# IndiBias: A Benchmark Dataset to Measure Social Biases in Language Models for Indian Context

arXiv: https://arxiv.org/abs/2403.20147 · HTML: https://mlsanity.com/paper/2403.20147/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 59 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 30 of 30)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://mlsanity.com/paper/2010.00133/) (2020)
- [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://mlsanity.com/paper/1707.09457/) (2017)
- [On Measuring Social Biases in Sentence Encoders](https://mlsanity.com/paper/1903.10561/) (2019)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Societal Biases in Language Generation: Progress and Challenges](https://mlsanity.com/paper/2105.04054/) (2021)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings](https://mlsanity.com/paper/1904.04047/) (2019)
- [mGPT: Few-Shot Learners Go Multilingual](https://mlsanity.com/paper/2204.07580/) (2022)
- [Assessing Social and Intersectional Biases in Contextualized Word Representations](https://mlsanity.com/paper/1911.01485/) (2019)
- [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021)
- [Re-imagining Algorithmic Fairness in India and Beyond](https://mlsanity.com/paper/2101.09995/) (2021)
- [Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages](https://mlsanity.com/paper/2212.05409/) (2022)
- [Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model](https://mlsanity.com/paper/2008.06460/) (2020)
- [SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models](https://mlsanity.com/paper/2305.11840/) (2023)
- [Re-contextualizing Fairness in NLP: The Case of India](https://mlsanity.com/paper/2209.12226/) (2022)
- [Socially Aware Bias Measurements for Hindi Language Representations](https://mlsanity.com/paper/2110.07871/) (2021)
- [Mitigating Gender Bias Amplification in Distribution by Posterior Regularization](https://mlsanity.com/paper/2005.06251/) (2020)
- [AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings](https://mlsanity.com/paper/2011.01575/) (2020)
- [Detecting Unintended Social Bias in Toxic Language Datasets](https://mlsanity.com/paper/2210.11762/) (2022)
- [Smart, Responsible, and Upper Caste Only: Measuring Caste Attitudes through Large-Scale Analysis of Matrimonial Profiles](https://mlsanity.com/paper/1904.04176/) (2019)
- [Hollywood Identity Bias Dataset: A Context Oriented Bias Analysis of Movie Dialogues](https://mlsanity.com/paper/2205.15951/) (2022)

