From healthcare to hiring—AI decisions increasingly shape the industries that impact our everyday lives. Ensuring that these systems are fair, transparent, and accountable is more crucial than ever.
That’s where FAI3 Network (Fair AI in Web3) comes in.
FAI3 is on a mission to make AI trustworthy by certifying AI models on key metrics like fairness, accuracy, and transparency using blockchain technology. By offering a decentralized, open-source platform, FAI3 allows businesses, researchers, and users to access a public leaderboard that directly displays AI models’ performance. Their goal is simple yet ambitious: to make AI more reliable, ethical, and inclusive for all, ensuring that users know what goes on inside the algorithms they rely on.
We spoke with the FAI3 team about their vision, approach, and how they’re changing the way AI is evaluated.
Q: What inspired you to start FAI3, and what problem does it aim to solve?
Answer:
I started FAI3 (Fair AI in Web3) with the goal of addressing the transparency and fairness challenges that many AI models face today. As AI becomes more integrated into industries like finance, healthcare, and hiring, it’s crucial to ensure that these algorithms are transparent, unbiased, and held accountable for their decisions.
FAI3 aims to solve the problem of biased or improperly tested AI algorithms by providing a framework for auditing and certifying AI models in a decentralized and transparent manner. We do this by verifying data quality, inference, and code on-chain, ensuring privacy while also making AI performance metrics public. This creates a trusted environment for companies, consumers, and developers, enabling more responsible AI usage.
Our mission is to reduce the risks of discrimination and unfair practices in AI, whether it’s in credit scoring, hiring, or healthcare, by making AI models more reliable, ethical, and explainable.
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Q: How does FAI3 help make AI more fair and accurate for everyday users?
Answer:
FAI3 allows everyday users to easily check on its public leaderboard whether the AI methods or companies they use are certified. This leaderboard displays key metrics related to fairness, accuracy, and bias, helping users make informed decisions by seeing the performance of the AI systems they rely on. It offers transparency, ensuring that the tools impacting their lives have been properly audited and meet ethical standards.
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Q: Why did you choose to use blockchain technology to evaluate AI models?
Answer:
I chose to use blockchain technology to evaluate AI models because it offers unparalleled transparency, accountability, and security. Blockchain’s decentralized nature ensures that the certification of AI models is transparent and accessible to the public, with records that are immutable and verifiable by anyone. This provides a level of trust and openness that traditional systems lack. Additionally, blockchain allows us to maintain data privacy while still verifying important performance metrics, which is crucial when working with sensitive information, such as proprietary algorithms or confidential data. The decentralized aspect of blockchain also removes the need for a central authority, making the certification process more objective and fair. Security is another major factor—blockchain’s cryptographic design ensures that records of AI evaluations cannot be tampered with, protecting the integrity of the entire process. By using blockchain, we are able to offer a trustworthy and robust system for auditing AI models that both companies and everyday users can rely on.
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Q: What kind of support do you provide businesses or researchers using your platform?
Answer:
For businesses, we offer assistance in integrating their AI models into our auditing framework, helping them prepare their data, code, and model outputs for evaluation. This includes guidance on how to meet our certification standards, ensuring that their AI systems are fair, accurate, and transparent. We also offer post-certification support, advising companies on how to improve their models if they fail to meet the required metrics.
For researchers, FAI3 offers the unique opportunity to actively contribute to the auditing protocol by proposing new metrics to enhance the evaluation system. This allows them to shape and improve the way AI models are assessed, fostering innovation and collaboration in developing more comprehensive and effective auditing criteria. Researchers can engage directly with the platform, helping to drive the advancement of AI auditing standards while also validating their own models within the framework.
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Q: How does FAI3 ensure that the data used by AI models is safe and reliable?
Answer:
FAI3 ensures that the data used by AI models is safe and reliable through two key mechanisms: zero-knowledge proofs and data certification. By using zero-knowledge proofs, we generate proof of inference, allowing businesses to validate their AI models without needing to upload their data, ensuring both privacy and security. In addition, FAI3 conducts a thorough data certification process, verifying that the data distributions align with expected patterns, ensuring the reliability and fairness of the models.
Moreover, during the verification process, we display the distribution of the data the model was tested on. This transparency allows users to see if any populations were excluded from the data and whether the model accounted for these gaps. This ensures that AI models are not only accurate but also inclusive and fair in their application.
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Q: What are you most excited about in the future of FAI3?
Answer:
I am most excited about the potential of FAI3 to drive meaningful change in the AI landscape by promoting transparency, fairness, and accountability through the collaboration of science and industry. By bringing together researchers and businesses on our platform, we can bridge the gap between theoretical advancements and practical applications, ensuring that ethical considerations are embedded in AI development from the ground up. As we continue to refine our auditing and certification processes, I look forward to seeing more organizations embrace FAI3 to ensure their AI models are ethical and reliable. The ability for researchers to propose new metrics will foster innovation, allowing us to continuously improve our auditing system in response to evolving challenges in AI. This collaboration not only enhances our approach to auditing but also helps align industry standards with the latest scientific insights, ultimately contributing to a more accountable and trustworthy AI ecosystem that benefits everyone.
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Fair AI Is The Only Future of AI
FAI3 is setting a new standard for transparency and accountability in AI, ensuring that the models shaping our world are both fair and reliable. By combining blockchain’s strengths with rigorous auditing protocols, they’re making it possible for everyone—users, companies, and researchers alike—to understand what goes on inside AI systems.
FAI3’s approach isn’t just about tech; it’s about creating a future where AI serves people without hidden biases or opaque decision-making. As they continue to grow and innovate, FAI3 is poised to play a pivotal role in the evolving conversation around AI ethics, offering a clear path to a more equitable digital world.