Recent research has revealed undesirable biases in NLP data and models.
However, these efforts focus on social disparities in West, and are not
directly portable to other geo-cultural contexts. In this paper, we focus on
NLP fair-ness in the context of India. We start with a brief account of the
prominent axes of social disparities in India. We build resources for fairness
evaluation in the Indian context and use them to demonstrate prediction biases
along some of the axes. We then delve deeper into social stereotypes for Region
andReligion, demonstrating its prevalence in corpora and models. Finally, we
outline a holistic research agenda to re-contextualize NLP fairness research
for the Indian context, ac-counting for Indian societal context, bridging
technological gaps in NLP capabilities and re-sources, and adapting to Indian
cultural values. While we focus on India, this framework can be generalized to
other geo-cultural contexts.

本文旨在研究印度社会准则在 NLP 的公正性中的作用，通过对印度社会背景、资源以及文化价值观的分析，建立公正性评估模型，探讨预测偏见和社会刻板印象的存在，并提出改进 NLP 公正性的全面研究议程，为其他地缘文化背景的研究提供借鉴。

NLP 中的公正性再概念化：以印度为例

Re-contextualizing Fairness in NLP: The Case of India

A crucial but often neglected aspect of algorithmic fairness is the question
of how we justify enforcing a certain fairness metric from a moral perspective.
When fairness metrics are proposed, they are typically argued for by
highlighting their mathematical properties. Rarely are the moral assumptions
beneath the metric explained. Our aim in this paper is to consider the moral
aspects associated with the statistical fairness criterion of independence
(statistical parity). To this end, we consider previous work, which discusses
the two worldviews "What You See Is What You Get" (WYSIWYG) and "We're All
Equal" (WAE) and by doing so provides some guidance for clarifying the possible
assumptions in the design of algorithms. We present an extension of this work,
which centers on morality. The most natural moral extension is that
independence needs to be fulfilled if and only if differences in predictive
features (e.g. high school grades and standardized test scores are predictive
of performance at university) between socio-demographic groups are caused by
unjust social disparities or measurement errors. Through two counterexamples,
we demonstrate that this extension is not universally true. This means that the
question of whether independence should be used or not cannot be satisfactorily
answered by only considering the justness of differences in the predictive
features.

本文分析算法公正中的道德层面，提出统计公平准则（独立性）需要在不公正社会差异或测量误差引发预测特征差异的情况下实现，同时引证两个反例说明这一准则并不是普遍适用的。

论统计平等的道德正当性

On the Moral Justification of Statistical Parity

As more industries integrate machine learning into socially sensitive
decision processes like hiring, loan-approval, and parole-granting, we are at
risk of perpetuating historical and contemporary socioeconomic disparities.
This is a critical problem because on the one hand, organizations who use but
do not understand the discriminatory potential of such systems will facilitate
the widening of social disparities under the assumption that algorithms are
categorically objective. On the other hand, the responsible use of machine
learning can help us measure, understand, and mitigate the implicit historical
biases in socially sensitive data by expressing implicit decision-making mental
models in terms of explicit statistical models. In this paper we specify,
implement, and evaluate a "fairness-aware" machine learning interface called
themis-ml, which is intended for use by individual data scientists and
engineers, academic research teams, or larger product teams who use machine
learning in production systems.

本文介绍了一种名为 themis-ml 的公平性感知机器学习接口，可以帮助数据科学家和工程师更好地理解和降低社会敏感数据中的隐式历史偏见。