Fair Inference On Outcomes

In this paper, we consider the problem of fair statistical inference involving outcome variables. Examples include classification and regression problems, and estimating treatment effects in randomized trials or observational data. The issue of fairness arises in such problems where some covariates or treatments are "sensitive," in the sense of having potential of creating discrimination. In this paper, we argue that the presence of discrimination can be formalized in a sensible way as the presence of an effect of a sensitive covariate on the outcome along certain causal pathways, a view which generalizes (Pearl, 2009). A fair outcome model can then be learned by solving a constrained optimization problem. We discuss a number of complications that arise in classical statistical inference due to this view and provide workarounds based on recent work in causal and semi-parametric inference

Versatile analog pulse height computer performs real-time arithmetic operations

Multipurpose analog pulse height computer performs real-time arithmetic operations on relatively fast pulses. This computer can be used for identification of charged particles, pulse shape discrimination, division of signals from position sensitive detectors, and other on-line data reduction techniques

High-light-yield calcium iodide (CaI2) scintillator for astroparticle physics

A high light yield calcium iodide (CaI2) scintillator is being developed for an astroparticle physics experiments. This paper reports scintillation performance of calcium iodide (CaI2) crystal. Large light emission of 2.7 times that of NaI(Tl) and an emission wavelength in good agreement with the sensitive wavelength of the photomultiplier were obtained. A study of pulse shape discrimination using alpha and gamma sources was also performed. We confirmed that CaI2 has excellent pulse shape discrimination potential with a quick analysis.Comment: 5 pages, 5 figures, Proceeding of the 15th Vienna Conference on Instrumentation (VCI2019

Charge and current-sensitive preamplifiers for pulse shape discrimination techniques with silicon detectors

New charge and current-sensitive preamplifiers coupled to silicon detectors and devoted to studies in nuclear structure and dynamics have been developed and tested. For the first time shapes of current pulses from light charged particles and carbon ions are presented. Capabilities for pulse shape discrimination techniques are demonstrated.Comment: 14 pages, 12 figures, to be published in Nucl. Inst. Meth.

Reconsidering Gender Bias in Intra-Household Allocation in India

Detecting gender discrimination among children in the intra-household allocation of goods from household surveys has often proven to be difficult. This paper uses some of the commonly used techniques in this field to analyze education expenditures in India. Contrary to most previous research, I find evidence of discrimination against girls. Results at the all-India level are robust to the statistical method and the education expenditure measure, while they are more sensitive to changes in the analysis at the state level. In general, girls experience gender discrimination especially from age 10 onwards, with almost universal disadvantage in the amount of education expenditures in the group of 15-19 year olds.gender discrimination, India, intra-household allocation, education expenditures

Development of visible/infrared/microwave agriculture classification and biomass estimation algorithms

Agricultural crop classification models using two or more spectral regions (visible through microwave) are considered in an effort to estimate biomass at Guymon, Oklahoma Dalhart, Texas. Both grounds truth and aerial data were used. Results indicate that inclusion of C, L, and P band active microwave data, from look angles greater than 35 deg from nadir, with visible and infrared data improve crop discrimination and biomass estimates compared to results using only visible and infrared data. The microwave frequencies were sensitive to different biomass levels. The K and C band were sensitive to differences at low biomass levels, while P band was sensitive to differences at high biomass levels. Two indices, one using only active microwave data and the other using data from the middle and near infrared bands, were well correlated to total biomass. It is implied that inclusion of active microwave sensors with visible and infrared sensors on future satellites could aid in crop discrimination and biomass estimation

Fair Kernel Learning

New social and economic activities massively exploit big data and machine learning algorithms to do inference on people's lives. Applications include automatic curricula evaluation, wage determination, and risk assessment for credits and loans. Recently, many governments and institutions have raised concerns about the lack of fairness, equity and ethics in machine learning to treat these problems. It has been shown that not including sensitive features that bias fairness, such as gender or race, is not enough to mitigate the discrimination when other related features are included. Instead, including fairness in the objective function has been shown to be more efficient. We present novel fair regression and dimensionality reduction methods built on a previously proposed fair classification framework. Both methods rely on using the Hilbert Schmidt independence criterion as the fairness term. Unlike previous approaches, this allows us to simplify the problem and to use multiple sensitive variables simultaneously. Replacing the linear formulation by kernel functions allows the methods to deal with nonlinear problems. For both linear and nonlinear formulations the solution reduces to solving simple matrix inversions or generalized eigenvalue problems. This simplifies the evaluation of the solutions for different trade-off values between the predictive error and fairness terms. We illustrate the usefulness of the proposed methods in toy examples, and evaluate their performance on real world datasets to predict income using gender and/or race discrimination as sensitive variables, and contraceptive method prediction under demographic and socio-economic sensitive descriptors.Comment: Work published on ECML'17, http://ecmlpkdd2017.ijs.si/papers/paperID275.pd

Improved discrimination in photographic density contouring

Density discrimination can be accomplished through use of special photographic contouring material which has two sensitive layers (one negative, one positive) on single support. Process will be of interest to investigators who require finer discrimination of densities of original photograph for purposes such as identification of crops and analysis of energy levels of radiating objects