The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Background: Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke. Methods: We did a double-blind, randomised, placebo-controlled trial in 610 sites across 28 countries. We randomly assigned patients aged 40 years and older with type 2 diabetes and cardiovascular disease (at a 1:1 ratio) to groups that either received a subcutaneous injection of albiglutide (30–50 mg, based on glycaemic response and tolerability) or of a matched volume of placebo once a week, in addition to their standard care. Investigators used an interactive voice or web response system to obtain treatment assignment, and patients and all study investigators were masked to their treatment allocation. We hypothesised that albiglutide would be non-inferior to placebo for the primary outcome of the first occurrence of cardiovascular death, myocardial infarction, or stroke, which was assessed in the intention-to-treat population. If non-inferiority was confirmed by an upper limit of the 95% CI for a hazard ratio of less than 1·30, closed testing for superiority was prespecified. This study is registered with ClinicalTrials.gov, number NCT02465515. Findings: Patients were screened between July 1, 2015, and Nov 24, 2016. 10 793 patients were screened and 9463 participants were enrolled and randomly assigned to groups: 4731 patients were assigned to receive albiglutide and 4732 patients to receive placebo. On Nov 8, 2017, it was determined that 611 primary endpoints and a median follow-up of at least 1·5 years had accrued, and participants returned for a final visit and discontinuation from study treatment; the last patient visit was on March 12, 2018. These 9463 patients, the intention-to-treat population, were evaluated for a median duration of 1·6 years and were assessed for the primary outcome. The primary composite outcome occurred in 338 (7%) of 4731 patients at an incidence rate of 4·6 events per 100 person-years in the albiglutide group and in 428 (9%) of 4732 patients at an incidence rate of 5·9 events per 100 person-years in the placebo group (hazard ratio 0·78, 95% CI 0·68–0·90), which indicated that albiglutide was superior to placebo (p<0·0001 for non-inferiority; p=0·0006 for superiority). The incidence of acute pancreatitis (ten patients in the albiglutide group and seven patients in the placebo group), pancreatic cancer (six patients in the albiglutide group and five patients in the placebo group), medullary thyroid carcinoma (zero patients in both groups), and other serious adverse events did not differ between the two groups. There were three (<1%) deaths in the placebo group that were assessed by investigators, who were masked to study drug assignment, to be treatment-related and two (<1%) deaths in the albiglutide group. Interpretation: In patients with type 2 diabetes and cardiovascular disease, albiglutide was superior to placebo with respect to major adverse cardiovascular events. Evidence-based glucagon-like peptide 1 receptor agonists should therefore be considered as part of a comprehensive strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. Funding: GlaxoSmithKline

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Critical empirical study on black-box explanations in AI

International audienceThis paper provides empirical concerns about post-hoc explanations of black-box ML models, one of the major trends in AI explainability (XAI), by showing its lack of interpretability and societal consequences. Using a representative consumer panel to test our assumptions, we report three main findings. First, we show that post-hoc explanations of black-box model tend to give partial and biased information on the underlying mechanism of the algorithm and can be subject to manipulation or information withholding by diverting users' attention. Secondly, we show the importance of tested behavioral indicators, in addition to self-reported perceived indicators, to provide a more comprehensive view of the dimensions of interpretability. This paper contributes to shedding new light on the actual theoretical debate between intrinsically transparent AI models and post-hoc explanations of black-box complex models-a debate which is likely to play a highly influential role in the future development and operationalization of AI systems

Formaliser l'équité en Machine Learning. Revue des méthodes de "fairness" en apprentissage supervisé

International audienceThe decisions resulting from supervised learning algorithms are coming from historical databases. One of the major ethical problems posed by Machine Learning algorithms is fairness of the decision with respect to several groups of the population. In this poster, we present the sources of bias, the metrics of fairness and the methods of bias mitagation in Machine Learning.Les décisions issues des algorithmes d’apprentissage supervisé s’adaptent à partir d’un historique d’exemples. Un des problèmes éthiques majeurs posés par les algorithmes du Machine Learning est celui de l’équité de la décision vis-à-vis de certains groupes de la population

Some critical and ethical perspectives on the empirical turn of AI interpretability

We consider two fundamental and related issues currently faced by Artificial Intelligence (AI) development: the lack of ethics and interpretability of AI decisions. Can interpretable AI decisions help to address ethics in AI? Using a randomized study, we experimentally show that the empirical and liberal turn of the production of explanations tends to select AI explanations with a low denunciatory power. Under certain conditions, interpretability tools are therefore not means but, paradoxically, obstacles to the production of ethical AI since they can give the illusion of being sensitive to ethical incidents. We also show that the denunciatory power of AI explanations is highly dependent on the context in which the explanation takes place, such as the gender or education level of the person to whom the explication is intended for. AI ethics tools are therefore sometimes too flexible and self-regulation through the liberal production of explanations do not seem to be enough to address ethical issues. We then propose two scenarios for the future development of ethical AI: more external regulation or more liberalization of AI explanations. These two opposite paths will play a major role on the future development of ethical AI

A Critical Empirical Study of Black-box Explanations in AI

This paper provides empirical concerns about post-hoc explanations of black-box ML models, one of the major trends in AI explainability (XAI), by showing its lack of interpretability and societal consequences. Using a representative consumer panel to test our assumptions, we report three main findings. First, we show that post-hoc explanations of black-box model tend to give partial and biased information on the underlying mechanism of the algorithm and can be subject to manipulation or information withholding by diverting users’ attention. Secondly, we show the importance of tested behavioral indicators, in addition to self-reported perceived indicators, to provide a more comprehensive view of the dimensions of interpretability. This paper contributes to shedding new light on the actual theoretical debate between intrinsically transparent AI models and post-hoc explanations of black-box complex models – a debate which is likely to play a highly influential role in the future development and operationalization of AI systems

Un cadre d’autorégulation pour l’éthique de L’IA : opportunités et défis

FNEGE 4International audienceWe propose a self-regulatory tool for AI design that integrates societal metrics such as fairness, interpretability, and privacy. To do so, we create an interface that allows data scientists to visually choose the Machine Learning (ML) algorithm that best fits the AI designers’ ethical preferences. Using a Design Science methodology, we test the artifact on data scientist users and show that the interface is easy to use, gives a better understanding of the ethical issues of AI, generates debate, makes the algorithms more ethical, and is operational for decision-making. Our first contribution is to build a bottom-up AI regulation tool that integrates not only users’ ethical preferences, but also the singularities of the practical case learned by the algorithm. The method is independent of ML use cases and ML learning procedures. Our second contribution is to show that data scientists can select freely to sacrifice some performance to reach more ethical algorithms if they use appropriate regulatory tools. We then provide the conditions under which this technical and self-regulatory approach can fail. This paper shows how it is possible to bridge the gap between theories and practices in AI Ethics using flexible and bottom-up tools.Proponemos una herramienta de autorregulación para el diseño de IA que integra métricas sociales como la equidad, la interpretabilidad y la privacidad. Para ello, creamos una interfaz que permite a los científicos de datos elegir visualmente el algoritmo de aprendizaje automático (ML) que mejor se adapte a las preferencias éticas de los diseñadores de IA. Utilizando una metodología de Design Science, probamos el artefacto con usuarios científicos de datos y demostramos que la interfaz es fácil de usar, permite comprender mejor las cuestiones éticas de la IA, genera debate, hace que los algoritmos sean más éticos y es operativa para la toma de decisiones. Nuestra primera contribución es construir una herramienta de regulación ascendente de la IA que integre no sólo las preferencias éticas de los usuarios, sino también las singularidades del caso práctico aprendido por el algoritmo. El método es independiente de los casos de uso de ML y de los procedimientos de aprendizaje de ML. Nuestra segunda contribución es demostrar que los científicos de datos pueden elegir libremente sacrificar parte del rendimiento para conseguir algoritmos más éticos si utilizan herramientas de regulación adecuadas. A continuación, proporcionamos las condiciones en las que este enfoque técnico y autorregulador puede fallar. Este documento muestra cómo es posible salvar la brecha entre las teorías y las prácticas en la ética de la IA utilizando herramientas flexibles y ascendentes.Nous proposons un outil d’autorégulation pour la conception d’IA qui intègre des mesures sociétales telles que l’équité, l’interprétabilité et la confidentialité. Pour ce faire, nous créons une interface qui permet aux praticiens de l’IA (experts en mégadonnées/data scientists) de choisir visuellement l’algorithme d’apprentissage (Machine Learning/ML) qui correspond le mieux aux préférences éthiques des concepteurs d’IA. En utilisant une méthodologie de conception en design science (science du design), nous testons l’artefact sur des data scientists et montrons que l’interface est facile à utiliser, permet de mieux comprendre les enjeux éthiques de l’IA, génère des débats, rend les algorithmes plus éthiques et est opérationnelle pour la prise de décision. Notre première contribution est de construire un outil de régulation de l’IA qui intègre non seulement les préférences éthiques des utilisateurs, mais aussi les singularités du cas pratique appris par l’algorithme. La méthode est indépendante des cas d’utilisation et des procédures d’apprentissage ML. Notre deuxième contribution est de montrer que les data scientists peuvent choisir librement de sacrifier certaines performances pour atteindre des algorithmes plus éthiques, à condition d’utiliser des outils réglementaires appropriés. Nous fournissons ensuite les conditions dans lesquelles cette approche technique et autorégulatrice peut échouer. Cet article montre comment il est possible de combler le fossé entre les théories et les pratiques en matière d’éthique de l’IA à l’aide d’outils flexibles qui prennent en compte les singularités des cas pratiques

Interprétabilité en Machine Learning, revue de littérature et perspectives

International audienceMachine Learning algorithms, and particularly deep neural networks, have strong predictive performance in recent years in many areas such as image recognition, text and speech analysis. Nevertheless, these good predictive results come genrally with the difficulty in interpreting the model generation process on the one hand and the learned décision on the other hand. There are many tools for interpreting Machine Learning ranging from local, global, intrinsic, post-hoc, agnostic, specific methods or methods for visualizing parts of the input to parts of the algorithm. The current literature mixes the different methods, the interactivity between these methods becoming the key to interpretation. In the same way that there is a multiplicity of definitions of interpretability depending on the context of use (Doshi-Velez and Kim 2017), there is also a multiplicity of methods and tools for interpreting so-called "black box" algorithms.Les algorithmes d’apprentissage automatique, et particulièrement les réseaux de neurones profonds, connaissent ces dernières années de fortes performances prédictives dans de nombreux domaines tels que la reconnaissance d’images, l’analyse textuelle ou vocale. Néanmoins, ces bons résultats prédictifs s’accompagnent généralement d’une difficulté à interpréter d’une part le processus de génération du modèle et d’autre part le résultat appris. Il existe de nombreux outils pour interpréter le Machine Learning allant de méthodes locales, globales, intrinsèques, post-hoc, agnostiques, spécifiques ou des méthodes de visualisation d’une partie de l’input à des parties de l’algorithme. La littérature actuelle semble manifester une volonté de mixer les différente méthodes, l’interactivité entre ces méthodes devenant la clé de l’interprétation. De la même manière où il existe une multiplicité de définitions de l’interprétabilité selon le contexte d’utilisation (Doshi-Velez et Kim 2017), il existe également une multiplicité de méthodes et outils pour interpréter les algorithmes dits « boîte noire »

Some critical and ethical perspectives on the empirical turn of AI interpretability

CNRS 2, FNEGE 2, HCERES A, ABS 3International audienceWe consider two fundamental and related issues currently facing the development of Artificial Intelligence (AI): the lack of ethics, and the interpretability of AI decisions. Can interpretable AI decisions help to address the issue of ethics in AI? Using a randomized study, we experimentally show that the empirical and liberal turn of the production of explanations tends to select AI explanations with a low denunciatory power. Under certain conditions, interpretability tools are therefore not means but, paradoxically, obstacles to the production of ethical AI since they can give the illusion of being sensitive to ethical incidents. We also show that the denunciatory power of AI explanations is highly dependent on the context in which the explanation takes place, such as the gender or education of the person for whom the explication is intended. AI ethics tools are therefore sometimes too flexible and self-regulation through the liberal production of explanations does not seem to be enough to resolve ethical issues. By following an STS pragmatist program, we highlight the role of non-human actors (such as computational paradigms, testing environments, etc.) in the formation of structural power relations, such as sexism. We then propose two scenarios for the future development of ethical AI: more external regulation, or more liberalization of AI explanations. These two opposite paths will play a major role in the future development of ethical AI