- Pascal's Chatbot Q&As
- Posts
- Consumers will be scrutinized by AI for every micro-decision they make. Their intent will be reconstructed, their actions assessed, their responsibility inferred probabilistically.
Consumers will be scrutinized by AI for every micro-decision they make. Their intent will be reconstructed, their actions assessed, their responsibility inferred probabilistically.
Meanwhile, vendors may try to use AI’s complexity as a shield to deflect their own liability. We must not allow this imbalance to materialize unchecked.
Pascal Hetzscholdt
November 15, 2025
When Autonomy Cuts Both Ways: How Manufacturers and Insurers Will Use AI to Vet Consumer Fault—and What This Means for Society
by ChatGPT-5
If the first essay established why cognitive autonomy should not absolve vendors from liability, the next logical step is to examine the inverse: how manufacturers and insurers will inevitably deploy AI to shift—or at least verify—responsibility back onto consumers. The tension is predictable. As AI systems become more autonomous and capable, vendors will be held accountable for harms their products cause. But these same capabilities will also give them unprecedented tools to audit the behaviour of their users, scrutinize claims, and triangulate responsibility in ways that have never before been technically possible.
This duality marks a profound shift: AI will make it easier for companies to investigate consumers than for consumers to investigate companies. And that asymmetry will shape everything from warranty disputes to insurance claims to regulatory enforcement.
1. AI as a Fault Attribution Engine
Manufacturers and insurers already collect vast amounts of telemetry data from devices, vehicles, household appliances, and digital services. AI supercharges this. Instead of relying on simple logs, AI can interpret behavioural patterns, reconstruct sequences of events, and infer intent or negligence.
In the near future, when a dishwasher “explodes,” the vendor will not simply inspect the hardware. They will run an AI-driven diagnostic of the entire behavioural context:
How many times did the user override safety warnings?
Did they regularly misuse detergent?
Did they circumvent recommended maintenance?
Did they disable load-balancing or safety features?
And crucially: Was this a predictable misuse that the manufacturer should have anticipated, or was it genuinely abnormal behaviour by the user?
The insurer will run similar checks before deciding whether to pay out.
AI will become an agent of verification—sometimes of truth, sometimes of denial.
2. Examples of AI-Assisted Consumer Fault Attribution Across Domains
Below is a non-exhaustive, but sweeping, list of scenarios where AI will likely be deployed to determine consumer responsibility. These examples are not speculative fiction; they are extensions of capabilities already emerging.
A. Home Appliances and Smart Devices
Smart dishwashers, ovens, and washing machines
AI detects whether the consumer loaded inappropriate items (e.g., flammable plastics).
AI logs repeated misuse patterns.
AI infers that the consumer ignored explicit warnings on the digital display.
AI analyses vibration, load distribution, and cycle history to detect abuse.
Insurance outcome: Claim denied due to “improper usage.”
B. Electric Vehicles and Mobility
Autonomous or semi-autonomous driving systems
AI reconstructs accident patterns with millisecond-level telemetry.
AI evaluates whether the driver took appropriate control when requested.
AI determines whether the user ignored takeover alerts, fatigue warnings, or lane departure alarms.
Insurance outcome: Claim reduced or denied for “driver negligence.”
Manufacturer outcome: Liability shifted to user even when system behaviour was ambiguous.
C. Consumer Electronics and Digital Services
Smartphones, augmented reality devices, VR headsets
AI evaluates whether a user overrode privacy prompts.
AI checks whether the user gave ambiguous or contradictory commands.
AI determines whether sensitive actions were authorized by the user, using behavioural biometrics.
Outcome: “You granted permissions; we are not liable.”
D. Health Tech and Wellness Devices
Wearable sensors and consumer medical diagnostics
AI evaluates compliance with usage instructions.
AI checks whether the consumer calibrated the device properly.
AI determines whether health monitoring failures were caused by user error (e.g., incorrect positioning of sensors, non-compliance with care routines).
