Certifying and removing disparate impact

What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, gender, religious practice) and an explicit description of the process. When the process is implemented using computers, determining disparate impact (and hence bias) is harder. It might not be possible to disclose the process. In addition, even if the process is open, it might be hard to elucidate in a legal setting how the algorithm makes its decisions. Instead of requiring access to the algorithm, we propose making inferences based on the data the algorithm uses. We make four contributions to this problem. First, we link the legal notion of disparate impact to a measure of classification accuracy that while known, has received relatively little attention. Second, we propose a test for disparate impact based on analyzing the information leakage of the protected class from the other data attributes. Third, we describe methods by which data might be made unbiased. Finally, we present empirical evidence supporting the effectiveness of our test for disparate impact and our approach for both masking bias and preserving relevant information in the data. Interestingly, our approach resembles some actual selection practices that have recently received legal scrutiny.Comment: Extended version of paper accepted at 2015 ACM SIGKDD Conference on Knowledge Discovery and Data Minin

Experimental and Numerical Performance Survey of a MW-Scale Supercritical CO2 Compressor Operating in Near-Critical Conditions

Closed power cycles based on carbon dioxide in supercritical conditions (sCO2 in the following) are experiencing a growing scientific, technical and industrial interest, due to the high energy conversion efficiency and components compactness. Despite these advantages, the use of a working fluid operating in proximity to the critical point, especially for the compressor, entails multidisciplinary challenges related to the severe non-ideality of the supercritical fluid, which includes the potential onset of phase change at the impeller intake. On the technical and industrial grounds, the phase-transition might dramatically affect the aerodynamics, the performance and the rangeability of the compressor. On the scientific ground, the modelling of two-phase flows in transonic/supersonic conditions still remains an open issue that demands a thorough experimental assessment. This work illustrates the results of a wide experimental campaign focused on the evaluation of the operative map of a MW-scale high-load sCO2 compressor operating in plant-representative conditions, i.e. in proximity to the critical point (P = 79.8 bar, T = 33°C), designed in the frame of the sCO2Flex project, EU Horizon 2020 funded program (grant agreement #764690). In the design process, the machine had been object of a thorough computational investigation, performed by using a homogeneous equilibrium model equipped with a barotropic equation of state, which revealed a significant impact of the phase change on the compressor aerodynamics and on its rangeability for flow rates higher than the design one. Such phenomena are connected to the sudden drop of the speed of sound, originated when the fluid thermodynamic condition crosses the saturation line, and they weaken as the compressor loading reduces. Experiments carried out on a first of a kind 5 MW sCO2 prototype compressor manufactured and tested by Baker Hughes in 2021 remarkably well matched the predicted compressor performance and, especially, the anticipated and sudden choking of the compressor at nominal peripheral Mach number. Results demonstrates experimentally, for the first time ever, the effects of the phase-change on the operation of a realistic sCO2 compressor, also providing significant insights on the predictive capabilities of the physical models employed for the calculation of two-phase flows in this class of machines

Alpha-band rhythms in visual task performance: phase-locking by rhythmic sensory stimulation

Oscillations are an important aspect of neuronal activity. Interestingly, oscillatory patterns are also observed in behaviour, such as in visual performance measures after the presentation of a brief sensory event in the visual or another modality. These oscillations in visual performance cycle at the typical frequencies of brain rhythms, suggesting that perception may be closely linked to brain oscillations. We here investigated this link for a prominent rhythm of the visual system (the alpha-rhythm, 8-12 Hz) by applying rhythmic visual stimulation at alpha-frequency (10.6 Hz), known to lead to a resonance response in visual areas, and testing its effects on subsequent visual target discrimination. Our data show that rhythmic visual stimulation at 10.6 Hz: 1) has specific behavioral consequences, relative to stimulation at control frequencies (3.9 Hz, 7.1 Hz, 14.2 Hz), and 2) leads to alpha-band oscillations in visual performance measures, that 3) correlate in precise frequency across individuals with resting alpha-rhythms recorded over parieto-occipital areas. The most parsimonious explanation for these three findings is entrainment (phase-locking) of ongoing perceptually relevant alpha-band brain oscillations by rhythmic sensory events. These findings are in line with occipital alpha-oscillations underlying periodicity in visual performance, and suggest that rhythmic stimulation at frequencies of intrinsic brain-rhythms can be used to reveal influences of these rhythms on task performance to study their functional roles

The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research

Alpha oscillations (7–13 Hz) are the dominant rhythm in both the resting and active brain. Accordingly, translational research has provided evidence for the involvement of aberrant alpha activ- ity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia, major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes at play, not to mention recent technical and methodological advances in this domain. Herein, we seek to address this issue by reviewing the literature gathered on this topic over the last ten years. For each neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger the associated symptomatology, as well as a summary of the most relevant studies and scientific con- tributions issued throughout the last decade. We conclude with some advice and recommendations that might improve future inquiries within this field

Fairness-enhancing interventions in stream classification

The wide spread usage of automated data-driven decision support systems has raised a lot of concerns regarding accountability and fairness of the employed models in the absence of human supervision. Existing fairness-aware approaches tackle fairness as a batch learning problem and aim at learning a fair model which can then be applied to future instances of the problem. In many applications, however, the data comes sequentially and its characteristics might evolve with time. In such a setting, it is counter-intuitive to "fix" a (fair) model over the data stream as changes in the data might incur changes in the underlying model therefore, affecting its fairness. In this work, we propose fairness-enhancing interventions that modify the input data so that the outcome of any stream classifier applied to that data will be fair. Experiments on real and synthetic data show that our approach achieves good predictive performance and low discrimination scores over the course of the stream.Comment: 15 pages, 7 figures. To appear in the proceedings of 30th International Conference on Database and Expert Systems Applications, Linz, Austria August 26 - 29, 201

The duration of a co-occurring sound modulates visual detection performance in humans.

