Energy justice is a growing area of interest in interdisciplinary energy
research. However, identifying systematic biases in the energy sector remains
challenging due to confounding variables, intricate heterogeneity in treatment
effects, and limited data availability. To address these challenges, we
introduce a novel approach for counterfactual causal analysis centered on
energy justice. We use subgroup analysis to manage diverse factors and leverage
the idea of transfer learning to mitigate data scarcity in each subgroup. In
our numerical analysis, we apply our method to a large-scale customer-level
power outage data set and investigate the counterfactual effect of demographic
factors, such as income and age of the population, on power outage durations.
Our results indicate that low-income and elderly-populated areas consistently
experience longer power outages, regardless of weather conditions. This points
to existing biases in the power system and highlights the need for focused
improvements in areas with economic challenges.

在能源公正领域，通过针对能源公正进行反事实因果分析，我们发现经济困难地区存在不公正现象，表明电力系统存在现有的偏见，同时也突出了这些领域需要有针对性的改进。

基于大规模停电数据的转移反事实学习技术 识别电力服务公平问题

Detecting Electricity Service Equity Issues with Transfer Counterfactual  Learning on Large-Scale Outage Datasets

Training the deep neural networks that dominate NLP requires large datasets.
These are often collected automatically or via crowdsourcing, and may exhibit
systematic biases or annotation artifacts. By the latter we mean spurious
correlations between inputs and outputs that do not represent a generally held
causal relationship between features and classes; models that exploit such
correlations may appear to perform a given task well, but fail on out of sample
data. In this paper we evaluate use of different attribution methods for aiding
identification of training data artifacts. We propose new hybrid approaches
that combine saliency maps (which highlight important input features) with
instance attribution methods (which retrieve training samples influential to a
given prediction). We show that this proposed training-feature attribution can
be used to efficiently uncover artifacts in training data when a challenging
validation set is available. We also carry out a small user study to evaluate
whether these methods are useful to NLP researchers in practice, with promising
results. We make code for all methods and experiments in this paper available.

本文提出了一种新的训练特征归因方法，通过结合显著性图和实例归因方法，可以有效地发现训练数据中的人工瑕疵，并在实验中取得了良好的结果。

结合特征和实例归因来检测伪影

Combining Feature and Instance Attribution to Detect Artifacts

While state-of-the-art NLP models have been achieving the excellent
performance of a wide range of tasks in recent years, important questions are
being raised about their robustness and their underlying sensitivity to
systematic biases that may exist in their training and test data. Such issues
come to be manifest in performance problems when faced with out-of-distribution
data in the field. One recent solution has been to use counterfactually
augmented datasets in order to reduce any reliance on spurious patterns that
may exist in the original data. Producing high-quality augmented data can be
costly and time-consuming as it usually needs to involve human feedback and
crowdsourcing efforts. In this work, we propose an alternative by describing
and evaluating an approach to automatically generating counterfactual data for
data augmentation and explanation. A comprehensive evaluation on several
different datasets and using a variety of state-of-the-art benchmarks
demonstrate how our approach can achieve significant improvements in model
performance when compared to models training on the original data and even when
compared to models trained with the benefit of human-generated augmented data.

提出了一种自动产生对立数据的方法，基于此数据增广训练集可以有效提高 NLP 模型的性能。

探究自动生成的反事实数据在情感分析中的有效性

Exploring the Efficacy of Automatically Generated Counterfactuals for  Sentiment Analysis

Predictive models deployed in the real world may assign incorrect labels to
instances with high confidence. Such errors or unknown unknowns are rooted in
model incompleteness, and typically arise because of the mismatch between
training data and the cases encountered at test time. As the models are blind
to such errors, input from an oracle is needed to identify these failures. In
this paper, we formulate and address the problem of informed discovery of
unknown unknowns of any given predictive model where unknown unknowns occur due
to systematic biases in the training data. We propose a model-agnostic
methodology which uses feedback from an oracle to both identify unknown
unknowns and to intelligently guide the discovery. We employ a two-phase
approach which first organizes the data into multiple partitions based on the
feature similarity of instances and the confidence scores assigned by the
predictive model, and then utilizes an explore-exploit strategy for discovering
unknown unknowns across these partitions. We demonstrate the efficacy of our
framework by varying the underlying causes of unknown unknowns across various
applications. To the best of our knowledge, this paper presents the first
algorithmic approach to the problem of discovering unknown unknowns of
predictive models.

本文提出了一个模型无关方法，该方法使用来自一个 oracle 的反馈来识别和智能引导 unknown unknowns 的发现，我们采用两阶段方法，我们首先根据实例特征的相似性和预测模型分配的置信度将数据组织成多个分区，然后利用探索 - 开发策略在这些分区中发现未知的未知情况。