Ensuring Quality Data for AI: The Words, Worlds and Models (W2M) Program
Training professionals and researchers for collecting and working with language data from the field for building speech and language technologies.
Course Offerings
Our specialized curriculum equips participants with skills needed for ethical, high-quality language data collection across diverse communities.
*All our courses are offline, residential courses, conducted with tribal, underserved communities in the regions of their primary residence. The course fee covers access to course materials and lectures, modest accommodation in a dormitory/shared room for the duration of the course, all meals, payment for the community members, field visit and other incidental expenses. Depending on the focus language and community, it might differ across different iterations of the course.
**Each iteration of the course will include fieldwork with a different linguistic community within that language family.
W2M Merit-cum-Means Fellowship (W2MCM)
Financial Assistance to select participants to partially/fully offset the expenditure of attending the course. It is applicable only for students not receiving any fellowships from any other source. It will be reimbursed after the completion of the course depending on the participants performance in the course. Please indicate your preference for fellowship with the application for individual courses.
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The Growing Digital Divide
Current Reality
In today's AI-driven world, the digital divide is rapidly evolving into a profound social and economic divide. Despite our diverse real world, the digital landscape remains largely monolithic and non-inclusive.
This lack of representation affects communities, languages, and cultures that don't fit the dominant technological paradigm.
Growing Concerns
Governments, organizations, communities, and individuals are increasingly worried about how sociocultural norms, languages, practices, and identities are represented in AI systems.
There's a pressing need for sensitive, unbiased representation that reflects our world's true diversity, not just the perspectives of dominant groups.
Failed Inclusion Efforts
Lack of Quality Training Data
Despite numerous initiatives to create more inclusive AI systems, these efforts often fall short due to insufficient quality training data that properly represents diverse languages and cultures.
Questionable Methods
While the intent to include more languages and cultures exists, the approaches used to gather representative data have been problematic, sometimes producing harmful results.
High Rejection Rates
Documentation shows that up to 35-40% of originally collected data must be discarded due to quality issues, even with robust methodologies and reputable organizations involved.
The Data Collection Problem
Ad-hoc Collection Teams
Currently, speech and language data collection is primarily handled by organizations and individuals with little to no experience working with linguistic data, resulting in fundamental misunderstandings of its unique requirements.
Misunderstood Complexity
There's a widespread failure to recognize that linguistic and cultural data differ fundamentally from demographic data, requiring specialized expertise and methodologies.
Compromised Results
The consequence is predictable: noisy, distorted, and biased datasets that undermine the very inclusion goals these projects aim to achieve.
Inappropriate Organizations for the Task
Data Annotation Agencies
General annotation firms lack specific training in collecting authentic language data from diverse communities.
Social Sector Professionals
While skilled in community engagement, they typically lack linguistic field research expertise.
Language Promotion Organizations
Often focused on promoting specific language varieties rather than capturing linguistic diversity.
University Linguistics Departments
Even academic departments frequently lack proper fieldwork training and methodology for this specialized work.
Undertrained Field Coordinators
Current Hiring Focus
Field coordinators are typically selected for their ability to mobilize community participation rather than linguistic expertise.
Native Speaker Fallacy
Organizations incorrectly assume that native speakers automatically possess the skills to collect high-quality linguistic data from their communities.
Missing Training
Understanding and training in linguistic fieldwork is rarely considered relevant expertise for these roles.
Professional Gap
Just as patients can't diagnose themselves and users can't improve software, untrained native speakers can't reliably conduct linguistic field research.
The Scale Excuse
Quality Solution
Properly trained professionals collecting representative data
Current Justification
Scale requirements excuse poor quality collection
Real Problem
Lack of trained linguistic field researchers
Organizations frequently cite the massive scale of data collection efforts as justification for cutting corners on expertise and accepting poor-quality datasets. However, this reasoning overlooks the fundamental solution: properly training professionals and requiring that only qualified individuals and organizations participate in linguistic data collection.
Introducing Words, Worlds and Models (W2M)
Certification Courses
Short and long-term professional training programs designed for linguistic fieldwork
Regional Approach
Courses conducted offline across different regions of India
Community Engagement
Hands-on field experience working with diverse community members
Tailored Training
Separate courses for those with and without prior linguistics background
Program Goals and Outcomes
Build expert capacity
Create a network of trained linguistic field researchers
Improve data quality
Dramatically reduce rejection rates in collected datasets
Enhance representation
Ensure diverse languages and cultures in AI systems
The W2M program aims to address the critical shortage of qualified professionals who can collect high-quality linguistic data. By creating a pipeline of trained experts, the program will help ensure that AI systems reflect true linguistic and cultural diversity, reducing bias and improving representation for all communities.
Why This Matters: The Future of AI Inclusion
Authentic Voice
Quality data ensures AI systems recognize and respond to all language varieties
Equal Access
Inclusive AI enables participation regardless of language or culture
Reduced Bias
Representative data minimizes harmful stereotypes and exclusions
Economic Opportunity
Linguistic inclusion creates pathways to digital economy participation