7.3.2. Feedback That Leads to Real Revision

Feedback That Leads to Real Revision¶

Most AI systems offer a way to submit feedback, a flag button, a user survey, or a complaint email. But these mechanisms are often symbolic. Inputs fall into unstructured inboxes, disconnected from the teams who maintain the model or the governance structures meant to correct it.

A feedback system that accepts input but has no obligation to respond is not a governance tool, it’s a distraction.

Real-World Breakdown: Air Canada’s Chatbot Misinformation Case¶

As discussed earlier in Section 5.2, Air Canada’s customer-facing chatbot confidently fabricated a bereavement fare refund policy in 2022. A traveler relied on that information, was denied compensation, and successfully sued the airline in 2024. The tribunal ruled that Air Canada was legally accountable for the system’s output, even if the chatbot was autonomous¹.

Post-incident reports revealed that multiple complaints had already been logged, but were siloed, never routed to policy, legal, or oversight teams. Signals existed, but the system treated them as noise.

If the feedback had triggered review earlier, a lawsuit might have been avoided. But the escalation chain never began.

From Passive Feedback to Structured Oversight¶

To ensure that feedback improves the system, not just documents failure, organizations must implement governance-aware pipelines that connect user input to action. This involves:

Assigning specific human reviewers with authority
Logging not just the complaint, but the action taken
Periodically surfacing aggregate feedback to model maintenance teams
Incorporating that signal into model retraining, policy revisions, and compliance reporting

Table 57: Feedback System Maturity Levels

Maturity Level	Description	Governance Effect
Level 0 – Passive	Feedback accepted but not tracked or reviewed	Symbolic, no real governance
Level 1 – Monitored	Inputs are reviewed periodically, no formal action tracking	Reactive, limited accountability
Level 2 – Integrated	Complaints linked to oversight roles and audit logs	Traceable response, selective revisions
Level 3 – Adaptive	Feedback drives model updates, policy changes, and risk reevaluation	Continuous trust alignment

Systems that mature toward Level 3 embody what ISO/IEC 24028:2020 defines as operational trustworthiness: not just a model’s reliability, but its responsiveness to evolving conditions and external input². This ISO guidance urges that feedback should trigger auditable intervention, and be stored in structured logs linked to risk response workflows.

Similarly, ISO/IEC 42001:2023 mandates that organizations managing AI systems must establish mechanisms for receiving, documenting, and acting upon stakeholder complaints, especially when they touch on risk, legality, or fairness.

Standards now treat feedback as more than a user courtesy. It’s a governance signal that must be traceable.

Governance Framework Spotlight: DAIR’s Escalation Ladder¶

One notable technical framework for feedback integration comes from the Distributed AI Research (DAIR) Institute, which developed the Escalation Ladder model to route user complaints through increasingly formal layers of review⁴:

DAIR Escalation Ladder

Level	Response Type	Stakeholder Involved
Tier 1	Acknowledgment only	Frontline support / bot
Tier 2	Internal triage	AI ethics team
Tier 3	Root-cause analysis	Engineering and policy
Tier 4	Governance escalation	C-suite or legal response
Tier 5	Regulator notification	External authorities

This structure reinforces a key principle of the EU AI Act (Article 71): serious incidents and systemic risks identified through post-market monitoring must be escalated to competent authorities, not just noted internally³.

Escalation is not a sign of failure. It’s the architecture of learning.

By using structured escalation protocols and aligning with ISO and EU requirements, organizations can turn informal feedback into formal system resilience.

“Feedback isn’t governance unless it changes something.”

Thinkbox

“Users shouldn’t have to go to court to fix your model.”
The Air Canada chatbot case exposed a governance vacuum, not in model design, but in feedback handling. International standards like ISO 24028 and regulatory tools like the EU AI Act now require systems to treat user complaints as governance inputs, not afterthoughts¹²³.

TRAI Challenges: Feedback Failure Governance Map

Revisit: The Air Canada chatbot case in Section 7.3.2.

🧩 Tasks:
- Use the Feedback System Maturity Table to assess what level their governance represented.
- Identify two failure points in the escalation ladder (e.g., no review routing, no authority to revise output).
- Recommend two structural upgrades (e.g., formal complaint triage, reviewer feedback loop into retraining).

For example: Add Tier 2-3 internal ethics team routing, track reviewer interventions for compliance audits.

Bibliography¶

CTV News. (2024, February 16). Air Canada ordered to refund passenger after chatbot provided false information. https://www.ctvnews.ca/canada/air-canada-ordered-to-refund-passenger-after-chatbot-provided-false-information-1.6773233 ↩↩
ISO/IEC. (2020). ISO/IEC TR 24028: Trustworthiness in Artificial Intelligence. https://www.iso.org/standard/77608.html ↩↩
European Union. (2024). EU Artificial Intelligence Act – Final Text (Article 71). https://artificialintelligenceact.eu/the-act ↩↩
DAIR Institute. (2023). Feedback, Escalation, and Community Review in AI Systems. https://www.dair-institute.org/reports/escalation-ladder ↩