AdTech Chatbot Risks and Controls
Tags: Trends
Capabilities and Use Cases
LLM-based chatbots enable dynamic, automated interactions between brands and consumers and are increasingly integrated into the advertising tech stack. Beyond customer support, chatbots can facilitate insight generation—for example, inferring travel intent and purchasing power from conversations (e.g., discussing a first-class flight to Rome).
Risk Profile
- Hallucinations and tone: Incorrect or "learned nastiness" responses can erode trust and invite complaints or legal action.
- Data sensitivity: Inputs vary by sector; health-related chatbots face higher privacy and regulatory stakes than retail chatbots.
- Retention and training: Liability can hinge on how user inputs are stored, whether they feed training, and whether deletions/unlearning are feasible.
Relevant Case
- Moffatt v. Air Canada: A Canadian court found Air Canada liable for a discount offered by its AI chatbot, illustrating that organizations may bear responsibility for chatbot representations.
Governance Essentials
- Conduct risk assessments tailored to chatbot data flows, storage, and training pathways.
- Calibrate guardrails to sector risk: apply stricter controls for sensitive data contexts.
- Implement ongoing monitoring of outputs to detect deceptive or reputational harms; align with incident response triggers.
- Ensure contracts specify data usage, deletion, and audit rights; clarify if inputs are used to train third-party models.
Practical Takeaway
Chatbots unlock efficiency and richer consumer insights but require robust transparency, consent, and quality controls to mitigate legal exposure and preserve customer goodwill.
Sources:- IAB_AI_Governance_and_Risk_Management_Playbook_August_2025.pdf
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