MarketPulse's Multi-Level Reliability Enhancement Program
The project reduces the uncertainty of AI analysis through technical design:
- double verification of source: The system prioritizes authoritative media content configured in TRUSTED_SOURCES and explicitly labels source reliability metrics in the analysis report
- Quantitative confidence index: Gemini AI generates a percentage confidence score for each suggestion, and the user can set a threshold to filter out low-confidence suggestions (requires modification of the analysis module).
- Fact-checking mechanisms: The original news link is pushed along with the analysis report, so that users can easily check it back, and the Bark push example includes a "view original" button.
Optimization recommendations: 1) Strictly qualify TRUSTED_SOURCES in config.py; 2) Regularly assess the quality of AI suggestions via daemon.log; 3) Combine with Finnhub's company fundamentals data for cross-validation. For critical decisions, it is still recommended to manually review the consistency of AI suggestions with the original news.
This answer comes from the articleMarketPulse: a service that pushes AI analytics on financial news in real timeThe
May not be reproduced without permission:AI productivity tools " How to Improve the AI Illusion Problem in Financial News Analysis to Increase Recommendation Reliability?
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