Re-contextualizing Fairness in NLP: The Case of India
The paper sheds light on the pressing need to address fairness in NLP within specific geo-cultural contexts, using India as a case paper. The research presented explores the social and technical intricacies associated with assessing and improving fairness in NLP models and datasets, considering India's unique socio-cultural fabric.
Overview of the Paper
The authors begin by acknowledging that much of the existing research on fairness in NLP is skewed toward Western norms, leaving a gap when it comes to applying these findings to non-Western contexts like India. They identify several axes of social disparities prevalent in India—such as Region, Caste, Gender, Religion, Ability, and Gender Identity and Sexual Orientation—and argue that these require distinct consideration in fairness research. The paper focuses specifically on Region and Religion as demonstrative axes of bias.
Methodology and Findings
To explore the biases inherent in NLP models, the authors utilize identity terms, personal names, and dialectal features as proxies for demographic subgroups. They conduct an empirical analysis using predictive models to demonstrate biases in sentiment and stereotype associations. For instance, the paper shows that sentiment prediction models exhibit varying sentiment shifts when regional identity terms are swapped, revealing implicit biases toward certain Indian regions.
The authors also curate and annotate a dataset of stereotypical associations for Region and Religion, aiming to identify and analyze biases in large NLP corpora and models. The results indicate a significant prevalence of stereotypes in model predictions, which are more pronounced when incorporating Indian names and dialectal features. This highlights the intensity of the biases when models are subjected to contextually relevant inputs.
Implications
The implications of this paper are multifold. Practically, it emphasizes the need for NLP models to be fine-tuned with context-specific data to ensure fairness across diverse geographies and cultures. Theoretically, the paper advocates for a more global understanding of fairness that transcends Western paradigms, pointing out that ethical AI needs to be aligned with local values and societal norms.
The paper proposes a holistic agenda for re-contextualizing NLP fairness in India, focusing on three key aspects: accounting for social disparities, bridging technological gaps across languages, and aligning fairness interventions with local value systems. The authors call for participatory approaches in data annotation, inclusion of diverse demographics in research, and culturally aligned justice models.
Future Directions
The research invites further exploration into intersectional biases and the development of resources across more axes and languages. It underscores the necessity of designing multilingual fairness evaluation strategies that account for variances in bias manifestation across languages. Additionally, given India's normative frameworks around fairness, particularly its restorative justice systems, future work could explore how these can inform computational ethics.
In summary, the paper presents an in-depth investigation into fairness in NLP, uniquely contextualized within the Indian socio-cultural landscape. It posits a framework that could be adapted for similar research in other non-Western contexts, urging the NLP community to consider broader, more inclusive definitions of fairness.