Fair AI


Fair AI is a new approach to artificial intelligence (AI) that has an emphasis on decentralization and equitability through rewarding individuals for their data and compute contributions.

What Is Fair AI?

Fair AI is a new approach to artificial intelligence (AI) that has an emphasis on decentralization and equitability through rewarding individuals for their data and compute contributions. The core principles of Fair AI are ownership, permission, and fair compensation.

Currently, Big Tech companies dominate the AI landscape, which is in much need of a major shift. Fair AI addresses the challenges within Big AI companies around data ownership, attribution, and compensation while creating a decentralized economy for the compute and bandwidth required to power AI. 

In this emerging market, where data is the new currency and AI is becoming increasingly adopted, Fair AI aims to ensure that those contributing data, compute or bandwidth are compensated in a fair and just manner. 

The Problem with Centralized AI Models & Companies

The centralized players powering the leading Large Language Models (LLMs) and AI industry have faced a vast amount of criticism in terms of transparency and fairness. AI models built by centralized players do not disclose the sources of their training data, or attribute their sources, even when copyrighted. The companies have landed themselves in hot water through lawsuits and legal risks. Citing the public web as their primary source, this public web data is powered by individuals from all over the world. 

Due to the AI boom, data is being stolen from users and locked behind walled gardens. Thus, data has become inaccessible, unusable and extremely costly. Centralized AI companies are amassing billions, soon to be trillions, in value, while limiting developer access, and stifling innovation. While users have received the benefit of enhanced productivity due to AI tools, it has come at the cost of millions of jobs, privacy, and the sacrifice of the value of their data. 

In addition, centralized AI companies have cornered the GPU market, building massive data centers Centralization cuts individuals and small businesses out of the compute AI marketplace, while billions of users have devices and energy sources that can contribute to the resources needed to power AI.

Decentralization as a Solution

Fair AI, as pioneered by Masa, decentralizes and democratizes data access and contributes to power AI. Through incentivizing individuals to contribute data, compute, and bandwidth to power its AI network, Masa aims to empower developers to access and utilize high-quality data while rewarding those contributing compute to power advanced and specialized AI applications. It aims to create a new economic system where individuals are rewarded for their contributions, powered by the blockchain

As a Fair AI network, it focuses on fairness and openness, rewarding contributors whenever their data, compute or bandwidth is used, alongside a staking model that helps to stabilize the network. With this type of model, Fair AI is built through trust, transparency, and collaboration.

Authored by: Calanthia Mei, Co-Founder of Masa

Calanthia is the co-founder of Masa and a leading global fintech investor and builder. She was a founding member of PayPal’s Venture Capital arm. At PayPal, she oversaw $250 million in investments in hyper-growth global fintech startups, including Toss in South Korea, and incubated crypto product and investment strategy, including Coinbase’s 2018 partnership with PayPal. Calanthia most recently scaled a Stripe-backed Fintech startup to 450 employees, raised $130 million in funding, and was acquired by a public company – all in a short span of 2 years. Transitioning her focus towards decentralized AI, Calanthia believes in leveraging technology to enhance global inclusivity and equality. She has shared her expert insights during broadcast appearances on platforms like the New York Stock Exchange, CNBC, and NASDAQ, and is known for embodying a global perspective that bridges the US and international tech landscapes.