Web3 And Blockchain to Tackle AI Fakes and Trust Issues

Artificial intelligence (AI) is changing the way we create and consume information – for better and worse. AI-powered analytics tools can extract insights from large collections of data, content tools can streamline and enhance the content creation process, and recommendation systems can deliver personalized recommendations of relevant content.

But AI tools can also be used to generate fake content, which raises questions of how we can trust what we see and hear on the Internet.

As people become increasingly skeptical about the content we consume, the next generation of the Internet — Web3 — suggests a decentralized, secure, and user-first ecosystem based on blockchain networks as a solution.

As Naveen Agnihotri, founder and chief executive officer (CEO) of content verification platform Trust App, told Techopedia:

“The immutable nature of blockchain offers untapped potential for scenarios where establishing trust is paramount.”

We explore how much trust we can have in a world of AI, blockchains, and everyone clamoring for your attention, trust, and money.

Table of Contents Table of Contents

1. Key Takeaways
2. How AI Can Benefit from Web3 Data Verification
3. How AI Can Enhance Web3 Decentralization
4. Challenges to effective Web3 traceability of AI content
5. The Bottom Line

Show Full Guide

Table of Contents

1. Key Takeaways
2. How AI Can Benefit from Web3 Data Verification
3. How AI Can Enhance Web3 Decentralization
Show Full Guide
4. Challenges to effective Web3 traceability of AI content
5. The Bottom Line

How AI Can Benefit from Web3 Data Verification

Agnihotri from Trust App told us:

“As we grapple with misinformation, deepfakes, and the influence of AI on content creation, the increasing importance of blockchain’s ability to establish a trustworthy source of information becomes clear.

“In an era where truth is under attack, blockchain could be a vital tool for restoring public trust – for instance, it could be used to track the origin and changes made to a news article, ensuring greater transparency for readers.”

“Over the next few years, we expect exciting new implementations of blockchain technology to solve a variety of problems, old and new.”

Web3 is an evolution of Web2 and not in competition or a parallel effort, according to Žiga Drev, Founder and Managing Director of Web3 infrastructure developer Trace Labs. This evolution is happening at the same time as the evolution of AI beyond computer labs to everyday use.

“Web3 has consistently demonstrated an immense propensity to grow, with its user base showing an incredible resistance despite sporadic boom and bust cycles.

“The quickly growing and loyal user base creates an ‘offer’ Web2 businesses simply cannot refuse and are therefore increasingly compelled to take into consideration when building new services.

“The capabilities of Web3 are permeating the Web2 world and are about to receive a huge boost with the appearance of AI that has the potential to become the biggest user of Web3 technology.”

AI algorithms rely on large datasets for training, and Web3 enables the creation of decentralized data marketplaces where users can securely buy, sell, and share data.

Add blockchain to the mix, and these marketplaces can provide access to diverse datasets while maintaining data privacy, integrity, and ownership rights.

Drev added: “AI has challenges of hallucinations, bias, and data ownership claims which are stifling its growth and the potential for truly mainstream adoption. Web3 technologies can enable AI solutions where provided information has provenance and access to data is managed by its owners.

“This is the way the new AI-based economy can be created on an even playing field, including individuals, organizations, and businesses across the globe rather than restricting it to a select few tech companies.”

Blockchain records can be an effective solution to tackle the challenge of AI-generated fake content and deter malicious use of AI algorithms, as they make it difficult to manipulate or falsify information.

As an example:

• A large language model (LLM) can integrate with a public blockchain via a bridge to create a tamper-proof and transparent record of input data and the output the LLM generates.
• The blockchain bridge can securely transmit data from the AI model to the blockchain network securely and efficiently.
• Each record on the blockchain would contain information about the input, the AI-generated output, and a timestamp so that the origin of the content is traceable and any changes are recorded on an immutable ledger.
• This facilitates content verification and helps to identify if, how, and when a piece of content has been manipulated.

How AI Can Enhance Web3 Decentralization

There are reciprocal benefits for both blockchain and AI in compensating for the shortcomings of each and extending their capabilities.

AI models can be trained in distributed networks using decentralized computer systems and data storage — federated systems train models on individual nodes or devices and share the aggregated data among them. This reduces reliance on centralized entities and increases privacy while providing diverse data sources to enrich model training.

AI also has a key role to play in data analytics in Web3 by processing and analyzing the large volumes of data that decentralized platforms, applications, and services generate. AI models can uncover insights quickly and efficiently and identify opportunities for optimization, contributing to the development of Web3 technologies.

Smart contracts on blockchain platforms such as Ethereum can facilitate AI services, allowing users to engage in automated transactions based on predefined conditions. For example, users can deploy smart contracts to request AI-based data analysis or prediction services and automatically pay for the results upon verification.

Drev added:

“The relationship between AI and Web3 is one of mutual need and requirement. Without Web3, AI solutions will run into challenges of lack of data integrity, intellectual property infringements, and potentially even the existential threat of AI model collapse.

“Once AI-generated data is the majority on the Internet, AI models can start to decrease in their abilities due to a lack of data variety.”

“An opposite scenario allows AI to access data with known provenance, compensates data owners for using their data, creates AI-generated products with lineage to original inputs, and ensures that there is a steady influx of real-world data ensuring AI models progress.”

Challenges to effective Web3 traceability of AI content

Decentralized blockchain ledgers have the potential to create traceability systems, but it is unlikely to be a panacea for the issues with AI-generated content. There are several obstacles that need to be addressed:

• Connecting LLMs to public blockchain networks may require significant infrastructure development and technical expertise.
• Recording all the input and output data on a blockchain would require increasingly large volumes of data to be stored on-chain. This could drive up storage costs, increase resource consumption, and slow down transaction processing. This, in turn, would limit the blockchain’s scalability, affecting its performance and usability.
• In addition, recording every input and output into an AI model is likely to result in the permanent storage of large volumes of unnecessary data, such as casual conversations or redundant prompts.
• Connecting AI models to public blockchains could raise legal compliance issues. For instance, storing data could raise issues of user consent and access to data under data protection laws like the European Union’s General Data Protection Regulation (GDPR).

The Bottom Line

The trustless nature of blockchain records could provide solutions to the question of how to establish trustworthy sources of information as AI-generated content proliferates. Public blockchains can track the origin of a piece of content and identify any changes, ensuring transparency.

Decentralized networks can train AI models, reducing centralization and enhancing privacy while providing diverse sources of training data. In turn, AI models can process large volumes of data from decentralized applications, providing insights that can help developers enhance Web3-based services.

However, developers will need to overcome integration and scalability challenges to ensure that blockchain and AI systems can work together efficiently while complying with data protection and privacy laws.