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Automated decision-making systems are increasingly deployed in domains such as hiring and credit approval where negative outcomes can have substantial ramifications for decision subjects. Thus, recent research has focused on providing explanations that help decision subjects understand the decision system and enable them to take actionable recourse to change their outcome. Popular counterfactual explanation techniques aim to achieve this by describing alterations to an instance that would transform a negative outcome to a positive one. Unfortunately, little user evaluation has been performed to assess which of the many counterfactual approaches best achieve this goal. In this work, we conduct a crowd-sourced between-subjects user study (N = 252) to examine the effects of counterfactual explanation type and presentation on lay decision subjects’ understandings of automated decision systems. We find that the region-based counterfactual type significantly increases objective understanding, subjective understanding, and response confidence as compared to the point-based type. We also find that counterfactual presentation significantly effects response time and moderates the effect of counterfactual type for response confidence, but not understanding. A qualitative analysis reveals how decision subjects interact with different explanation configurations and highlights unmet needs for explanation justification. Our results provide valuable insights and recommendations for the development of counterfactual explanation techniques towards achieving practical actionable recourse and empowering lay users to seek justice and opportunity in automated decision workflows. 

Summary Identifying dependency in multivariate data is a common inference task that arises in numerous applications. However, existing nonparametric independence tests typically require computation that scales at least quadratically with the sample size, making it difficult to apply them in the presence of massive sample sizes. Moreover, resampling is usually necessary to evaluate the statistical significance of the resulting test statistics at finite sample sizes, further worsening the computational burden. We introduce a scalable, resampling-free approach to testing the independence between two random vectors by breaking down the task into simple univariate tests of independence on a collection of $2\times 2$ contingency tables constructed through sequential coarse-to-fine discretization of the sample , transforming the inference task into a multiple testing problem that can be completed with almost linear complexity with respect to the sample size. To address increasing dimensionality, we introduce a coarse-to-fine sequential adaptive procedure that exploits the spatial features of dependency structures. We derive a finite-sample theory that guarantees the inferential validity of our adaptive procedure at any given sample size. We show that our approach can achieve strong control of the level of the testing procedure at any sample size without resampling or asymptotic approximation and establish its large-sample consistency. We demonstrate through an extensive simulation study its substantial computational advantage in comparison to existing approaches while achieving robust statistical power under various dependency scenarios, and illustrate how its divide-and-conquer nature can be exploited to not just test independence, but to learn the nature of the underlying dependency. Finally, we demonstrate the use of our method through analysing a dataset from a flow cytometry experiment. 

Seawater microorganisms play an important role in coral reef ecosystem functioning and can be influenced by biological, chemical, and physical features of reefs. As coral reefs continue to respond to environmental changes, the reef seawater microbiome has been proposed as a conservation tool for monitoring perturbations. However, the spatial variability of reef seawater microbial communities is not well studied, limiting our ability to make generalizable inferences across reefs. In order to better understand how microorganisms are distributed at multiple spatial scales, we examined seawater microbial communities in Florida Reef Tract and US Virgin Islands reef systems using a nested sampling design. On 3 reefs per reef system, we sampled seawater at regular spatial intervals close to the benthos. We assessed the microbial community composition of these waters using ribosomal RNA gene amplicon sequencing. Our analysis revealed that reef water microbial communities varied as a function of reef system and individual reefs, but communities did not differ within reefs and were not significantly influenced by benthic composition. For the reef system and inter-reef differences, abundant microbial taxa were found to be potentially useful indicators of environmental difference due to their high prevalence and variance. We further examined reef water microbial biogeography on a global scale using a secondary analysis of 5 studies, which revealed that microbial communities were more distinct with increasing geographic distance. These results suggest that biogeography is a distinguishing feature for reef water microbiomes, and that development of monitoring criteria may necessitate regionally specific sampling and analyses. 

Abstract We study Λ-type Electromagnetically Induced Transparency (EIT) on the Rb D2 transition in a buffer-gas-free thermal vapor cell without anti-relaxation coating. Experimental data show well-resolved features due to velocity-selective optical pumping and one EIT resonance. The Zeeman splitting of the EIT line in magnetic fields up to 12 Gauss is investigated. One Zeeman component is free of the first-order shift and its second-order shift agrees well with theory. The full width at half maximum (FWHM) of this magnetic-field-insensitive EIT resonance is reduced due to Doppler narrowing, scales linearly in Rabi frequency over the range studied, and reaches about 100 kHz at the lowest powers. These observations agree with an analytic model for a Doppler-broadened medium developed in (Javan et al 2002 Phys. Rev. A 66 013805; Lee et al 2003 Appl. Phys. B, Lasers Opt. (Germany) B 76 , 33–9; Taichenachev et al 2000 JETP Lett. 72 , 119). Numerical simulation using the Lindblad equation reveals that the transverse laser intensity distribution and two Λ-EIT systems must be included to fully account for the measured line width and line shape of the signals. Ground-state decoherence, caused by effects that include residual optical frequency fluctuations, atom-wall and trace-gas collisions, is discussed. 

We demonstrate laser induced DC electric fields in an all-glass vapor cell without bulk or thin film electrodes. The spatial field distribution is mapped by Rydberg electromagnetically induced transparency (EIT) spectroscopy. The fields are generated by a photoelectric effect and allow DC electric field tuning of up to 0.8 V/cm within the Rydberg EIT probe region. We explain the measured with a boundary-value electrostatic model. This work may inspire new approaches for DC electric field control in designing miniaturized atomic vapor cell devices. Limitations and other charge effects are also discussed.