## Built on by (most cited first, 30 of 32)

- [Measuring what Matters: Construct Validity in Large Language Model Benchmarks](https://mlsanity.com/paper/2511.04703/) (2025)
- [Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://mlsanity.com/paper/2406.03930/) (2024)
- [Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs](https://mlsanity.com/paper/2503.05371/) (2025)
- [Social Bias in Large Language Models For Bangla: An Empirical Study on Gender and Religious Bias](https://mlsanity.com/paper/2407.03536/) (2024)
- [DRISHTIKON: A Multimodal Multilingual Benchmark for Testing Language Models' Understanding on Indian Culture](https://mlsanity.com/paper/2509.19274/) (2025)
- [DECASTE: Unveiling Caste Stereotypes in Large Language Models through Multi-Dimensional Bias Analysis](https://mlsanity.com/paper/2505.14971/) (2025)
- [FairI Tales: Evaluation of Fairness in Indian Contexts with a Focus on Bias and Stereotypes](https://mlsanity.com/paper/2506.23111/) (2025)
- [KULTURE Bench: A Benchmark for Assessing Language Model in Korean Cultural Context](https://mlsanity.com/paper/2412.07251/) (2024)
- [BharatBBQ: A Multilingual Bias Benchmark for Question Answering in the Indian Context](https://mlsanity.com/paper/2508.07090/) (2025)
- [BanStereoSet: A Dataset to Measure Stereotypical Social Biases in LLMs for Bangla](https://mlsanity.com/paper/2409.11638/) (2024)
- [McBE: A Multi-task Chinese Bias Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2507.02088/) (2025)
- [Surfacing Subtle Stereotypes: A Multilingual, Debate-Oriented Evaluation of Modern LLMs](https://mlsanity.com/paper/2511.01187/) (2025)
- [Common to Whom? Regional Cultural Commonsense and LLM Bias in India](https://mlsanity.com/paper/2601.15550/) (2026)
- [Purdah and Patriarchy: Evaluating and Mitigating South Asian Biases in Open-Ended Multilingual LLM Generations](https://mlsanity.com/paper/2505.18466/) (2025)
- [Social Bias in Multilingual Language Models: A Survey](https://mlsanity.com/paper/2508.20201/) (2025)
- [IndiCASA: A Dataset and Bias Evaluation Framework in LLMs Using Contrastive Embedding Similarity in the Indian Context](https://mlsanity.com/paper/2510.02742/) (2025)
- [Camellia: Benchmarking Cultural Biases in LLMs for Asian Languages](https://mlsanity.com/paper/2510.05291/) (2025)
- [Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large Language Models](https://mlsanity.com/paper/2407.06917/) (2024)
- [From n-gram to Attention: How Model Architectures Learn and Propagate Bias in Language Modeling](https://mlsanity.com/paper/2505.12381/) (2025)
- [NEAT: Concept driven Neuron Attribution in LLMs](https://mlsanity.com/paper/2508.15875/) (2025)
- [AmchiBias: Measuring Stereotypical Bias in Goan Identity Groups with a Minimal Pair Dataset in English and Konkani](https://mlsanity.com/paper/2606.15191/) (2026)
- [Stereotype Detection as a Catalyst for Enhanced Bias Detection: A Multi-Task Learning Approach](https://mlsanity.com/paper/2507.01715/) (2025)
- [IndicFairFace: Balanced Indian Face Dataset for Auditing and Mitigating Geographical Bias in Vision-Language Models](https://mlsanity.com/paper/2602.12659/) (2026)
- [Analysing LLM Persona Generation and Fairness Interpretation in Polarised Geopolitical Contexts](https://mlsanity.com/paper/2603.22837/) (2026)
- [Invisible Influences: Investigating Implicit Intersectional Biases through Persona Engineering in Large Language Models](https://mlsanity.com/paper/2604.06213/) (2026)
- [Safety Alignment Illusion: The Cross-Lingual Safety Gap in LLMs](https://mlsanity.com/paper/2608.18131/) (2026)
- [Which India Survives Translation? Narrative Homogenisation Across Indian Oral Traditions in LLMs](https://mlsanity.com/paper/2608.26123/) (2026)
- [LLMs Mirror Country-Specific Gender Patterns If Asked, but Skew Male When Generating Media in Local Languages](https://mlsanity.com/paper/2609.06545/) (2026)
- [CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systems](https://mlsanity.com/paper/2609.22494/) (2026)
- [FineWeb-CLaR: Culture, Language, and Region Annotations for Benchmark-Aligned Corpus Auditing](https://mlsanity.com/paper/2609.25298/) (2026)

## Abstract (arXiv)

The pervasive influence of social biases in language data has sparked the need for benchmark datasets that capture and evaluate these biases in Large Language Models (LLMs). Existing efforts predominantly focus on English language and the Western context, leaving a void for a reliable dataset that encapsulates India's unique socio-cultural nuances. To bridge this gap, we introduce IndiBias, a comprehensive benchmarking dataset designed specifically for evaluating social biases in the Indian context. We filter and translate the existing CrowS-Pairs dataset to create a benchmark dataset suited to the Indian context in Hindi language. Additionally, we leverage LLMs including ChatGPT and InstructGPT to augment our dataset with diverse societal biases and stereotypes prevalent in India. The included bias dimensions encompass gender, religion, caste, age, region, physical appearance, and occupation. We also build a resource to address intersectional biases along three intersectional dimensions. Our dataset contains 800 sentence pairs and 300 tuples for bias measurement across different demographics. The dataset is available in English and Hindi, providing a size comparable to existing benchmark datasets. Furthermore, using IndiBias we compare ten different language models on multiple bias measurement metrics. We observed that the language models exhibit more bias across a majority of the intersectional groups.