If I refuse to pay the price they want, will that impact future offers? How do they know I refused, increased surveillance? (Privacy Foundation https://law.du.edu/content/the-privacy-foundation seminar on this topic Friday Oct 23rd. Details soon?)
https://www.usatoday.com/story/money/retail/2026/10/15/surveillance-shopping-prices/92124063007/
Are you paying more than other shoppers? How to protect your wallet
At a time when American shoppers are seeing price hikes from tariffs, the war with Iran and inflation, there's another reason some shoppers are paying different prices, some consumer advocates and lawmakers say.
It's surveillance pricing, which uses personalized information about you to potentially change the price. That is different than dynamic pricing, which is often called surge pricing, and changes with supply and demand.
"Pricing has changed in our economy and in our country,'' said Lindsay Owens, author of "Gouged: The End of a Fair Price – and What That Means for Your Wallet." "It used to be simple. How much does it cost to make the item, what's the profit margin and put a sticker on it. Now it is a highly engineered science. Companies hire physicists, they hire AI specialists. They build algorithms that can change prices in a nanosecond at retail locations across this country."
(Related)
https://digital.sandiego.edu/mcnair-summer/42/
What, Exactly, Is Surveillance Pricing?
(Related)
https://epic.org/wp-content/uploads/2026/08/explaining-surveillance-pricing-2026.pdf
EXPLAINING SURVEILLANCE PRICING AND OTHER DATADRIVEN PRICING PRACTICES
Moscow law?
https://criminaljustice.jurisocial.org/index.php/jjccj/article/view/6
Artificial Intelligence and the Transformation of Legal Decision-Making
Artificial intelligence is increasingly used to support legal and public decision-making, including legal research, document review, risk assessment, administrative processing, and other forms of decision support. Its use can improve speed, consistency, and institutional capacity, but it can also create risks concerning opacity, discriminatory effects, automation bias, procedural fairness, privacy, and the allocation of responsibility. This article examines those risks through qualitative doctrinal analysis and focused comparative policy analysis. It considers the Wisconsin Supreme Court’s decision in State v. Loomis, the European Union Artificial Intelligence Act, Canada’s Directive on Automated Decision-Making, Council of Europe guidance on AI and judicial systems, and selected interdisciplinary scholarship. The article argues that the central legal problem is not simply whether an AI system is accurate, but whether its use can be reconciled with requirements of lawful authority, reasoned decision-making, equality, contestability, institutional accountability, and effective remedy. The article further distinguishes decision-support systems from systems that materially automate or determine legal outcomes and argues that the required safeguards should be proportionate to the legal stakes and degree of automation. The analysis also considers Pakistan, where constitutional fair-trial, equality, dignity, and due-process principles provide an existing legal vocabulary for evaluating public-sector AI, while the National Artificial Intelligence Policy 2025 provides a policy framework rather than a substitute for legislation. The article concludes that responsible public-sector AI requires layered governance: lawful authority, impact assessment, procurement controls, validation and monitoring, meaningful human review, reasons and notice, auditability, access to remedy, and institutional technical capacity.
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