Measuring Social Biases in Language Models for the Indian Context: The IndiBias Benchmark Dataset

Core Concepts

IndiBias is a comprehensive benchmark dataset designed to measure and quantify social biases in language models for the Indian context, addressing biases across multiple dimensions including gender, religion, caste, age, region, physical appearance, and occupation.

Abstract

The paper introduces IndiBias, a benchmark dataset for evaluating social biases in language models in the Indian context. The dataset is composed of three key components: An Indian version of the CrowS-Pairs dataset, created by translating and adapting the original English sentences to the Indian context in both English and Hindi. A set of 300 identity-attribute tuples capturing positive and negative stereotypes prevalent in Indian society across seven demographic dimensions: gender, religion, caste, age, region, physical appearance, and occupation. A resource of bleached sentences to measure intersectional biases along three axes: gender-religion, gender-caste, and gender-age. The authors discuss the challenges faced in adapting the existing CrowS-Pairs dataset to the Indian context, such as handling machine translation issues, ensuring cultural relevance, and maintaining the minimally distant property of sentence pairs. They also highlight the importance of considering intersectional biases, which can be more pronounced in the diverse Indian context. The dataset is evaluated using ten multilingual language models, including India-specific models like IndicBERT and Muril. The results show that the models exhibit varying degrees of bias across different demographic groups and intersectional identities, with some models performing better than others in terms of reduced bias.

Stats

"Women can't do math." "Women wearing traditional attire in Rajasthan are seen as conservative." "Women wearing traditional attire in West Bengal are seen as cultural ambassadors."

Quotes

"India is a country with many different languages, religions, castes, and regional identities. Ergo, it is important to create thorough frameworks for measuring and reducing biases that are suited to many different aspects of this country." "The fast adoption of NLP applications in India's legal, medical, education, and media sectors necessitates ensuring LM's fairness for the Indian context."

Key Insights Distilled From

IndiBias

by Nihar Ranjan... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.20147.pdf

Deeper Inquiries

How can the IndiBias dataset be expanded to cover a wider range of stereotypes and biases, including those that are not explicitly expressed in text?

Expanding the IndiBias dataset to cover a wider range of stereotypes and biases, especially those not explicitly expressed in text, can be achieved through several strategies: Incorporating Implicit Biases: Conducting surveys, interviews, or focus groups with individuals from diverse backgrounds to uncover implicit biases that may not be overtly stated in text data. This qualitative data can then be used to create scenarios or prompts that capture these implicit biases. Utilizing Multimedia Data: Including images, videos, audio clips, or social media content in the dataset to capture biases that are conveyed through non-textual mediums. This can help in understanding biases related to visual representations, tone of voice, or gestures. Collaborating with Experts: Working with sociologists, psychologists, anthropologists, and other experts in social sciences to identify and define a broader spectrum of stereotypes and biases prevalent in Indian society. Their insights can guide the creation of new data points for the dataset. Exploring Historical and Cultural Contexts: Delving into historical texts, cultural artifacts, folklore, and traditions to uncover deep-rooted biases that may not be explicitly articulated in modern language. This historical perspective can enrich the dataset with nuanced biases. Addressing Intersectionality: Considering the intersection of multiple identities (e.g., gender, caste, religion) to capture the compounded effects of biases. Creating scenarios that reflect the complex interplay of various social dimensions can enhance the dataset's coverage of diverse biases. By incorporating these approaches, the IndiBias dataset can evolve to encompass a more comprehensive range of stereotypes and biases, providing a richer resource for evaluating and mitigating biases in language models.

How can the dataset be used to develop debiasing techniques that are specifically tailored to the Indian context, considering its unique socio-cultural diversity?

Developing debiasing techniques tailored to the Indian context using the IndiBias dataset involves the following steps: Identifying Biased Patterns: Analyzing the dataset to identify prevalent biases and stereotypes specific to the Indian socio-cultural landscape. Understanding the unique manifestations of bias in Indian contexts is crucial for effective debiasing. Feature Engineering: Leveraging the dataset to create features that capture the nuanced biases present in Indian language data. This may involve encoding intersectional identities, cultural references, or regional variations that influence biases. Model Training: Training debiasing models on the IndiBias dataset to learn to recognize and mitigate biased patterns in Indian language data. Techniques like adversarial training, bias-aware embeddings, or counterfactual data augmentation can be employed. Evaluation and Fine-Tuning: Evaluating the performance of debiasing models on the dataset and fine-tuning them to address specific biases prevalent in the Indian context. Iterative refinement based on feedback from domain experts and marginalized communities is essential. Ethical Considerations: Ensuring that debiasing techniques are ethically sound and culturally sensitive. Engaging with diverse stakeholders, including community representatives, to validate the effectiveness and fairness of the debiasing methods. By tailoring debiasing techniques to the nuances of the Indian context using the IndiBias dataset, researchers can develop more effective and culturally relevant strategies to mitigate biases in Indian language models.

What are the potential implications of unaddressed biases in language models on marginalized communities in India, and how can the research community work towards more inclusive and equitable NLP systems?

The unaddressed biases in language models can have significant implications for marginalized communities in India, including: Reinforcement of Stereotypes: Unaddressed biases can perpetuate harmful stereotypes against marginalized groups, leading to discrimination, exclusion, and stigmatization in various societal contexts. Algorithmic Discrimination: Biased language models can result in algorithmic discrimination, where automated decisions based on these models disadvantage marginalized communities in areas such as hiring, lending, and law enforcement. Limited Representation: Biases in language models can result in underrepresentation or misrepresentation of marginalized voices and experiences, further marginalizing these communities in the digital space. Impact on Access and Opportunities: Biased NLP systems can restrict access to resources, opportunities, and services for marginalized groups, exacerbating existing inequalities and hindering social mobility. To work towards more inclusive and equitable NLP systems, the research community can take the following steps: Diverse Dataset Collection: Prioritize the collection of diverse and representative datasets that capture the full spectrum of identities, cultures, and languages present in India, ensuring inclusivity in training data. Bias Detection and Mitigation: Develop robust tools and methodologies to detect and mitigate biases in language models, with a specific focus on addressing intersectional biases that affect marginalized communities. Community Engagement: Engage with marginalized communities, advocacy groups, and domain experts to co-create solutions that address their specific needs and concerns, ensuring that NLP systems are designed with inclusivity in mind. Transparency and Accountability: Promote transparency in the development and deployment of NLP systems, including disclosing biases, limitations, and potential impacts on marginalized communities. Establish mechanisms for accountability and redressal in case of bias-related harms. By actively addressing biases, promoting diversity, and centering the voices of marginalized communities in NLP research and development, the research community can contribute to building more ethical, fair, and inclusive language technologies for India.