AI Price Fixing and Discrimination with Recommendations for Retailers
Next year will likely bring more lawmaking surrounding artificial intelligence algorithms used to set consumer pricing. Two themes are emerging: price fixing and price discrimination.
AI price fixing uses algorithms to coordinate pricing, which is raising increased scrutiny in the U.S. and Europe. Companies provide competitively sensitive data to third-party providers that analyze it to recommend prices for participating companies, leading to artificially inflated prices and reducing competition.
After over a year of litigation, the Department of Justice (DOJ) recently filed a proposed settlement with a revenue management software company, related to its algorithmic rental pricing software. State AGs continue to litigate against participating companies, and lawmakers have introduced bills to ban algorithmic price fixing. California has passed laws to restrict the use of common pricing algorithms, and regulators in the EU and UK are also focusing on algorithmic collusion.
Regulation challenges include the argument that antitrust laws may have loopholes that make AI-driven pricing hard to prosecute; distinguishing collusion from companies’ legitimate responses to market conditions; and AI complexities creating difficulty proving violations.
AI price discrimination uses algorithms to set personalized prices for products or services based on the individual consumer’s behavior, demographics or perceived willingness to pay. The process is often hidden, making it hard to know who pays what. That raises fairness concerns (algorithms might exploit vulnerable groups and charge them more), prompting legal scrutiny and proposed regulations.
The FTC is investigating AI’s role in price discrimination, while recently passed regulations and laws in California aim to ban using personal data for discriminatory pricing or make it easier to sue over algorithmic pricing. New York has enacted several price discrimination laws requiring algorithmic pricing disclosure and prohibiting gender-based pricing.
Recommendations for retailers
While it is perfectly legal to test prices and adjust them to market conditions, retailers should be careful when reviewing and adopting AI pricing tools. The recent proposed settlement from the DOJ highlights some best practices for using these tools:
- Confirm that the AI tools train on publicly available pricing data or aggregated data, not the non-public and sensitive pricing strategies of competitors.
- Ensure that the AI tools allow prices to be lowered as well as increased within user-defined price ranges.
- Keep humans in the loop and check that users can reject or override price suggestions.
In addition, businesses may consider the following best practices to lessen the risks of allegations of unlawful AI price discrimination:
- The AI tools should not adjust pricing dynamically based on consumer profiles or other sensitive personal information (e.g., veteran status, religious beliefs, health condition).
- If the AI tool adjusts pricing based on location, retailers should confirm that this reflects legitimate market conditions and business concerns, rather than historic bias.
- Confirm that the AI tool uses de-identified and/or aggregated data, both to train its models and during deployment, so information is not linkable to an individual.
Shadow AI: How Can Companies Protect Against Unknown Uses?
Employees don’t always wait for their employers to approve new technology. This could be using a personal chatbot account to summarize a document, installing an AI browser extension, uploading information into an AI analysis tool or using an AI feature built into software without their employer knowing about it. This practice is often referred to as “shadow AI.”
Shadow AI is typically not malicious. Often, it’s the result of employees wanting to work more efficiently, or as a result of unclear AI use policies. However, shadow AI can create security and confidentiality issues. If employees send personal or company information into an AI system that has not been reviewed, the business may not have an accurate picture of where its information is going, how it’s being used, or for how long it is being stored.
What does shadow AI look like?
Shadow AI could include any number of unapproved AI tools used for work purposes. For example, an employee may paste customer information into an LLM to create a summary, upload internal presentation information for editing or use an AI tool to analyze a spreadsheet.
The problem is that using shadow AI can result in company information being sent to a third-party provider that the original company may not know about. By hiding in the “shadows,” employees who use AI tools in this way can create governance gaps. Without proper review of the AI tools being used by its employees, a company may not know what information is being provided, how long it will be retained for, who can access it or whether the vendor can use it for other purposes, like training its own systems.
How can companies address employee AI usage?
Company AI policies can bring clarity to issues surrounding shadow AI.
By addressing what kinds of technologies may be used, these policies can explain which AI tools are approved, what information employees may enter into those tools, and when a new AI tool or use requires review. In general, companies drafting these policies may want to specifically and clearly state what information may and may not be used. For example, a general policy telling employees to use AI responsibly may not provide enough guidance when someone is deciding whether to upload a confidential document or customer data into a new tool.
