AI Transparency Report

Our Approach to AI Transparency

JobMog.ai believes in full transparency about how our AI systems operate. We disclose the data sources powering our career intelligence, the methodology behind our AI impact scores, the limitations of our AI recommendations, and the human oversight processes in place.

AI Bias Monitoring

We actively monitor our AI systems for bias across multiple dimensions including historical bias, language bias, representation bias, credential bias, geographic bias, and emerging bias patterns. Each category is evaluated against defined severity thresholds to ensure our evaluations remain fair and job-relevant.

Our bias monitoring framework covers: regular audits of AI outcomes by candidate group, accuracy tracking for scoring consistency, model retraining based on identified disparities, and a continuous improvement cycle informed by real-world usage data.

Data Sources

Our AI impact scores and career intelligence draw from authoritative sources including the U.S. Bureau of Labor Statistics (BLS), the O*NET occupational database, published AI research and industry reports, and community professional input. These sources are continuously refreshed to reflect current market conditions.

How AI Impact Scores Are Calculated

Our AI impact scores (1–10) are calculated by analyzing: task automation potential for each profession, skill displacement trends derived from labor market data, industry adoption rates of AI tools, and historical precedent from comparable technological transitions. A higher score indicates greater potential for AI-driven change in that profession's task composition.

Limitations

AI can make errors. A known class of errors is generally described as "hallucinations." Not all skills can be fully assessed automatically, and soft skills can be subjective. Our AI outputs are designed to support — not replace — human judgment, and all recommendations should be interpreted with appropriate context. We disclose uncertainty where it exists.