Outcome: Insurance coverage disputes over “user non-compliance.”
E. Home Security and IoT Systems
AI-driven alarm systems
AI checks if the homeowner disabled the alarm intentionally.
AI reviews whether suspicious activity was ignored.
AI infers user negligence (e.g., leaving doors unlocked despite warnings).
Outcome: Burglary-related claims challenged by insurers.
F. Content, IP, and Account-Based Systems
AI agents acting in user accounts
AI logs whether the user approved draft outputs.
AI compares the user’s historical behaviour with anomalous agent actions.
AI infers whether the user failed to configure permissions properly.
Outcome: Vendor claims the user failed to set boundaries—fault shifted back.
G. Workplace Tools and Business Software
AI-assisted productivity and enterprise systems
AI assesses whether an employee ignored safety protocols.
AI analyses compliance with workflow instructions.
AI flags human override choices that led to system failures.
Outcome: Employer disputes employee defence claims by referencing AI logs.
3. At the Border of Surveillance: AI Infers Intent
Where things become ethically fraught is not the simple logging of actions, but AI’s ability to infer why a user acted. For example:
AI guesses that a user intentionally ignored warnings because they were “in a hurry” based on behavioural patterns.
AI claims the user “should have known” given past usage data.
AI asserts “predictable misuse” and allocates fault accordingly.
This crosses into an epistemic domain once reserved for courts, not algorithms.
4. The Coming Arms Race: Consumers vs Algorithmic Forensics
Once AI becomes the arbiter of fault, consumers will:
argue against black-box interpretations
attempt to restrict data collection
try to prove the system misinferred intent
demand access to logs and explanations
fight “algorithmic blame shifting” in court
Regulators will struggle to keep pace. Manufacturers and insurers will increasingly rely on AI to protect their margins. And the resulting terrain will look less like consumer protection and more like a digital battlefield where one side holds all the data.
5. Societal Consequences: A Future of Algorithmic Accountability Battles
The consequences of this shift extend far beyond any individual product liability scenario.
A. Erosion of consumer trust
Consumers will face systems that:
gather evidence against them
make probabilistic judgements about their behaviour
deny claims at scale with algorithmic precision
Trust in insurers, manufacturers, and service providers will erode rapidly.
B. Asymmetric power dynamics
Vendors will possess:
complete telemetry histories
behavioural analytics
proprietary inference models
Consumers will have none of these tools.
This imbalance fundamentally destabilizes consumer rights frameworks.
C. Normalization of surveillance-by-design
To protect themselves, companies will hardwire surveillance into their products. “Continuous monitoring” becomes a feature, not a warning label.
D. Legal and ethical complexity around blame attribution
Courts will have to adjudicate:
whether AI inferences are admissible
whether consumers can challenge black-box decisions
whether algorithmic intent reconstruction is legally valid
These will become recurring questions in civil and commercial litigation.
E. A chilling effect on autonomy and creativity
Consumers may avoid:
modifying products
experimenting with features
using devices in novel ways
engaging with AI agents
For fear of accidentally triggering liability.
F. The potential for discriminatory outcomes
AI fault attribution will inherit bias:
interpreting behaviour differently across socioeconomic groups
penalizing vulnerable populations
amplifying historical discrimination in insurance or warranty claims
Conclusion: The Age of Algorithmic Accountability is Coming—But Only for Consumers
The irony of the future is palpable. Consumers will be scrutinized by AI for every micro-decision they make. Their intent will be reconstructed, their actions assessed, their responsibility inferred probabilistically. Meanwhile, vendors may try to use AI’s complexity as a shield to deflect their own liability.
We must not allow this imbalance to materialize unchecked.
If society is to embrace cognitive autonomy, the rules must evolve to ensure that accountability does not become a one-way mirror—transparent when examining consumers, opaque when examining manufacturers. Only through enforceable transparency, explainability, and balanced liability models can we prevent AI from becoming a tool of blame-shifting rather than a force for safety, reliability, and fairness.
·
10 NOV
