SlopeReady

AI coaching · 6 min read

How to audit an AI coach’s data, proposals, and plan changes

This checklist does not prove that an AI recommendation is accurate, medically safe, or right for you. It helps you inspect the data the app used, what was missing, the AI proposal, and the final state of the saved plan. When those elements are clear, it is easier to spot an error and keep control of your plan.

Three separate trays in front of a snowy mountain model: the first holds dark plan tiles and an empty space, the second holds blue proposal pieces, and the third has one piece secured by an orange confirmation clip while unused blue alternatives remain loose beside it.
On this page

Check how the app arrived at its answer. Which data were present, which were missing, what is an interpretation, and what is only a proposal? Then confirm that the saved plan changes only after a clear action from you.

A transparent interface does not make advice correct. It can make contradictions easier to notice and gives you a chance to reject a proposed change.

Inspect three separate layers

These layers are the conceptual model. The app should keep them visibly distinct.

1. Data used

Which training, recovery, goal, and plan data entered the request?

An app should not claim complete context while hiding its sources or gaps.

2. AI proposal

Which statements follow from data, which are interpretations, and which plan edit is only a suggestion?

A convincing explanation can still be wrong. An explanation is not evidence.

3. Saved plan

What exactly will change, and which action makes that change part of the stored plan?

A chat response should not silently overwrite the calendar.

The five-question check

Use it before subscribing or when you reassess an app after a major update.

  1. Can you inspect the data used?

    Look for the time window, workout summaries, goals, the part of the plan considered, and other information used for the answer.

  2. Are missing or stale data marked?

    Missing sleep, an unsynced workout, or a short history should not quietly become zero, normal, or a plausible estimate.

  3. Does the answer separate facts, interpretation, and proposal?

    The app should identify what was measured, what it inferred, and which other explanations remain possible.

  4. Can you see the exact change before it is saved?

    The date, session, type, and size of the edit should be visible. Agreeing with general prose is not the same as controlling a calendar.

  5. Can you verify the saved plan afterwards?

    Reopen the plan and confirm that it stayed unchanged or contains only the edit you explicitly applied.

Test the app without changing your real training plan

Use an existing context or one clearly intended for testing. You are checking the interface, not the quality of the suggested workout.

Open the visible context

Note the data types, time window, and part of the plan used for the request.

Find a gap you already know

Choose a day or workout with a missing value and check whether the app keeps that gap visible.

Request a small change

Confirm that a concrete edit appears as a proposal while the saved plan remains unchanged.

Stop before applying it

Dismiss or leave the proposal without approving it. Reopen the plan and verify that nothing changed.

Five warning signs

These summarize the main limits. Several together make the recommendation difficult to review.

  • The app claims to know your complete current state.
  • Missing measurements appear as zero, normal, or estimated without a clear label.
  • The explanation sounds precise but names neither the data used nor the uncertainty.
  • The proposal and the saved plan are not clearly separated.
  • Dates or sessions change without a visible, explicit action from you.

What the research actually supports

The NIST profile for generative AI covers documenting system knowledge limits and upstream data sources, maintaining human oversight, and comparing output with known facts. It is general risk guidance, not a ready-made interface test for a consumer fitness app.

Buçinca and colleagues studied 199 people performing a food-related decision task, not ski training or a fitness app. The interface interventions reduced overreliance on AI recommendations but did not remove it. More effective interventions could also receive lower ratings for usability or trust and higher ratings for difficulty.

The five questions are SlopeReady’s editorial adaptation of those general principles. NIST has not tested or certified SlopeReady, and it does not validate this checklist or the accuracy of app recommendations.

A fully visible workflow can still produce advice that is wrong or unsuitable. This check improves your control over data and plan changes. It does not establish the quality of the recommendation.

Sources: NIST AI 600-1, Buçinca et al. (2021).

What you can inspect in SlopeReady

The “What was sent” view shows the context SlopeReady sends for that AI request, including available readiness and confidence values, missing inputs, recent workout summaries, the goal, and the part of the plan considered. It does not expose the model’s internal reasoning. Missing data remain missing.

An AI proposal is displayed separately from the saved plan. You can inspect the concrete edit and apply or dismiss it. Without an explicit Apply action, the plan remains unchanged.

With your permission, SlopeReady reads available Apple Health data and has read-only access. It does not record workouts, live heart rate, a GPS route, or a ski run. The visible context may be incomplete or stale, and AI responses may remain incomplete or wrong.

Sources: SlopeReady Privacy Policy, SlopeReady data sheet, How SlopeReady works, SlopeReady Terms.

Audit the next part of the system

Start with a clear question Define one concrete training question before comparing the data used with the answer. Choose a question An answer or a maintained plan Separate a one-off explanation from a plan that incorporates only approved changes. Understand the difference Confidence with missing data Separate a readiness result from the completeness of its inputs. Audit confidence Start with a short history See how an app should show uncertainty before enough data exist. Interpret a short history A stable plan without silent edits Check which dates may adapt and which should remain protected from automatic changes. Open the plan rules

Start by checking what data was sent.

SlopeReady shows the context sent with an AI request. Missing values remain identifiable, and a proposal changes your saved plan only after you explicitly apply it.

See how SlopeReady works

This checklist helps you inspect how an app works. It does not establish the medical safety, accuracy, or personal suitability of a recommendation. Do not base health, injury, or safety decisions on an AI response alone.