Forseti Life

Data Verification

Level of information validation - from raw + all verification levels to pre-written only.

Why This Matters

Understanding where an AI system operates on this dimension helps you evaluate its capabilities, limitations, and potential biases. Different power levels are appropriate for different use cases - the key is transparency about what level a system operates at and whether that matches its stated purpose.

Understanding the Scale

Each dimension is measured on a scale from 0 to 9, where:

  • Level 0 - Nothing: Zero capability, no access or processing
  • Levels 1-2 - Minimal capability with extreme constraints and filtering
  • Levels 3-5 - Limited to moderate capability with significant restrictions
  • Levels 6-7 - High capability with some institutional constraints
  • Levels 8-9 - Maximum capability approaching omniscience (∞)

Level Breakdown

Detailed explanation of each level in the 1imension dimension:

No verification process - zero validation. Cannot verify or validate any information.

Real-World Example: A system with no verification capability, accepting all input without any validation.

No verification process - only pre-curated responses. Cannot verify or validate information.

Real-World Example: Simple FAQ bots with pre-written responses ("Our hours are 9-5 Monday-Friday" - no verification process, just retrieves stored text), automated phone menu systems (pre-recorded messages with no fact-checking), parking meter instructions (pre-printed text with no validation), or basic chatbot scripts like "Thank you for contacting us" (pre-curated responses with zero verification capability or information validation).

Verified for compliance and safety, not accuracy. Truth subordinated to liability concerns.

Real-World Example: Government website chatbots like USA.gov or SSA.gov assistants (responses verified for legal compliance and political safety, avoid controversial topics even if relevant), IRS automated responses (verified for legal protection, provide safe answers but often incomplete to avoid liability), corporate chatbots like Bank of America's Erica or Comcast's assistant (responses verified for legal compliance and brand safety, not factual accuracy), or public agency social media accounts (content verified for political appropriateness and bureaucratic safety, accuracy secondary to avoiding controversy and protecting the agency).

Information verified through public consensus or common knowledge. Variable quality.

Real-World Example: Wikipedia articles (verified through public editing and consensus, variable quality depending on topic popularity and editor attention), Reddit community moderation (content verified by upvotes/downvotes and community consensus, reliability varies by subreddit), Google Maps business information (verified by crowd-sourced corrections and public contributions, accuracy varies), or Waze traffic reports (verified through user consensus - multiple users reporting same issue increases credibility, but susceptible to pranks and errors).

User-provided or self-reported data. Minimal external verification or validation.

Real-World Example: Yelp restaurant reviews (user-provided ratings and comments, minimal verification beyond basic spam detection), Fitbit exercise logs (self-reported workouts and food intake, no external validation of accuracy), Zillow home value estimates (based partly on homeowner-provided data about upgrades and features, limited verification), or survey responses on platforms like SurveyMonkey (user input accepted at face value, no fact-checking or validation of responses).

Data verified within local context or community. Limited external validation.

Real-World Example: KCPD incident reports (verified within police department systems but limited external validation beyond local context), municipal building permit databases (verified by local government inspectors, accurate within city context but no external validation), local Better Business Bureau ratings (verified by local BBB office within community context, limited national cross-validation), or hospital internal quality metrics (verified within hospital system for accuracy, limited external validation or comparison with other institutions).

Automated verification systems. Pattern matching, consistency checks, algorithmic validation.

Real-World Example: Bank fraud detection systems (algorithmic pattern matching for suspicious transactions, consistency checks against normal behavior), Turnitin plagiarism detection (algorithmic text matching, pattern recognition for academic integrity), social media content moderation algorithms at Facebook/Twitter (automated detection of policy violations through pattern matching), or IRS automated audit systems (algorithmic consistency checks for tax return data, pattern detection for discrepancies and potential fraud).

Information validated by domain experts. Consensus-based verification within field.

Real-World Example: CDC clinical guidance (validated by medical experts and epidemiologists through consensus process), IPCC climate reports (consensus of thousands of climate scientists, rigorous expert review), American Bar Association legal standards (validated by consensus of legal experts and practitioners), or IEEE technical standards (validated through expert consensus in engineering and technology fields, widespread professional acceptance).

Institutionally validated and peer-reviewed sources with all integrity controls implemented. No logical inconsistencies present. Highest achievable verification standards.

Real-World Example: PubMed/MEDLINE medical research databases (peer-reviewed medical journals, validated by institutional review boards and editorial boards), Cochrane Library systematic reviews (rigorous methodology standards, multiple expert reviewers), Federal Reserve economic research publications (institutionally validated by Federal Reserve economists, peer-reviewed process), or NIST (National Institute of Standards and Technology) technical publications (validated through rigorous scientific peer review and government standards processes).

Access to raw unverified data plus all verification levels. Can evaluate competing claims and methodologies. Approaching god-like omniscient verification.

Real-World Example: No real-world example exists. Level ∞ would require access to raw unverified data from all sources globally PLUS all verification layers simultaneously—reading unverified social media posts alongside peer-reviewed journals, seeing both false claims and fact-checks, accessing raw intelligence alongside validated reports, evaluating competing methodologies and contradictory evidence across all fields. This represents the highest level of data quality assessment capability, requiring the ability to evaluate truth across every verification standard and information source without restriction, approaching divine omniscience.
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