An official website of the United States government
Mendelian Randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases due to weak instruments as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We demonstrate in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, while standard MR methods yield inflated false positive rates. We then conducted an exploratory analysis of MR-Twin and other MR methods applied to 121 trait pairs in the UK Biobank dataset. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, while MR-Twin is immune to this type of confounding, and that MR-Twin can help assess whether traditional approaches may be inflated due to confounding from population stratification.
more »
« less
Model-Based Fairness Metric for Speaker Verification
Ensuring that technological advancements benefit all groups of people equally is crucial. The first step towards fairness is identifying existing inequalities. The naive comparison of group error rates may lead to wrong conclusions. We introduce a new method to determine whether a speaker verification system is fair toward several population subgroups. We propose to model miss and false alarm probabilities as a function of multiple factors, including the population group effects, e.g., male and female, and a series of confounding variables, e.g., speaker effects, language, nationality, etc. This model can estimate error rates related to a group effect without the influence of confounding effects. We experiment with a synthetic dataset where we control group and confounding effects. Our metric achieves significantly lower false positive and false negative rates w.r.t. baseline. We also experiment with VoxCeleb and NIST SRE21 datasets on different ASV systems and present our conclusions.
more »
« less
- Award ID(s):
- 2147350
- PAR ID:
- 10489540
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
- ISBN:
- 979-8-3503-0689-7
- Page Range / eLocation ID:
- 1 to 7
- Subject(s) / Keyword(s):
- fairness speaker verification
- Format(s):
- Medium: X
- Location:
- Taipei, Taiwan
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Microaggressions are subtle, offensive comments that are directed at minority group members and are characteristically ambiguous in meaning. In two studies, we explored how observers interpreted such ambiguous statements by comparing microaggressions to faux pas, offenses caused by the speaker having an incidental false belief. In Experiment 1, we compared third-party observers’ blame and intentionality judgments of microaggressions with those for social faux pas. Despite judging neither microaggressions nor social faux pas to be definitively intentional, participants judged microaggressions as more blameworthy. In Experiment 2, microaggressions without explicit mental state information elicited a similar profile of judgments to those accompanied by explicit prejudiced or ignorant beliefs. Although they were, like faux pas, judged not to cause harm intentionally, microaggressive comments appeared to be judged more blameworthy on account of enduring prejudice thought to be lurking behind a speaker's false beliefs. Our current research demonstrates a distinctive profile of moral judgment for microaggressions.more » « less
-
Abstract Citizen and community science datasets are typically collected using flexible protocols. These protocols enable large volumes of data to be collected globally every year; however, the consequence is that these protocols typically lack the structure necessary to maintain consistent sampling across years. This can result in complex and pronounced interannual changes in the observation process, which can complicate the estimation of population trends because population changes over time are confounded with changes in the observation process.Here we describe a novel modelling approach designed to estimate spatially explicit species population trends while controlling for the interannual confounding common in citizen science data. The approach is based on Double machine learning, a statistical framework that uses machine learning (ML) methods to estimate population change and the propensity scores used to adjust for confounding discovered in the data. ML makes it possible to use large sets of features to control for confounding and to model spatial heterogeneity in trends. Additionally, we present a simulation method to identify and adjust for residual confounding missed by the propensity scores.To illustrate the approach, we estimated species trends using data from the citizen science project eBird. We used a simulation study to assess the ability of the method to estimate spatially varying trends when faced with realistic confounding and temporal correlation. Results demonstrated the ability to distinguish between spatially constant and spatially varying trends. There were low error rates on the estimated direction of population change (increasing/decreasing) at each location and high correlations on the estimated magnitude of population change.The ability to estimate spatially explicit trends while accounting for confounding inherent in citizen science data has the potential to fill important information gaps, helping to estimate population trends for species and/or regions lacking rigorous monitoring data.more » « less
-
The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear and bilinear vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type in the decompressed data, (1) keeping each critical point in its original cell and (2) retaining the type of each critical point (e.g., saddle and attracting node). The key to our method is to adapt a vertex-wise error bound for each grid point and to compress input data together with the error bound field using a modified lossy compressor. Our compression algorithm can be also embarrassingly parallelized for large data handling and in situ processing. We benchmark our method by comparing it with existing lossy compressors in terms of false positive/negative/type rates, compression ratio, and various vector field visualizations with several scientific applications.more » « less
-
Papin, Jason A. (Ed.)Substantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ASRU57964.2023.10389804
