Uncategorized

The DOJ’s consent decrees with RealPage, LivCor, Cortland, and Greystar have defined what compliant algorithmic pricing must look like — and California, New York, and Connecticut have now banned the practice outright in residential rental. This post walks through the enforcement campaign and what businesses in every industry must take from it.

The UK’s Competition and Markets Authority has developed a four-category framework for algorithmic collusion — from cartel implementation to autonomous AI coordination — that is now the global reference point for regulators worldwide. This post explains the framework and the CMA’s live investigation into Hilton, IHG, and Marriott.

New York’s Algorithmic Pricing Disclosure Act is already in effect, requiring businesses to display mandated language whenever AI algorithms use personal data to set individualized prices. This post is a step-by-step compliance guide covering scope, exemptions, enforcement, and the antitrust exposure that runs alongside the disclosure obligation.

AI-driven surveillance pricing — where algorithms use your personal data to set individualized maximum prices — now faces simultaneous enforcement from the FTC, DOJ, and more than 20 state legislatures. This post maps the current regulatory landscape and the compliance steps businesses must take now.

The DOJ’s RealPage case established a hub-and-spoke antitrust theory for algorithmic pricing that is now spreading to hotels, healthcare, retail, and beyond. This post traces the theory’s development through the RealPage settlement, the Ninth Circuit’s Cendyn ruling, and the MultiPlan healthcare litigation, and explains what the current legal landscape means for businesses using shared-data pricing systems.

The CFTC’s December 2024 AI advisory maps existing regulatory obligations onto AI systems across the full spectrum of registered entities, and its May 2026 cooperation policy creates a meaningful declination path for firms that self-report, cooperate fully, and remediate. This post explains both developments and what they mean for compliance programs in derivatives markets.

AI-driven underwriting creates real compliance risk under the FCRA and ECOA — and regulators have made clear that algorithmic complexity is not an excuse. This post covers adverse action notice requirements, the CFPB’s 2023 and 2025 guidance on AI credit scoring, and what a compliant AI lending program actually requires.

NYDFS has issued a sequence of AI guidance documents covering insurance underwriting, cybersecurity, third-party vendor risk, and fair lending — and together they create a detailed compliance framework for every bank, insurer, and fintech licensed in New York. This post surveys what those documents require and what compliance looks like in practice.

When you deploy a third-party AI vendor for a covered function, your regulatory obligation to supervise that function doesn’t transfer—it stays with you. This post covers what the SEC and FINRA currently require from financial firms using third-party AI systems, including due diligence, contractual controls, and ongoing supervision.

FINRA’s 2026 Annual Regulatory Oversight Report moves from describing AI risks to specifying what supervisory frameworks broker-dealers must have in place — including, for the first time, substantive requirements for autonomous AI agents. This post translates those priorities into a concrete compliance action plan.

The SEC’s 2024 enforcement actions against Delphia and Global Predictions establish concrete compliance obligations for investment advisers using AI. This post analyzes each case in depth and translates the findings into five specific obligations every registered adviser must address.

AI washing — overstating a company’s AI capabilities to investors — has become an active SEC enforcement priority. From the 2024 Delphia and Global Predictions actions to the 2025 Presto Automation case and criminal charges against Nate Inc., this post analyzes what the cases establish and how to build a legally defensible AI disclosure program.

The SEC’s recordkeeping enforcement campaign cost financial institutions over $600 million in penalties — and AI-generated content faces the same analytical framework. This post explains what broker-dealers and investment advisers must retain under Rules 17a-4 and 204-2, and what a compliance audit should cover in 2026.

AI is transforming drug discovery, but it raises complex legal questions around patent inventorship, FDA regulatory validation, and licensing agreement structure. This post works through all three systematically for biotech companies, pharma licensors, and their counsel.

Biobanks hold millions of samples collected under consent forms that predate AI — but pharmaceutical companies need that data for AI drug discovery training. This post examines the Common Rule and HIPAA consent gaps, the IP risks they create, and the remediation strategies institutions must deploy now.

AI patient recruitment tools trained on historically non-diverse datasets can systematically exclude underrepresented populations — directly undermining FDA’s FDORA diversity action plan requirements. This post examines the algorithmic bias risks, the regulatory exposure, and what sponsors must do to stay compliant.

FDA’s January 2025 draft guidance creates new audit and documentation obligations for every AI tool used in clinical trials. This post examines what inspectors will ask, what vendor contracts must include, and how sponsors can ensure their clinical trial AI is fully audit-ready.

Only 3.6% of FDA-cleared AI medical devices reported race or ethnicity data from their validation cohort. This post examines what FDA now requires for training data representativeness, and the product liability exposure manufacturers face when demographic bias causes patient harm.

FDA’s January 2025 draft guidance on AI in drug development establishes a seven-step credibility assessment framework that pharma and biotech companies must apply to any AI used to support regulatory submissions. Here is what the context-of-use framework requires and what companies must do now.

The PCCP framework lets AI medical device manufacturers pre-authorize future algorithm updates without filing a new FDA submission for each change. Here is how the three required components work and why building your PCCP at submission time is a strategic necessity.

Whether your AI healthcare product is a regulated medical device under FDA rules is the most consequential classification decision you will make. Here is how the four-factor CDS test works, what falls inside regulation, and why getting this right at the start matters.

Agentic AI is shifting healthcare compliance from periodic sampling to continuous monitoring. Here is what agentic compliance programs can do, and the governance architecture required to make them legally defensible.

Tennessee’s AI Mental Health Law bans marketing chatbots as licensed mental health professionals and creates a private right of action. Here is what the law requires, where it sits in the national patchwork, and what digital health companies must do now.

AI in healthcare creates real False Claims Act exposure across five key use cases — from clinical decision support to billing optimization. Here is what your compliance program needs to address before the government comes knocking.

AI-powered patient text messaging sits at the intersection of the TCPA’s class action exposure, the FCC’s 2025 consent revocation rules, and the Supreme Court’s McLaughlin decision freeing courts to chart their own course on TCPA interpretation. Here’s the compliance architecture every digital health platform needs.