Editorial Policy

Last updated: February 22, 2026

1. Our Editorial Standards

Logo Analyzer is committed to accuracy, scientific rigor, and transparency in everything we publish. Our logo analysis platform produces measurable, reproducible results using computer vision algorithms and peer-reviewed research frameworks — not subjective AI opinions. We hold our blog content, methodology documentation, and marketing materials to the same standard: every claim must be supported by data, research, or clearly labeled as editorial perspective.

We distinguish between three types of content on our platform:

  • Computed metrics — directly measured by computer vision algorithms (e.g., contrast ratios, Shannon entropy, edge density)
  • Predicted metrics — generated by validated models (e.g., DeepGaze IIE saliency predictions, CLIP semantic classification)
  • AI-interpreted insights — generated by large language models informed by the measured data and research frameworks

Each metric on our results page is labeled with its provenance type so users understand exactly how each data point was produced.

2. Content Creation Process

Our blog content is created using an AI-assisted workflow. We use Google's Gemini language model to generate initial drafts based on detailed topic outlines, keyword targets, and internal linking requirements. Every AI-generated draft is reviewed for factual accuracy, scientific validity, and alignment with our editorial standards before publication.

Our content creation pipeline:

  1. Topic selection from a curated editorial calendar aligned with our areas of expertise
  2. AI-assisted draft generation with specific instructions for accuracy and citation requirements
  3. Quality validation: minimum word count, section structure, and internal link verification
  4. Hero image generation using Google's Imagen model, styled to match our brand guidelines
  5. Publication with proper metadata, schema markup, and author attribution

We believe in transparency about our use of AI tools. We do not present AI-generated content as human-written without disclosure.

3. AI Methodology Transparency

Our logo analysis pipeline combines multiple measurement and modeling stages. We are transparent about what each stage does and its limitations:

Computer Vision Measurements

We perform 20+ direct measurements using established CV algorithms: WCAG 2.1 contrast ratios, Shannon entropy for visual complexity, edge density analysis, color space extraction (RGB, HSL, LAB), and geometric symmetry calculations. These are deterministic — the same image always produces the same measurements.

Colorblind Simulation

We simulate 4 color vision deficiency conditions (Protanopia, Deuteranopia, Tritanopia, Achromatopsia) using the Brettel et al. (1997) and Viénot et al. (1999) simulation matrices. These are peer-reviewed, widely adopted methods for simulating how individuals with color vision deficiencies perceive images.

Image Quality Assessment

We use three no-reference IQA algorithms — NIQE (Natural Image Quality Evaluator), BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator), and MUSIQ (Multi-Scale Image Quality Transformer) — to assess technical image quality without requiring a reference image.

Saliency and Attention Prediction

We use DeepGaze IIE, a deep neural network trained on human eye-tracking data (MIT1003 and other datasets), to predict where viewers are most likely to look. These are predictions, not actual eye-tracking measurements from human participants.

AI Interpretation Layer

We use Google's Gemini models to interpret the measured data through the lens of published research frameworks, including Aaker's Brand Personality Model (1997), Palmer and Schloss's ecological valence theory of color preference (2010), and Gestalt principles of visual perception. The AI generates narrative insights, recommendations, and persona-based evaluations. These interpretations are informed by the measured data but are not themselves direct measurements.

4. Source Verification & Citations

We cite peer-reviewed academic research throughout our platform and blog content. Our key research sources include:

  • Aaker, J. L. (1997) — "Dimensions of Brand Personality," Journal of Marketing Research. Foundation for our brand archetype and personality analysis.
  • Palmer, S. E. & Schloss, K. B. (2010) — "An Ecological Valence Theory of Human Color Preference," PNAS. Framework for our color preference modeling.
  • Brettel, H. et al. (1997) — "Computerized Simulation of Color Appearance for Dichromats," JOSA A. Basis for our colorblind simulation matrices.
  • Viénot, F. et al. (1999) — "Digital Video Colourmaps for Checking the Legibility of Displays by Dichromats," Color Research & Application. Additional colorblind simulation reference.
  • Linardatos, P. et al. (2020) — DeepGaze IIE saliency prediction model, trained on MIT1003 human eye-tracking dataset.
  • WCAG 2.1 (W3C, 2018) — Web Content Accessibility Guidelines for contrast ratio and accessibility standards.

When we reference statistics or research findings in our blog content, we include inline citations in [Author, Year] format. If a specific claim cannot be attributed to a verifiable source, we label it as editorial analysis or AI interpretation.

5. Corrections & Updates Policy

We take accuracy seriously. If we discover an error in any published content — whether in our blog, methodology documentation, or analysis results — we follow this process:

  1. Acknowledge the error and assess its impact
  2. Correct the content with accurate information
  3. Update the "Last updated" date (displayed on all articles and reflected in our Article schema dateModified property)
  4. For significant corrections, add an editor's note explaining what was changed and why

We monitor content freshness and flag articles that have not been reviewed or updated within 90 days for editorial review.

6. Editorial Independence

Our analysis results are generated algorithmically. No paid placement, sponsorship, or business relationship influences the scores, metrics, or recommendations produced by our analysis pipeline. When we publish comparison content (e.g., "Logo Analyzer vs. [Competitor]"), we strive for factual accuracy based on publicly available feature information.

Our blog content is independently produced and is not influenced by advertisers, sponsors, or business partnerships. We do not accept paid guest posts or sponsored content without clear disclosure.

7. Contact

If you believe any content on our platform contains an error, misrepresentation, or outdated information, please contact us at [email protected]. We review all editorial feedback and respond within 5 business days.