Paper Presented : Evaluation and Benchmarking of AI Applications in Indian Languages: A Critical Examination
Venue of Presentation: Central Institute of Indian Languages (CIIL) - Mysore, India
Date of Presentation : 21 March, 2025
Paper Title : Evaluation and Benchmarking of AI Applications in Indian Languages: A Critical Examination
Conference/Event : National Conference on AI Benchmarking for Indic Languages held at CIIL - Mysore, India in March 2025.
Abstract : This paper examines the evaluation and benchmarking of AI applications in Indian languages, highlighting their potential to improve communication, education, translation, and digital accessibility in India’s multilingual society. It discusses key challenges such as limited datasets, linguistic diversity, code-switching, dialectal variations, and ethical concerns including bias, misinformation, deepfakes, and voice cloning. The study emphasizes the need for accurate, inclusive, transparent, and fair AI systems through proper benchmarking methods. It also suggests solutions such as government–industry collaboration, open-source resources, public awareness, ethical regulations, and fairness audits to ensure reliable and socially responsible AI development for Indian languages.
Key Highlights : India’s linguistic diversity creates significant opportunities for AI applications in regional and multilingual communication.
AI applications in Indian languages can support:
Translation and communication
Education and learning
Language preservation
Digital accessibility
Content generation
Participation in the digital economy.
Evaluation and benchmarking are essential to ensure AI systems are:
Accurate
Fluent and natural
Inclusive
Fair and unbiased
Robust and user-friendly
Transparent and secure.
Major challenges in Indian language AI include:
Lack of standardized datasets
Limited resources for low-resource languages
Code-switching and multilingual usage
Dialectal variations
Bias and ethical concerns
Difficulty in evaluating fluency and naturalness.
The paper highlights risks associated with AI misuse, such as:
Deepfakes
Voice cloning
AI-generated misinformation
Fraudulent messages and scams.
Recommendations include:
Government and industry collaboration
Open-source datasets and models
Fairness audits and ethical guidelines
Public awareness and AI literacy
Development of AI-detection tools
Explainable and transparent AI systems.
The study concludes that collaborative efforts among researchers, policymakers, developers, and language experts are necessary to build trustworthy and inclusive AI systems for Indian languages.
Research Areas : Based on the attached paper, the following research areas can be identified:
a. Artificial Intelligence in Indian Languages
Development of AI systems for multilingual and regional language processing.
b. Natural Language Processing (NLP)
Machine translation, speech recognition, text generation, summarization, and language understanding for Indian languages.
c. Evaluation and Benchmarking of AI Models
Designing metrics and frameworks to evaluate AI performance, fluency, inclusivity, fairness, and robustness.
d. Low-Resource Language Processing
Research on AI systems for languages with limited datasets and linguistic resources.
e. Code-Switching and Multilingual AI
Studying mixed-language communication patterns such as Hinglish and multilingual text processing.
f. AI Ethics and Fairness
Bias detection, cultural sensitivity, neutrality, and ethical AI development.
g. Explainable AI (XAI)
Developing transparent AI systems that explain their decisions and outputs.
h. AI Security and Misinformation Detection
Detection and prevention of deepfakes, voice cloning, fake alerts, scams, and AI-generated misinformation.
i. AI in Education and Digital Inclusion
AI-powered learning platforms, tutoring systems, and accessibility solutions for regional language users.
j. Dataset Creation and Language Resource Development
Building annotated datasets, corpora, and benchmark resources for Indian languages.
Download Paper PDF
Explore Presentation - PPT
