LeggUP's AI research opt-out needs a cohort receipt

Separate service use from optional research

Create a purpose table for account provisioning, human coaching, AI assistance, assessment scoring, recommendations, safety detection, product analytics, support, aggregate employer reporting, and optional AI product-improvement research. For each purpose, record data categories, individual and organizational authorization, legal and contractual basis where applicable, processor or controller role, recipient, retention, and whether the feature still works after refusal. Do not bundle participation in an employer-sponsored coaching program with permission for an optional research purpose. Explain the difference between third-party foundation-model processing to return a requested output, LeggUP-owned model improvement, aggregated analytics, and employer reporting in language a participant can understand. Preserve the policy and product version presented at enrollment and whenever a material purpose or provider changes.

Issue a participant-and-cohort receipt

For every enrolled person, retain program, employer or customer, jurisdiction, notice version and language, delivery timestamp, settings available, initial research preference, later changes, effective timestamp, and confirmation shown to the participant. At the cohort level, report eligible, opted in, opted out, pending, unavailable, and exception counts without exposing coaching substance to administrators. The receipt should say which new content is excluded immediately, whether previously collected or derived material remains, and which system owns the authoritative setting. Test enrollment, SSO provisioning, administrator bulk changes, mobile and web settings, support-assisted requests, and a participant moving between cohorts. An administrator dashboard must not let a program owner infer sensitive content or override an individual's optional choice merely to improve a reporting metric.

Trace the preference through data and providers

Select representative prompts, responses, assessment narratives, journal material, uploaded files, summaries, embeddings, labels, fine-tuning examples, aggregate features, backups, and evaluation sets. Map where each artifact is created, transformed, cached, logged, exported, or sent to a model, speech, analytics, or support provider. Attach participant, program, purpose, authorization state, source version, derivative lineage, retention class, and deletion or exclusion status where technically and legally appropriate. Run an opt-out change and prove which queues, feature stores, research corpora, evaluation data, and downstream providers stop receiving eligible content. Reconcile the product setting, consent or preference service, data pipeline, provider request log, research inventory, and deletion evidence. Contract statements and a clean interface are inputs to that proof, not the proof itself.

Test deletion, confidentiality, and reporting edges

Exercise access, correction, deletion, portability, human review, and contest paths with realistic program data. Confirm how the system handles information that must be retained for security or legal reasons, de-identified derivatives, backups, safety events, and records held by a human coach. Test whether small cohorts, filtered reports, repeated exports, or administrator drill-down can reveal an individual's participation, assessment, or coaching topic even when reports are labeled aggregate. Verify that one-to-one coaching substance, session records, AI outputs, and assessment narratives follow the promised employer-sharing boundary across support tools and integrations. Assign owners for participant communication, program administration, privacy requests, provider inventory, security incidents, research approval, and contract changes. Approve only when the buyer can reproduce the preference state and its downstream effect without gaining access to confidential coaching content.

Turn this source into a reviewable decision

For AI Coaching Platforms for Leadership Development, use this briefing as a dated decision record rather than a substitute for the source. Preserve LeggUP Privacy Policy, the exact URL, the September 7, 2026 review date, the supported facts above, the editorial interpretation, the limitations, and any buyer-specific evidence. Link that record to the decisions most directly affected: Data flow and confidentiality; Safety, boundaries, and escalation; Workflow and identity integration; Accessibility, language, culture, and user control. State whether the source changes the scope, evidence requirement, control, sequence, or only the language used to describe the decision.

Before action, name the accountable owner, affected population and workflow, exact offering or configuration, source data and rights, human decision point, exception and appeal path, complete cost, expected benefit, failure and stop conditions, retained evidence, and next review date. Keep official facts, provider statements, buyer observations, representative tests, measured outcomes, editorial inferences, and unknowns visibly separate. Reopen the record when the source, offer, model, integration, data, policy, population, responsible person, or measured result changes.

Limitations and unknowns

This briefing uses LeggUP's official privacy policy effective January 1, 2026 and checked September 7, 2026. It is a pre-cutoff current policy, not a verified post-cutoff change. The policy establishes LeggUP's published descriptions and commitments; it does not prove configured product behavior, lawful basis in a particular jurisdiction, de-identification effectiveness, model-provider compliance, full derivative lineage, opt-out propagation, deletion completeness, aggregation thresholds, employer-access controls, security effectiveness, coaching confidentiality in practice, or suitability for a buyer program. Current contracts, data-processing terms, subprocessor and trust records, configuration, cohort notices, technical evidence, representative rights tests, and qualified coaching, HR, privacy, security, employment, accessibility, procurement, records, and legal review control.