Karya: Elevating Ethical Data for AI & Data Workers to the Middle Class

by Gloria Lee and Jayson Wang


Karya is a social enterprise bringing dignified, digital work, and upskilling to economically disadvantaged workers in the Global South, offering a pathway out of poverty. It provides above-market wages and royalties, supporting both professional growth and higher-skilled data work. For AI/ML clients, including major Silicon Valley companies, Karya offers high-quality, high-accuracy ethical data annotation services for AI/ML clients. Amidst an explosion of generative artificial intelligence, few consider the workers producing the large datasets required to train AI models. India-based Karya’s dream is a world in which data can both financially and technologically empower millions of individuals living in poverty.

Learning Objectives


1. Apply frameworks to evaluate a social enterprise’s efficacy, financial sustainability, and scale.

2. Understand the tensions and tradeoffs in strategic decisions as an organization strives to maximize social benefit and financial health at scale.

3. Explore the ways in which the company can influence the development of the larger industry and ecosystem in which it operates.

Details

Pub Date: October 1, 2025

Discipline: Strategy

Keywords: Mission, Strategy, Business model development, Business model innovation, Business models, Corporate social responsibility, Social enterprise, Social entrepreneurship, Entrepreneurship, Social innovation, Artificial intelligence, Language

Product #: B6079-PDF-ENG

Industry: Translation Services, Professional services

Geography: India, United States

Length: 9 page(s)


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Berkeley Haas Case Series The Berkeley Haas Case Series is a collection of business case studies written by faculty members at the Haas School of Business. Cases are conceived, developed, written, and published throughout the year, on subjects ranging from entrepreneurship and strategy to finance and marketing. Each case includes a teaching note for use in the classroom.

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