Forget p(Doom): Here’s How to Hold AI Accountable by Creating an Inference Economy

By neub9
2 Min Read

AI’s existential risk is a serious concern. The tech industry is grappling with the uncertainty of whether the dystopian “control the world” notion is real or just science fiction.

Before addressing this issue, however, we must first confront a more immediate problem. Semi-autonomous AI agents are already making decisions and responding to their environment. From trading robots adjusting stock trades based on market conditions to ChatGPT generating work memos and school papers, AI’s practical issues are evident.

These practical issues include hallucinated citations, lazy decisions, sudden misdirections, and overall unpredictability. Trust in these systems, and the machine learning algorithms behind them, is hindered by this unpredictability.

Trusting unknown inferences is a significant concern, as demonstrated by real-world cases like the UnitedHealth lawsuit and the creditworthiness assessment industry’s prior mistakes.

Fortunately, there is a solution to this trust gap: the creation of an inference economy.

What Is an Inference Economy?

An inference economy provides a formal arena for addressing unreliable machine learning outputs. It commercializes the pursuit of honest, reliable, and secure machine learning production through a marketplace for verifiable machine intelligence, including a tournament-style competition for data scientists and an open marketplace for verified inferences.

AI agents or developers building smart contracts or applications will be able to select streams of inferences from a wide variety of proven, community-vetted machine learning models, thereby reducing Oracle risk and enabling them to react to trusted streams of inferences.

Accountability Starts with Web3

The inference economy combines Web3 validation staking, streaming payments, and zero-knowledge proofs to enhance accountability. This approach ensures proof of integrity and protects modelers’ models, making trustless systems possible.

This matters because holding AI accountable is a critical challenge. By integrating Web3 design philosophies, the inference economy offers a practical solution to the existential issue posed by AI. This marketplace of ML models generates higher-performing and more verifiable inferences, benefiting a wide range of industries.

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