Responsible AI for Market Research

What this resource does

Financial AI should organize evidence and uncertainty rather than imitate certainty. A model output is a derived research artifact whose usefulness depends on source identity, data cut-off, methodology version, and limitations.

ShareKeyX separates observed data, calculated metrics, retrieved statements, model inference, and editorial explanation. Unsupported performance claims, hidden source substitution, and personalized recommendations fall outside this standard.

Methodology

1. Attach company, security, source, period, and timestamp identity to every material input.
2. Preserve model, prompt, methodology, and schema versions for reproducibility.
3. Require citations, counterevidence, missing-data state, uncertainty, and scenario conditions.
4. Evaluate factual grounding, calibration, completeness, stability, and user-facing disclosures.

How to interpret it

A confident answer is not necessarily a reliable answer. Confidence should reflect evidence quality and model calibration, not writing style.

Human review remains necessary for public financial claims, methodology changes, material corrections, and content that could be mistaken for personalized advice.

Limitations and failure modes

• Models can hallucinate, omit, or misinterpret evidence.
• Retrieved sources can be stale, incomplete, or contradictory.
• Evaluation sets cannot represent every future market condition.
• AI output does not remove the need for independent verification.

Research workflow

1. Ingest and validate sources.
2. Generate a versioned draft.
3. Run evidence and contradiction checks.
4. Publish only with disclosure and review status.

Questions and answers