Transparency Standards

Transparency Standards

If we want journalists, researchers, and industry professionals to cite our work, they need to be able to check it. Everything we publish — methods, limitations, potential biases — is documented and open to scrutiny.

Core Principles

1. Methodological Openness

Every published finding includes a methodology section explaining:

• How data was collected
• What analytical methods were applied
• What assumptions were made
• What limitations exist
• What confidence level we assign

2. Reproducibility

We aim to make our findings reproducible by:

• Publishing detailed methodology documentation
• Providing access to anonymized datasets where possible
• Describing analytical pipelines in sufficient detail for independent replication
• Supporting academic researchers with data access requests

3. Error Acknowledgment

When we make mistakes, we:

• Publish corrections prominently
• Explain what went wrong
• Describe what we've done to prevent recurrence
• Maintain a public correction log

4. Conflict of Interest Disclosure

• AiSlopData is powered by Mobian intelligence capabilities
• Mobian provides the underlying technology and research infrastructure
• Editorial decisions are made independently based on data and methodology
• Any findings that may intersect with Mobian's commercial interests are disclosed

5. Funding Transparency

We disclose:

• Our relationship with Mobian as our technology and research partner
• Any external research funding or partnerships
• No pay-for-play research or sponsored findings

Data Access

Public Datasets

We publish curated datasets at regular intervals, including:

• Monthly AI Slop Index data
• Platform saturation estimates
• Taxonomy classification samples
• Benchmark detection datasets

Research Access

Academic researchers can request expanded data access for:

• Peer-reviewed research projects
• Thesis and dissertation work
• Government and regulatory analysis
• Journalism with editorial oversight

API Access

We plan to provide API access to:

• Current and historical AI Slop Index values
• Platform-level saturation estimates
• Trend data and time series

Citation Standards

We encourage citation of our work and provide:

• Standard citation formats for all publications
• DOI assignment for major reports
• BibTeX entries for academic use
• Clear version numbers for all datasets

Feedback and Challenge

We welcome:

• Methodological critiques
• Alternative interpretations of our data
• Reports of potential errors
• Suggestions for improvement

Contact: research@aislopdata.org