Part of the Complete Guide
Governance for Sensory AI: Regulation, Ethics & the EU AI Act
AI in sensory science must be governed as carefully as any lab instrument. From the EU AI Act's risk classification to the ethics of optimization, here is the complete framework for responsible sensory AI.
By Dr. John Ennis, PhD — Aigora
Governance for Sensory AI: Treat Models Like Instruments
Sensory AI should be governed as carefully as any lab instrument:
Intended-Use Statement
Where the model may be trusted; where it may not.
Data Sheet
Sources, representativeness, known gaps; explicit handling of perceptual minorities (anosmias, super-tasters, trigeminal sensitivities).
Drift Monitors
Seasonality, supply-chain shifts, demographic changes.
Interpretability Pack
Drivers, localized effects, counterfactuals, error taxonomy.
Human Stop-Rules
Thresholds that automatically trigger panel/consumer re-checks.
This is not bureaucracy; it is how we keep models scientific and trustworthy.
The EU AI Act: Regulatory Implications for Sensory Science
The regulatory environment is now formalizing these principles. The EU AI Act, entering full implementation on August 2, 2026, introduces a four-tier risk classification system with direct consequences for sensory AI. Systems that use biometric data (including physiological responses to olfactory or gustatory stimuli) for identification or categorization fall into the “High-Risk” category, requiring pre-market conformity assessments, mandatory registration in the EU database, and continuous post-market monitoring. The forthcoming “Digital Omnibus” proposals will further clarify GDPR obligations for AI systems, making it clear that reliance on “legitimate interests” for processing biometric data faces heightened scrutiny.
Companies must now implement “AI Literacy” obligations for employees and design systems with human oversight as a core requirement. For sensory science practitioners, this means that biometric-informed research tools—EEG headsets at retail counters, GSR during panel sessions, heart-rate-based preference models—must be governed under a clear regulatory framework. Notably, the AI Act also permits the processing of special-category personal data to uncover and rectify bias, provided strict safeguards are in place—a provision that supports efforts to make sensory models more inclusive and representative of diverse populations.
EU AI Act Risk Classification for Sensory Science
Unacceptable Risk
Subliminal manipulation of consumer behavior, social scoring based on sensory preferences. Banned outright.
High Risk
Biometric-informed sensory systems (EEG, GSR, heart-rate preference models). Requires conformity assessments, registration, and continuous monitoring.
Limited Risk
AI chatbots for consumer feedback (e.g., ScentChat), synthetic content generation. Transparency obligations apply.
Minimal Risk
Standard predictive models for formulation screening, flavor prediction without biometric data. No specific obligations beyond general AI literacy.
Persistent AI Agents and Pragmatic Governance
The pragmatic personhood framework extends naturally into governance. As AI agents become persistent participants in formulation pipelines—maintaining state, remembering past evaluations, and planning across product-development horizons—the governance toolkit must evolve beyond treating them as passive instruments. Assigning clear “bundles” of obligations (mandate adherence, transparency, systemic non-harm) to these agents, and establishing human stop-rules that can “arrest” an agent's operations when thresholds are breached, provides a practical mechanism for accountability that does not require resolving intractable debates about machine consciousness.
“This is not bureaucracy; it is how we keep models scientific and trustworthy.”
— Dr. John Ennis, “From Measurement to Meaning”
Responsibilities and Risks: Design for Flourishing, Not Just Acceptance
Optimization has a dark side. If we optimize only for short-run liking, we risk hyper-palatable cul-de-sacs and homogenization of taste, eroding cultural variety. Sensory scientists should:
Broaden Objectives
Include durability, nutrition, sustainability, and cultural relevance alongside acceptance.
Protect Attention and Well-Being
Personalization should respect thresholds and avoid sensory overload.
Preserve Provenance and Ritual
The stories around products shape perception; steward them responsibly.
Generative Ghosts and Synthetic Provenance
The rise of “Generative Ghosts”—AI agents designed to represent deceased individuals using LLMs and multimodal synthesis—sharpens this responsibility in unexpected ways. Research by Morris and Brubaker (2025) identifies a design space in which such agents can generate novel content, remember past interactions, and plan future actions, blurring the boundary between archival legacy and dynamic evolution.
For sensory science, the parallel is instructive: when AI systems generate “synthetic provenance” for products—fabricated origin stories, AI-authored tasting notes presented as human expertise, or digital replicas of heritage processes—they risk what hauntological theory calls a “false ontology”: collapsing a complex relationship between artifact and history into a misleading narrative of authenticity.