The duration of sounds can affect the perceived duration of co-occurring visual stimuli. However, it is unclear whether this is limited to amodal processes of duration perception or affects other non-temporal qualities of visual perception

Genetic and Clinical Features of Multiple Endocrine Neoplasia Types 1 and 2

Multiple endocrine neoplasia (MEN) are clinical inherited syndromes affecting different endocrine glands. Three different patterns of MEN syndromes can occur (MEN 1, MEN 2A, and MEN 2B). MEN syndromes are very rare, affect all ages and both sexes are equally affected. MEN 1 is characterized by the neoplastic transformation of the parathyroid glands, pancreatic islets, anterior pituitary, and gastrointestinal tract. Heterozygous MEN 1 germline mutations have been detected in about 70–80% of patients with MEN 1. The mutations are scattered throughout the entire genomic sequence of the gene. MEN 1 patients are characterized by variable clinical features, thus suggesting the lack of a genotype-phenotype correlation. Therapeutical approaches are different according to the different endocrinopathies. The prognosis is generally good if adequate treatment is provided. In MEN 2 syndromes, the medullary thyroid cancer (MTC) is almost invariably present and can be associated with pheochromocytoma (PHEO) and/or multiple adenomatosis of parathyroid glands with hyperparathyroidism (PHPT). The different combination of the endocrine neoplasia gives origin to 3 syndromes: MEN 2A, MEN 2B, and FMTC. The clinical course of MTC varies considerably in the three syndromes. It is very aggressive in MEN 2B, almost indolent in the majority of patients with FMTC and with variable degrees of aggressiveness in patients with MEN 2A. Activating germline point mutations of the RET protooncogene are present in 98% of MEN 2 families. A strong genotype-phenotype correlation has been observed and a specific RET mutation may be responsible for a more or less aggressive clinical course. The treatment of choice for primary MTC is total thyroidectomy with central neck lymph nodes dissection. Nevertheless, 30% of MTC patients, especially in MEN 2B and 2A, are not cured by surgery. Recently, developed molecular therapeutics that target the RET pathway have shown very promising activity in clinical trials of patients with advanced MTC. MEN 2 prognosis is strictly dependent on the MTC aggressiveness and thus on the success of the initial treatment

Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach

Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients' etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC

Polygenic Susceptibility to Papillary Thyroid Cancer in Italian Subjects.

olygenic Susceptibility to Papillary Thyroid Cancer in Italian Subjects INTRODUCTION AND AIM. Thyroid cancer is the most common endocrine neoplasia, with an estimated age- standardized incidence rate of 6.7 per 100000 worldwide in 2018 [1]. This rate is rapidly increasing and papillary thy- roid carcinoma (PTC) is the main histotype. PTC suscepti- bility is the result of genetic predisposition, environmental factors and lifestyle. We studied the genetic combination that characterizes PTC affected subjects, differentiating them from healthy controls. METHODS AND RESULTS. We considered the genetic variants (SNPs) significantly associated with PTC on the PubMed database. 184 informative SNPs were selected, considering linkage disequilibrium. Then, SNPs data were extracted from the online 1000 Genomes database,comprising genome of 2504 unselected individuals col- lected worldwide. The combination of 184 SNPs associ- ated with PTC was used to group individuals in different risk-clusters according to their genetic structure, calcu- lated by Bayesian statistics, as previously performed for polycystic ovary syndrome [2]. Individuals were distrib- uted among 7 groups worldwide, indicating different de- gree of genetic predisposition to PTC. We then considered genetic data from about 1200 individuals (697 PTC versus 497 healthy controls) of Central/South Italian origin reg- istered in a GWAS, specific for PTC [3]. This first analysis was refined using the 33 SNPs reasonably most causa- tive of genetic clustering (26 with p<0.05 at trend test in GWAS and 7 with p<0.05 in the model of recessive inher- itance). At multivariate logistic regression analysis, PTC and healthy controls resulted genetically different (ODDS RATIO 188.6, 95%CI 64.35-552.8), revealing diverse pre- disposition to develop cancer. Afterwards, these results have been confirmed in an independent cohort of Italian subjects (234 PTC and 100 controls). Then, the genetic structure of each subject was indicated as a percentage of affinity to each risk-cluster and re-analyzed together with other risk factors: sex, body-mass index, area of origin and familiarity (quantified in a growing score as the degree of kinship increases). These data were analyzed together by principal component analysis and clustering of the two groups was even more pronounced. The most contributive factors to the diversity between PTC and healthy controls were genetics and familiarity. CONCLUSION. We demonstrated that PTC affected subjects are genetically different from healthy controls, and that the difference is identifiable in a peculiar combi- nation of genetic variants