GLocalX - From Local to Global Explanations of Black Box AI Models

Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLOCALX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLOCALX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLOCALX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLOCALX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications.Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLOCALX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLOCALX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLOCALX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLOCALX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications

Impaired sense of smell in a Drosophila Parkinson's model.

Parkinson’s disease (PD) is one of the most common neurodegenerative disease characterized by the clinical triad: tremor, akinesia and rigidity. Several studies have suggested that PD patients show disturbances in olfaction at the earliest onset of the disease. The fruit fly Drosophila melanogaster 32 is becoming a powerful model organism to study neurodegenerative diseases. We sought to use this system to explore olfactory dysfunction, if any, in PINK1 mutants, which is a model for PD. PINK1 mutants display many important diagnostic symptoms of the disease such as akinetic motor behavior. In the present study, we describe for the first time, to the best of our knowledge, neurophysiological and neuroanatomical results concerning the olfactory function in PINK1 mutant flies. Electroantennograms were recorded in response to synthetic and natural volatiles (essential oils) from groups of PINK1 mutant adults at three different time points in their life cycle: one from 3-5 day-old flies, from 15-20 and from 27-30 days. The results obtained were compared with the same age-groups of wild type flies. We found that mutant adults showed a decrease in the olfactory response to 1-hexanol, α-pinene and essential oil volatiles. This olfactory response in mutant adults decreased even more as the flies aged. Immunohistological analysis of the antennal lobes in these mutants revealed structural abnormalities, especially in the expression of Bruchpilot protein, a marker for synaptic active zones. The combination of electrophysiological and morphological results suggests that the altered synaptic organization may be due to a neurodegenerative process. Our results indicate that this model can be used as a tool for understanding PD pathogensis and pathophysiology. These results help to explore the potential of using olfaction as a means of monitoring PD progression and developing new treatments

Multidisciplinary Study of Biological Parameters and Fatigue Evolution in Quay Crane Operators

AbstractIn intermodal terminals the handling of containers and the number of accidents still depends on a wide range of human errors due to fatigue despite the automation level reached nowadays. For this reason it is very important to increase knowledge about the factors affecting the propensity of operators to make errors, increasing the chance of accidents happening. The aim of this work is to propose a novel approach to assess fatigue and performance levels in quay crane operators as a function of physiological parameters and of the many varying boundary conditions encountered in daily work. During their work, quay crane operators have to deal with variable environmental conditions, such as task type, wind speed and direction, lighting conditions that reduce visibility that can require an exacting level of attention. In the trial eight operators have been examined in a session lasting four hours. All actual conditions are reproduced through a fully immersive quay crane simulator. The operator completes the assigned task (the same for each one) and can see through four wide monitors a high quality virtual reality view of the simulation. Most biological parameters are acquired using different devices including a Holter ECG monitor, electromyographic monitoring the four trunk muscles most involved in the test, eye tracker and seat-body pressure interface for both seat pan and backrest. Changes in physiological parameters have been monitored during the trial and interesting correlations with performance levels and boundary conditions have been found for each operator, in accordance with their age and skills. The present study can form the basis for further investigations aimed at developing a cost effective, reliable and robust system for monitoring increasing fatigue and for predicting the critical conditions that may result in an accident

Multidisciplinary study of biological parameters and fatigue evolution in quay crane operators

In intermodal terminals the handling of containers and the number of accidents still depends on a wide range of human errors due to fatigue despite the automation level reached nowadays. For this reason it is very important to increase knowledge about the factors affecting the propensity of operators to make errors, increasing the chance of accidents happening. The aim of this work is to propose a novel approach to assess fatigue and performance lev els in quay crane operators as a function of physiological parameters and of the many varying boundary conditions encountered in daily work. During their work, quay crane operators have to deal with variable environmental conditions, such as task type, wind speed and direction, lighting conditions that redu ce visibility that can require an exacting level of attention. In the trial eight operators have been examined in a session lastin g four hours. All actual conditions are reproduced through a fully imme rsive quay crane simulator. The operator completes the assigned task (the same for each one) and can see through four wide monito rs a high quality virtual reality view of the simulation. Most biological parameters are acquired using different devices including a Holter ECG monitor, electromyographic monitoring the four trunk muscles most involved in the test, eye tracker and seat - body pressure interface for both seat pan and backrest. Changes in physiological parameters have been monitored during the trial and interesting correlations with performance levels and boundary conditions ha ve been f ound for each operator, in accord ance with their age and skills. The present study can form the basis for further investigations aimed at developing a cost effective, reliable and robust system for monitoring increasing fat igue and for predicting the critical conditions that may result in an acciden

