Three Levels of AI Transparency

Transparency is generally cited as a key consideration towards building Trustworthy AI. However, the concept of transparency is fragmented in AI research, often limited to transparency of the algorithm alone. While considerable attempts have been made to expand the scope beyond the algorithm, there has yet to be a holistic approach that includes not only the AI system, but also the user, and society at large. We propose that AI transparency operates on three levels, (1) Algorithmic Transparency, (2) Interaction Transparency, and (3) Social Transparency, all of which need to be considered to build trust in AI. We expand upon these levels using current research directions, and identify research gaps resulting from the conceptual fragmentation of AI transparency highlighted within the context of the three levels

Achieving a Data‐Driven Risk Assessment Methodology for Ethical AI

The AI landscape demands a broad set of legal, ethical, and societal considerations to be accounted for in order to develop ethical AI (eAI) solutions which sustain human values and rights. Currently, a variety of guidelines and a handful of niche tools exist to account for and tackle individual challenges. However, it is also well established that many organizations face practical challenges in navigating these considerations from a risk management perspective within AI governance. Therefore, new methodologies are needed to provide a well-vetted and real-world applicable structure and path through the checks and balances needed for ethically assessing and guiding the development of AI. In this paper, we show that a multidisciplinary research approach, spanning cross-sectional viewpoints, is the foundation of a pragmatic definition of ethical and societal risks faced by organizations using AI. Equally important are the findings of cross-structural governance for implementing eAI successfully. Based on evidence acquired from our multidisciplinary research investigation, we propose a novel data-driven risk assessment methodology, entitled DRESS-eAI. In addition, through the evaluation of our methodological implementation, we demonstrate its state-of-the-art relevance as a tool for sustaining human values in the data-driven AI era

The high A_V Quasar Survey: A z=2.027 metal-rich damped Lyman-alpha absorber towards a red quasar at z=3.21

To fully exploit the potential of quasars as probes of cosmic chemical evolution and the internal gas dynamics of galaxies it is important to understand the selection effects behind the quasar samples and in particular if the selection criteria exclude foreground galaxies with certain properties (most importantly a high dust content). Here we present spectroscopic follow-up from the 10.4-m GTC telescope of a dust-reddened quasar, eHAQ0111+0641, from the extended High A_V Quasar (HAQ) survey. We find that the z=3.21 quasar has a foreground Damped Lyman-alpha Absorber (DLA) at z=2.027 along the line of sight. The DLA has very strong metal lines due to a moderately high metallicity (with an inferred lower limit of 25% of the solar metallicity), but a very large gas column density along the line-of-sight in its host galaxy. This discovery is further evidence that there is a dust bias affecting the census of metals, caused by the combined effect of dust obscuration and reddening, in existing samples of z>2 DLAs. The case of eHAQ0111+0641 illustrates that dust bias is not only caused by dust obscuration, but also dust reddening.Comment: 7 pages, 4 figures. Accepted for publication in A&A. A few typos have been correcte

Sustainable AI : An inventory of the state of knowledge of ethical, social, and legal challenges related to artificial intelligence

This report is an inventory of the state of knowledge of ethical, social, and legal challenges related to artificial intelligence conducted within the Swedish Vinnova-funded project “Hållbar AI – AI Ethics and Sustainability”, led by Anna Felländer. Based on a review and mapping of reports and studies, a quantitative and bibliometric analysis, and in-depth analyses of the healt- care sector, the telecom sector, and digital platforms, the report proposes three recommendations. Sustainable AI requires: 1. a broad focus on AI governance and regulation issues, 2. promoting multi-disciplinary collaboration, and 3. building trust in AI applications and applied machine-learning, which is a matter of key importance and requires further study of the relationship between transparency and accountability

On the Complexity of Lie Algebras

Let U, V, W be finite dimensional vector spaces over a field k and let : U x V → W be a bilinear mapping. The (multiplieative) complexity L(~) of ~ is defined as the least r ∈~ such that there are linear forms Ul,...,u r , Vl,...,v r ∈ (U xV) and elements Wl,...,w r ∈ W satisfying r ~(x,y) = ~ Up(X,y) Vp(X,y) Wp for all (x~y) ∈ U× V .Departamento de Matemátic

Exploring real-time functional magnetic resonance imaging neurofeedback in adolescents with disruptive behavior disorder and callous unemotional traits

Introduction: Adolescents with increased callous unemotional traits (CU traits) in the context of disruptive behavior disorder (DBD) show a persistent pattern of antisocial behavior with shallow affect and a lack of empathy or remorse. The amygdala and insula as regions commonly associated with emotion processing, empathy and arousal are implicated in DBD with high CU traits. While behavioral therapies for DBD provide significant but small effects, individualized treatments targeting the implicated brain regions are missing. Methods: In this explorative randomized controlled trial we randomly assigned twenty-seven adolescents with DBD to individualized real-time functional magnetic resonance neurofeedback (rtfMRI-NF) or behavioral treatment as usual (TAU). Visual feedback of either amygdala or insula activity was provided during rtfMRI-NF by gauges and included a simple and concurrent video run plus a transfer run. A linear mixed model (LMM) was applied to determine improvement of self-regulation. Specificity was assessed by correlating individual self-regulation improvement with clinical outcomes. Results: The rtfMRI-NF (n = 11) and TAU (n = 10) completers showed comparable and significant clinical improvement indicating neither superiority nor inferiority of rtfMRI-NF. The exploratory LMM revealed successful learning of self-regulation along the course of training for participants who received feedback from the amygdala. A significant exploratory correlation between individual target region activity in the simple run and clinical improvement was found for one dimension of DBD. Conclusions: This exploratory study demonstrated feasibility and suggests clinical efficacy of individualized rtfMRI-NF comparable to active TAU for adolescents with DBD and increased CU traits. Further studies are needed to confirm efficacy, specificity and to clarify underlying learning mechanisms

Randomized Controlled Trial of Individualized Arousal-Biofeedback for children and adolescents with Disruptive Behavior Disorders (DBD)

Background: Disruptive behavior disorders (including conduct disorder (CD) and oppositional defiant disorder (ODD)) are common childhood and adolescent psychiatric conditions often linked to altered arousal. The recommended first-line treatment is multi-modal therapy and includes psychosocial and behavioral interventions. Their modest effect sizes along with clinically and biologically heterogeneous phenotypes, emphasize the need for innovative personalized treatment targeting impaired functions such as arousal dysregulation. Methods: A total of 37 children aged 8-14 years diagnosed with ODD/CD were randomized to 20 sessions of individualized arousal biofeedback using skin conductance levels (SCL-BF) or active treatment as usual (TAU) including psychoeducation and cognitive-behavioral elements. The primary outcome was the change in parents´ ratings of aggressive behavior measured by the Modified Overt Aggression Scale. Secondary outcome measures were subscales from the Child Behavior Checklist, the Inventory of Callous-Unemotional traits and the Reactive-Proactive Aggression Questionnaire. Results: The SCL-BF treatment was neither superior nor inferior to the active TAU. Both groups showed reduced aggression after treatment with small effects for the primary outcome and large effects for some secondary outcomes. Importantly, successful learning of SCL self-regulation was related to reduced aggression at post-assessment. Conclusions: Individualized SCL-BF was not inferior to active TAU for any treatment outcome with improvements in aggression. Further, participants were on average able to self-regulate their SCL, and those who best learned self-regulation showed the highest clinical improvement, pointing to specificity of SCL-BF regulation for improving aggression. Further studies with larger samples and improved methods, for example by developing BF for mobile use in ecologically more valid settings are warranted

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio