QA.json

An alternative approach to AIs.txt, proposed by Robert Reich

Introduction

The proposed standard provides a mechanism for transforming website content into question-answer (QA) pairs. It details a data format, including general dataset information, specific website details, and the QA pairs, each accompanied by related metadata.

Key Advantages

1. Less data, faster. The big problem for AI reading the web is the sheer amount it has to read, parse, and understand. Large websites run into token limits and slow processing. Turning a site into structured QA pairs gives a short, meaningful summary of it. The AI doesn't have to read the whole document, only the distilled parts that matter, which makes it a lot faster.
2. Accessibility (a11y). QA pairs can work alongside meta tag descriptions to make web content more accessible. Content in question-answer form is easier for assistive technologies (like screen readers) to parse, so people with disabilities can get to the information more easily.
3. More control for publishers. Robots.txt mostly tells search engine crawlers which parts of a site not to crawl. It says little about how the content is understood and used. Here, publishers write the QA pairs themselves, which is like giving the AI more detailed instructions about the content. They decide which parts stand out, how they're read, and how they're shown to the end user. That gives publishers back some say, and helps their content get used the way they meant it.
4. Legal grounding. Robots.txt has no legal force. This format could be structured to carry some, which would give website owners more protection over how their content is used.

Motivation

The goal is to help AI pull meaningful information out of websites faster and better, getting around token limits and information overload. It also gives some control back to the publisher, much like an RSS feed or sitemap.xml does. And the structured format could also help build site-specific versions of AI models.

High-Level API

The data format is JSON with these main objects.

• datasetInfo: Provides general information about the dataset, such as its source, collection method, privacy-preserving technique, data balance, legal considerations, and contact info.
• website: Contains specific details about each website, such as URL, crawl frequency, suggested crawl pages, compression techniques, privacy and legal considerations, compensation, and content.
• content: An array under website that contains QA pairs each with its question, answer, keywords, rank, votes, metadata (like date added, source, language), augmented data, and data balance.

Detailed Design

The QA pairs are expected to be AI-generated and periodically updated. The AI crawler should be designed to interpret the content and form suitable QA pairs. Alongside these pairs, there will also be a mechanism for ranking the quality of the teams, which is expected to help improve the quality of the dataset overall.

Backwards Compatibility and Interoperability

No known backward compatibility or interoperability issues at this stage.

Security, Privacy, and Legal Considerations

While robots.txt files currently have no legal binding, this proposal suggests structuring the Terms of Service (TOS) to provide some legal grounding for these files. This would respect the website's privacy and legal considerations outlined in the website object.

Open Issues and Questions

• How will the AI accurately generate QA pairs?
• How will the system handle potential inaccuracies in the generated QA pairs?
• How to formulate the TOS for the legal grounding of the files?
• How to handle possible disagreements or disputes regarding the content or the ranking of the QA pairs?