Mucuna pruriens (Velvet bean) Rescues Motor, Olfactory, Mitochondrial and Synaptic Impairment in PINK1(B9) Drosophila melanogaster Genetic Model of Parkinson's Disease

The fruit fly Drosophila melanogaster (Dm) mutant for PTEN-induced putative kinase 1 (PINK1B9) gene is a powerful tool to investigate physiopathology of Parkinson's disease (PD). Using PINK1B9 mutant Dm we sought to explore the effects of Mucuna pruriens methanolic extract (Mpe), a L-Dopa-containing herbal remedy of PD. The effects of Mpe on PINK1B9 mutants, supplied with standard diet to larvae and adults, were assayed on 3–6 (I), 10–15 (II) and 20–25 (III) days old flies. Mpe 0.1% significantly extended lifespan of PINK1B9 and fully rescued olfactory response to 1-hexanol and improved climbing behavior of PINK1B9 of all ages; in contrast, L-Dopa (0.01%, percentage at which it is present in Mpe 0.1%) ameliorated climbing of only PINK1B9 flies of age step II. Transmission electron microscopy analysis of antennal lobes and thoracic ganglia of PINK1B9 revealed that Mpe restored to wild type (WT) levels both T-bars and damaged mitochondria. Western blot analysis of whole brain showed that Mpe, but not L-Dopa on its own, restored bruchpilot (BRP) and tyrosine hydroxylase (TH) expression to age-matched WT control levels. These results highlight multiple sites of action of Mpe, suggesting that its effects cannot only depend upon its L-Dopa content and support the clinical observation of Mpe as an effective medication with intrinsic ability of delaying the onset of chronic L-Dopa-induced long-term motor complications. Overall, this study strengthens the relevance of using PINK1B9 Dm as a translational model to study the properties of Mucuna pruriens for PD treatment

A Measurement of the Branching Ratio of KL→e+e−γγK_L \to e^+e^-\gamma\gamma

We report on a study of the decay $K_L \to e^+e^-\gamma\gamma$ carried out as a part of the KTeV/E799 experiment at Fermilab. The 1997 data yielded a sample of 1543 events, including an expected background of $56 \pm 8$ events. An effective form factor was determined from the observed distribution of the $e^+e^-$ invariant mass. Using this form factor in the calculation of the detector acceptance, the branching ratio was measured to be ${\mathcal B}(K_L \to e^+ e^- \gamma \gamma, E^*_\gamma > 5 {MeV}) = (5.84 \pm 0.15 {\rm ~(stat)} \pm 0.32 {\rm ~(sys)})\times 10^{-7}$.Comment: 5 pages, 4 figure

Neurodegeneration progresses despite complete elimination of clinical relapses in a mouse model of multiple sclerosis.

BACKGROUND: [corrected] Multiple Sclerosis has two clinical phases reflecting distinct but inter-related pathological processes: focal inflammation drives the relapse-remitting stage and neurodegeneration represents the principal substrate of secondary progression. In contrast to the increasing number of effective anti-inflammatory disease modifying treatments for relapse-remitting disease, the absence of therapies for progressive disease represents a major unmet clinical need. This raises the unanswered question of whether elimination of clinical relapses will prevent subsequent progression and if so how early in the disease course should treatment be initiated. Experimental autoimmune encephalomyelitis in the Biozzi ABH mouse recapitulates the clinical and pathological features of multiple sclerosis including relapse-remitting episodes with inflammatory mediated demyelination and progressive disability with neurodegeneration. To address the relationship between inflammation and neurodegeneration we used an auto-immune tolerance strategy to eliminate clinical relapses in EAE in a manner analogous to the clinical effect of disease modifying treatments. RESULTS: By arresting clinical relapses in EAE at two distinct stages, early and late disease, we demonstrate that halting immune driven demyelination even after the first major clinical event is insufficient to prevent long-term neurodegeneration and associated gliosis. Nonetheless, early intervention is partially neuroprotective, whereas later interventions are not. Furthermore early tolerisation is also associated with increased remyelination. CONCLUSIONS: These findings are consistent with both a partial uncoupling of inflammation and neurodegeneration and that the regenerative response of remyelination is negatively correlated with inflammation. These findings strongly support the need for early combinatorial treatment of immunomodulatory therapies and neuroprotective treatments to prevent long-term neurodegeneration in multiple sclerosis