Project 4: “Deep research This Answer” Button

Expanded Problem Statement

Financial analysts using AI often get confident-sounding answers that might be wrong or lack justification. The current tools rarely show how they arrived at an answer, making it hard to trust the output. Pain points include:

• Low Transparency: Analysts can’t see the reasoning or source behind an AI’s recommendation, breeding mistrust.

• No Easy Way to Question: If something looks off, users have to manually cross-check or re-prompt AI for explanation, which is time-consuming.

• Compliance Concerns: In finance, every recommendation might need an audit trail. A black-box answer is often a non-starter for serious decisions.

Feature Description

The “Deep research This Answer” feature introduces a feedback and transparency mode:

• When clicked, the AI reveals a step-by-step reasoning for its last answer, almost like showing its “workings” or thought process. For example, if it suggested “Buy Stock X,” it might show bullet points: “1. Stock X has 20% revenue growth… 2. It’s undervalued vs peers by 15%... (source)… 3. Analyst sentiment is improving.”

• It might also display relevant data points or sources used to derive the answer splore.comsplore.com.

• The user can then give feedback: “This doesn’t fully explain the risk factors” or “Show me more on point 2,” which the AI will use to refine the explanation.

• Essentially, it’s a built-in Explainable AI (XAI) tool, fostering trust through clarity. It also encourages analysts to engage critically with AI output, rather than taking it at face value.

Deliverables

• UI Mockups showing an answer with a “Challenge” button, and the expanded view with an explanation and sources.

• Backend Logic Design for how the AI retrieves its reasoning or generates an explanation without hallucinating.

• User Testing Summary from analysts who tried challenging answers, capturing how it influenced their trust and speed.

• Guidelines/Policy for the extent of detail to show (to avoid information overload vs. enough transparency).

Skills to Manage

• AI Explainability Expertise: Knowledge of techniques to extract or generate rationale from AI models (or implementing chain-of-thought prompting safely).

• UX Design: Balancing detail and clarity in the explanation view; making it easy to digest the AI’s logic.

• Data Science/QA: Verifying that sources or numbers shown in explanations are correct and up-to-date (to maintain credibility).

Risks to Manage

• Information Overload: Showing every step could overwhelm users; we need the right level of summary vs detail.

• Potential for Error in Explanations: The AI might fabricate a rationale. Mitigate with rigorous testing and maybe a constraint that explanations only come from logged reasoning paths (if available).

Performance: Generating an explanation on demand might be slow. We may need to optimize by having the AI “think aloud” behind the scenes whenever it answers, so a rationale is ready if asked, or use lightweight models for explanation to keep it snappy.