The “second death” problem identified in generative-ghost research—where the platform hosting a digital legacy goes bankrupt, leaving survivors to grieve anew—has a commercial analogue: brands that build consumer rituals around AI-generated provenance narratives are vulnerable to a “second loss” of trust if the synthetic origins are exposed. Sensory scientists, as stewards of the link between cues and meaning, have a responsibility to advocate for “thanatosensitive” design principles: clear consent, transparent sourcing of narratives, and the humility to distinguish between what the data show and what the model imagines.
Sensory scientists, as stewards of the link between cues and meaning, have a responsibility to advocate for “thanatosensitive” design principles: clear consent, transparent sourcing of narratives, and the humility to distinguish between what the data show and what the model imagines.
Champion Open Ontologies
Champion open ontologies. Shared descriptors and data hygiene will outcompete proprietary vagueness.
LeCun's argument for open-source AI reinforces this point from a structural angle: allowing any single commercial entity to control the “repository of all human knowledge” encoded in foundation models would be a strategic and democratic mistake. For our field, this translates into championing open sensory ontologies, shared descriptor lexicons, and transparent data practices. Open-source world models—trained on diverse sensory data and accessible to researchers across cultures—are more likely to preserve the rich variety of human taste than proprietary systems optimized for a narrow demographic. Openness accelerates progress, fosters diversity, and serves as a hedge against the homogenization of taste that closed systems risk producing.
Key Principles for Responsible Sensory AI
Govern AI models with the same rigor as lab instruments
Classify systems by EU AI Act risk tiers and comply accordingly
Establish human stop-rules that can arrest agent operations
Broaden optimization beyond liking to include nutrition, sustainability, and culture
Champion open ontologies and transparent data practices
Distinguish between data-grounded insight and model-generated narrative
Frequently Asked Questions
How does the EU AI Act affect sensory science?
The EU AI Act, entering full implementation on August 2, 2026, introduces a four-tier risk classification system. Sensory AI systems that use biometric data—including physiological responses to olfactory or gustatory stimuli measured via EEG, galvanic skin response, or heart-rate variability—fall into the "High-Risk" category. This requires pre-market conformity assessments, mandatory registration in the EU database, continuous post-market monitoring, AI Literacy obligations for employees, and human oversight as a core design requirement.
What governance framework should sensory AI follow?
Sensory AI should be governed as carefully as any lab instrument. A robust framework includes five components: (1) an intended-use statement defining where the model may and may not be trusted; (2) a data sheet documenting sources, representativeness, and known gaps including perceptual minorities; (3) drift monitors for seasonality, supply-chain shifts, and demographic changes; (4) an interpretability pack providing drivers, localized effects, counterfactuals, and error taxonomy; and (5) human stop-rules—thresholds that automatically trigger panel or consumer re-checks when model confidence drops.
What are the risks of AI in food optimization?
Optimizing only for short-run liking risks creating hyper-palatable cul-de-sacs—products engineered to maximize immediate hedonic scores while eroding nutritional quality, cultural variety, and long-term consumer well-being. This can lead to the homogenization of taste, where AI-driven optimization converges on a narrow sensory profile that appeals to the broadest demographic but flattens the rich diversity of food culture. Sensory scientists must broaden objectives to include durability, nutrition, sustainability, and cultural relevance alongside acceptance.
What is synthetic provenance and why is it a concern?
Synthetic provenance occurs when AI systems generate fabricated origin stories, AI-authored tasting notes presented as human expertise, or digital replicas of heritage processes. This risks what theorists call a "false ontology"—collapsing the complex relationship between a product and its history into a misleading narrative of authenticity. Brands that build consumer rituals around AI-generated provenance narratives are vulnerable to a catastrophic loss of trust if the synthetic origins are exposed. Sensory scientists should advocate for transparent sourcing of narratives and clear consent.
Why do open ontologies matter for sensory AI?
Open sensory ontologies—shared descriptor lexicons and transparent data practices—prevent any single commercial entity from controlling the foundation models that encode human taste knowledge. Open-source world models trained on diverse sensory data are more likely to preserve the rich variety of human taste than proprietary systems optimized for a narrow demographic. Openness accelerates scientific progress, fosters cultural diversity in product development, and serves as a hedge against the homogenization of taste that closed, proprietary systems risk producing.
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