Cultural Re-contextualization of Fairness Research in Language
Technologies in India
- URL: http://arxiv.org/abs/2211.11206v1
- Date: Mon, 21 Nov 2022 06:37:45 GMT
- Title: Cultural Re-contextualization of Fairness Research in Language
Technologies in India
- Authors: Shaily Bhatt, Sunipa Dev, Partha Talukdar, Shachi Dave, Vinodkumar
Prabhakaran
- Abstract summary: Recent research has revealed undesirable biases in NLP data and models.
We re-contextualize fairness research for the Indian context, accounting for Indian societal context.
We also summarize findings from an empirical study on various social biases along different axes of disparities relevant to India.
- Score: 9.919007681131804
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent research has revealed undesirable biases in NLP data and models.
However, these efforts largely focus on social disparities in the West, and are
not directly portable to other geo-cultural contexts. In this position paper,
we outline a holistic research agenda to re-contextualize NLP fairness research
for the Indian context, accounting for Indian societal context, bridging
technological gaps in capability and resources, and adapting to Indian cultural
values. We also summarize findings from an empirical study on various social
biases along different axes of disparities relevant to India, demonstrating
their prevalence in corpora and models.
Related papers
- Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approach [8.805524738976073]
This research investigates the impact of Generative Artificial Intelligence (GAI) models, specifically text-to-image generators (T2Is), on the representation of non-Western cultures.
arXiv Detail & Related papers (2024-07-20T07:01:37Z) - Extrinsic Evaluation of Cultural Competence in Large Language Models [53.626808086522985]
We focus on extrinsic evaluation of cultural competence in two text generation tasks.
We evaluate model outputs when an explicit cue of culture, specifically nationality, is perturbed in the prompts.
We find weak correlations between text similarity of outputs for different countries and the cultural values of these countries.
arXiv Detail & Related papers (2024-06-17T14:03:27Z) - Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art [70.1063219524999]
The surge of interest in culturally aware and adapted Natural Language Processing has inspired much recent research.
The lack of common understanding of the concept of "culture" has made it difficult to evaluate progress in this emerging area.
We propose an extensive taxonomy of elements of culture that can provide a systematic framework for analyzing and understanding research progress.
arXiv Detail & Related papers (2024-06-06T10:16:43Z) - Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking [48.21982147529661]
This paper introduces a novel approach for massively multicultural knowledge acquisition.
Our method strategically navigates from densely informative Wikipedia documents on cultural topics to an extensive network of linked pages.
Our work marks an important step towards deeper understanding and bridging the gaps of cultural disparities in AI.
arXiv Detail & Related papers (2024-02-14T18:16:54Z) - A Material Lens on Coloniality in NLP [57.63027898794855]
Coloniality is the continuation of colonial harms beyond "official" colonization.
We argue that coloniality is implicitly embedded in and amplified by NLP data, algorithms, and software.
arXiv Detail & Related papers (2023-11-14T18:52:09Z) - An Inclusive Notion of Text [69.36678873492373]
We argue that clarity on the notion of text is crucial for reproducible and generalizable NLP.
We introduce a two-tier taxonomy of linguistic and non-linguistic elements that are available in textual sources and can be used in NLP modeling.
arXiv Detail & Related papers (2022-11-10T14:26:43Z) - Re-contextualizing Fairness in NLP: The Case of India [9.919007681131804]
We focus on NLP fair-ness in the context of India.
We build resources for fairness evaluation in the Indian context.
We then delve deeper into social stereotypes for Region andReligion, demonstrating its prevalence in corpora and models.
arXiv Detail & Related papers (2022-09-25T13:56:13Z) - Understanding misinformation in India: The case for a meaningful
regulatory approach for social media platforms [0.0]
This paper aims at introducing a coherent reading into the context of misinformation in the country and the subsequent social and business disruptions that will follow.
The literature sources have been mentioned in their respective sections for reference.
arXiv Detail & Related papers (2022-06-19T15:14:06Z) - Systematic Inequalities in Language Technology Performance across the
World's Languages [94.65681336393425]
We introduce a framework for estimating the global utility of language technologies.
Our analyses involve the field at large, but also more in-depth studies on both user-facing technologies and more linguistic NLP tasks.
arXiv Detail & Related papers (2021-10-13T14:03:07Z) - Re-imagining Algorithmic Fairness in India and Beyond [9.667710168953239]
We de-center algorithmic fairness and analyse AI power in India.
We find that data is not always reliable due to socio-economic factors.
We provide a roadmap to re-contextualise data and models, empower oppressed communities, and enable Fair-ML ecosystems.
arXiv Detail & Related papers (2021-01-25T10:20:57Z) - Non-portability of Algorithmic Fairness in India [9.8164690355257]
We argue that a mere translation of technical fairness work to Indian subgroups may serve only as a window dressing.
We argue that a collective re-imagining of Fair-ML, by re-contextualising data and models, empowering oppressed communities, and more importantly, enabling ecosystems.
arXiv Detail & Related papers (2020-12-03T23:14:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.