch the standard approach in the credit industry since there is a dearth of literature on the topic

Social networks : the future for health care delivery

With the rapid growth of online social networking for health, health care systems are experiencing an inescapable increase in complexity. This is not necessarily a drawback; self-organising, adaptive networks could become central to future health care delivery. This paper considers whether social networks composed of patients and their social circles can compete with, or complement, professional networks in assembling health-related information of value for improving health and health care. Using the framework of analysis of a two-sided network – patients and providers – with multiple platforms for interaction, we argue that the structure and dynamics of such a network has implications for future health care. Patients are using social networking to access and contribute health information. Among those living with chronic illness and disability and engaging with social networks, there is considerable expertise in assessing, combining and exploiting information. Social networking is providing a new landscape for patients to assemble health information, relatively free from the constraints of traditional health care. However, health information from social networks currently complements traditional sources rather than substituting for them. Networking among health care provider organisations is enabling greater exploitation of health information for health care planning. The platforms of interaction are also changing. Patient-doctor encounters are now more permeable to influence from social networks and professional networks. Diffuse and temporary platforms of interaction enable discourse between patients and professionals, and include platforms controlled by patients. We argue that social networking has the potential to change patterns of health inequalities and access to health care, alter the stability of health care provision and lead to a reformulation of the role of health professionals. Further research is needed to understand how network structure combined with its dynamics will affect the flow of information and potentially the allocation of health care resources

Genetic Sensitivity to Peer Behaviors: 5HTTLPR, Smoking, and Alcohol Consumption

We investigate whether the serotonin transporter-linked polymorphic region (5HTTLPR), a gene associated with environmental sensitivity, moderates the association between smoking and drinking patterns at adolescents' schools and their corresponding risk for smoking and drinking themselves. Drawing on the school-based design of the National Longitudinal Study of Adolescent Health in conjunction with molecular genetic data for roughly 15,000 respondents (including over 2,000 sibling pairs), we show that adolescents smoke more cigarettes and consume more alcohol when attending schools with elevated rates of tobacco and alcohol use. More important, an individual's susceptibility to school-level patterns of smoking or drinking is conditional on the number of short alleles he or she has in 5HTTLPR. Overall, the findings demonstrate the utility of the differential susceptibility framework for medical sociology by suggesting that health behaviors reflect interactions between genetic factors and the prevalence of these behaviors in a person's context

Automated electroencephalographic discontinuity in cooled newborns predicts cerebral MRI and neurodevelopmental outcome

BACKGROUND AND HYPOTHESIS: Prolonged electroencephalographic (EEG) discontinuity has been associated with poor neurodevelopmental outcomes after perinatal asphyxia but its predictive value in the era of therapeutic hypothermia (TH) is unknown. In infants undergoing TH for hypoxic-ischaemic encephalopathy (HIE) prolonged EEG discontinuity is associated with cerebral tissue injury on MRI and adverse neurodevelopmental outcome. METHOD: Retrospective study of term neonates from three UK centres who received TH for perinatal asphyxia, had continuous two channel amplitude-integrated EEG with EEG for a minimum of 48 h, brain MRI within 6 weeks of birth and neurodevelopmental outcome data at a median age of 24 months. Mean discontinuity was calculated using a novel automated algorithm designed for analysis of the raw EEG signal. RESULTS: Of 49 eligible infants, 17 (35%) had MR images predictive of death or severe neurodisability (unfavourable outcome) and 29 (59%) infants had electrographic seizures. In multivariable logistic regression, mean discontinuity at 24 h and 48 h (both p=0.01), and high seizure burden (p=0.05) were associated with severe cerebral tissue injury on MRI. A mean discontinuity >30 s/min-long epoch, had a specificity and positive predictive value of 100%, sensitivity of 71% and a negative predictive value of 88% for unfavourable neurodevelopmental outcome at a 10 µV threshold. CONCLUSIONS: In addition to seizure burden, excessive EEG discontinuity is associated with increased cerebral tissue injury on MRI and is predictive of abnormal neurodevelopmental outcome in infants treated with TH. The high positive predictive value of EEG discontinuity at 24 h may be valuable in selecting newborns with HIE for adjunctive treatments

ExplainabilityAudit: An Automated Evaluation of Local Explainability in Rooftop Image Classification

Explainable Artificial Intelligence (XAI) is a key concept in building trustworthy machine learning models. Local explainability methods seek to provide explanations for individual predictions. Usually, humans must check these explanations manually. When large numbers of predictions are being made, this approach does not scale. We address this deficiency for a rooftop classification problem specifically with ExplainabilityAudit, a method that automatically evaluates explanations generated by a local explainability toolkit and identifies rooftop images that require further auditing by a human expert. The proposed method utilizes explanations generated by the Local Interpretable Model-Agnostic Explanations (LIME) framework as the most important superpixels of each validation rooftop image during the prediction. Then a bag of image patches is extracted from the superpixels to determine their texture and evaluate the local explanations. Our results show that 95.7% of the cases to be audited are detected by the proposed system

Delivering energy efficiency and carbon reduction schemes in England: Lessons from Green Deal Pioneer Places

Against a background of growing international and national carbon reduction legislation, the UK government introduced the “Green Deal” to deliver a significant increase in housing energy efficiency and reduction in carbon emissions. This paper reflects on one English local authority's experience delivering a programme intended to foster local interest in the Green Deal. Drawing on social surveys and pre and post Green Deal intervention interviews with five demonstrator homes (households that applied to receive a Green Deal package fully funded by the scheme, providing a test bed for the Green Deal recruitment and installation process), this paper shows that awareness and understanding of the Green Deal scheme is low. There is opposition to the cost of finance offered but a strong interest in improving household warmth and for funding improvements through payments added to the electricity bill. Demonstrator home residents perceived Green Deals had improved the warmth and quality of their home, but saving money was the primary motivator for their involvement, not increasing warmth. Whilst Green Deal has not delivered the level of success that was hoped, much can be learned from the scheme to improve future energy efficiency schemes that will be necessary to deliver emission reduction commitments

Directional Pairwise Class Confusion Bias and Its Mitigation

Recent advances in Natural Language Processing have led to powerful and sophisticated models like BERT (Bidirectional Encoder Representations from Transformers) that have bias. These models are mostly trained on text corpora that deviate in important ways from the text encountered by a chatbot in a problem-specific context. While a lot of research in the past has focused on measuring and mitigating bias with respect to protected attributes (stereotyping like gender, race, ethnicity, etc.), there is lack of research in model bias with respect to classification labels. We investigate whether a classification model hugely favors one class with respect to another. We introduce a bias evaluation method called directional pairwise class confusion bias that highlights the chatbot intent classification model’s bias on pairs of classes. Finally, we also present two strategies to mitigate this bias using example biased pairs