Model selection assistant
comparing two models
previewing a models transcription
1
Understanding analysts current workflow and XAI best practices
Empathize
2
Identify the core problem
Define
3
Initial concept development
Ideate
4
Conduct usability tests
test
5
Refinement & High-Fi vibe coding
refine
Informal interviews with analysts (limited time & availability)
Understanding current model-selection and audio-analysis workflows
Explainable AI, trust calibration, and AI-assisted decision making
exploring current speech-to-text models and analysts workflow
Key finding
Layered explanations explore trust, transparency, and adaptability
One of the most important insights was the value of layered explanations framework for explainable AI. Instead of presenting all system logic at once, explanations can be structured to allow analysts to progressively access deeper levels of detail about a recommendation.
In this framework, high-level signals communicate what the system recommends, additional context explains why a model is appropriate for a specific audio scenario, and deeper layers reveal how the system arrived at that recommendation. This approach supports both quick decision-making and deeper investigation, giving analysts control over how much information they engage with while maintaining transparency and trust.
With the framework established, we began exploring how layered explanations might appear within the analyst workflow. We tested multiple ways information could be revealed across the recommendation experience.
Concept 1
List-based model recommendations
When users are working with a model, the system automatically suggests alternative models based on performance and community usage data, presented in a concise list format. This approach focuses on quick comparison and supports progressively deeper explanation levels as users engage further.
Concept 2
Visual comparison
The system visually compares the current model with top alternatives across key performance metrics, allowing users to quickly identify strengths and weaknesses. This concept emphasizes pattern recognition and supports comparison without relying solely on text.
Concept 3
Task-focused expansion
Based on the user’s current activity, the system suggests not only better-suited models but also potential next tasks. This approach supports forward-thinking exploration beyond the user’s initial query.
key insight
Trust was not tied to seniority, but to how supported analysts felt in understanding and validating model recommendations. Stakeholders found visualizations difficult to interpret and more cognitively demanding than anticipated. While informative, they required additional education and slowed quick decision-making.
surveying analysts
Trust was built with adaptability, support, and control
Insights from the survey directly informed the final design direction. Findings confirmed that trust was not tied to seniority, but to how supported analysts felt in understanding and validating model recommendations. Analysts consistently valued adaptability, community insight, and the ability to control explanation depth, which reinforced the need for a layered system that could support both quick decisions and deeper investigation.
Calibrating trust
[…] if there is user feedback to crowdsource […] wouldn't that always trump the system suggestions? Users are going to be more influenced by other users rather than a nebulous "system suggestion…"
Explanation Needs
Model description & tags are helpful but don't explain how audio is processed [...] would be helpful to have examples of original vs processed clip & what the model is NOT good at [...] too many options can make the selection difficult
Improving Tools
The ability to immediately jump to keywords and have a provided STT transcript is an incredibly valuable tool that would speed up my workflow considerably […] to filter through audio much more quickly than I am able to now.
solution
Supporting analyst decision-making through layered explanations
The first layer helps analysts quickly identify promising models based on their audio conditions and workflow requirements.
Recommendations are paired with concise reasoning and key performance indicators, allowing analysts to understand why a model surfaced without diving into technical details.
Once analysts identify a promising model, they can explore the factors influencing the recommendation.
This layer introduces richer contextual information.
This layer supports side-by-side exploration of model strengths, weaknesses, and tradeoffs.
Rather than identifying a single "best" model, the experience helps analysts determine which model best fits their current situation.
impact
Beyond a single recommendation; Building a 3-Level Explainability Framework
The project established a foundation for supporting analyst decision-making as model ecosystems continue to expand. Through layered explanations, the framework helps analysts move beyond accepting recommendations at face value and toward understanding the reasoning behind them. The resulting framework was documented for stakeholders as a foundation for continued development.
