As more crypto firms flex their lobbying muscles in the U.K., Solana is among a cohort of Web3 research labs attending the Labour Party Annual Conference this week.
The U.K. arm of Solana Superteam, an ecosystem support network, hosted an event on Monday, Sept. 29, exploring how blockchain technology can be used to increase trust and transparency in AI.
As thousands of Labour members gather in Liverpool for the party’s four-day annual conference, the U.K.’s ruling party faces internal divisions that span the full spectrum of political debates.
While senior party officials are preoccupied with major policy announcements and a looming threat to Keir Starmer’s leadership, Solana’s side event explored AI risks and how blockchains can mitigate them.
The event was organized in collaboration with Labour Tech, a new thinktank established to shape the party’s technology policy. Other partners included Fabric Ventures, GSR, and Syntagma Labs.
With presentations from various experts and industry representatives, it explored ongoing tech policy debates and provided an opportunity for Labour members to learn about blockchain and AI.
Under existing, centralized AI frameworks, a lack of transparency, privacy concerns, and emerging malicious applications of the technology have contributed to a “trust crisis,” a Superteam U.K. report argued.
With their immutable ledgers and public verifiability, the report poses blockchains as an important solution to the AI trust crisis.
For instance, it pointed to Numbers Protocol as an example of a decentralized content provenance platform that could address intellectual property challenges in AI training.
Meanwhile, it highlighted the novel AI governance models that have been developed by projects like Autonolas and Fetch.AI.
The Solana report was optimistic about blockchain-powered provenance tracking, decentralized databases, and on-chain governance mechanisms. However, contributors were less convinced that decentralized networks could replace hosted Big Tech AI platforms like ChatGPT.
“Most interviewees expressed skepticism about decentralized AI’s viability as an alternative to centralized models,” the report noted.
Citing performance limitations and slower processing speed, platforms like BitTensor, “remain marginal” the report added, arguing that the choice of decentralized over centralized AI may be more ideological than practical.