These policies should also be communicated clearly. Without understanding the policies that a company has in place, employees may inadvertently engage in shadow AI use. With clarity on approved tools, uses, and inputs, an effective employee AI use policy could lower these risks. Within this policy, a company may may consider creating a process to request new AI tools. This way, relevant business teams can review any AI tools and uses before company information is provided.
What should businesses take away?
Businesses do not necessarily need to prohibit AI use. However, companies may consider reviewing which tools employees are using and what information is being provided to them. Some steps companies may consider in this review process include, but are not limited to:
- Identifying AI tools employees are actually using, including personal accounts, browser extensions and AI features within existing software.
- Explaining which tools are approved, restricted or prohibited and what information employees may enter.
- Considering privacy, confidentiality, retention, deletion and AI-training terms with vendors.
- Determining whether AI uses involve processing that requires a CCPA risk assessment or updates to privacy documentation.
- Establishing rules preventing employees from providing sensitive company information to unapproved AI tools.
- Giving employees a clear way to request new AI tools before using them for company work.
While AI in the workplace may boost efficiency, it may also create risk if the company is not aware of it. By providing employees with guidance on AI, businesses may be able to reduce the risks of shadow AI.
AI Notetakers: Key Takeaways for Recording Calls
Use of AI notetakers is quickly becoming routine. These tools can automatically join video calls, listen to conversations, generate transcripts, create summaries, and identify key points in a meeting.
While these tools have an argument for efficiency, they also create legal, privacy, and confidentiality risks.
This issue is a lot more complicated than simply asking participants of a meeting for their consent to be “recorded.” This is because recording, transcription, and AI processing are separate activities – and each may have its own notice and consent requirements.
As a result, businesses using these tools need to understand both what is happening during the meeting and what happens to the information after the meeting has ended.
What are AI notetakers?
An AI notetaker is a tool that captures meeting content and uses artificial intelligence to create transcriptions, summaries, action items, and log other important meeting records. Most operate as a bot that joins a video conference meeting as an additional participant.
Many AI notetakers process information from meetings through cloud-based services rather than keeping the conversation only on an employee’s local computer. Depending on the provider, the service may receive audio transcripts and other meeting information and then use an AI system to analyze and summarize the dialogue. This means meeting content may be transferred outside of the company’s own system and processed or stored by a third-party provider, which may have privacy and data-sharing implications.
Why do recording and consent laws matter?
Recording laws differ across the United States. Some states generally follow a one-party consent approach, meaning the consent of one participant is sufficient to record a conversation. However, other states follow an all-party consent approach, sometimes referred to as “two-party consent” which generally requires the consent of everyone involved in a conversation for recording. The specific details and exceptions vary by jurisdiction.
What actually counts as consent?
Sometimes consent can be more complicated than just simply displaying a recording symbol. Video-conference platforms may use different methods to alert participants when recording a meeting begins: a pop-up requiring participants to acknowledge the recording, an audible announcement, a banner or an icon that’s displayed during the meeting, some hosts may also require verbal consent.
But notice and consent are not always the same thing and what constitutes legally sufficient consent can depend on the applicable law and the circumstances at hand.
What happens to meeting information after the call?
Once an AI notetaker has captured a meeting, businesses should understand what rights the provider has over that information. Vendor terms can address data ownership, licensing, data retention, and whether information may be used to develop or improve the provider’s service.
This becomes especially important as ordinary workplace conversations frequently include information that employees may not intentionally send to a third party: customer information, personnel issues, financial projections, internal strategy, product development and many other confidential materials may all be discussed while the AI notetaker captures the conversation.
The transcription or summary can also create additional security concerns once the meeting ends. For example, automatically generated notes may be distributed via email, downloaded, forwarded and stored in employee accounts long after an original conversation has occurred. Meeting summaries create additional opportunities for sensitive information to spread or remain stored indefinitely.
Businesses should therefore review not only what the tool captures but also who receives the resulting transcript, where it is stored, how long it is retained and who has the ability to delete it.
What about attorney-client privilege?
Adding an AI notetaker to a legal discussion can create questions about whether confidentiality has been maintained, and depending on the circumstances, whether privilege could be challenged or waived. You can read more updates from Federal District Courts on the issue here.
This does not mean that all use of technology during a legal meeting automatically destroys attorney-client privilege. Whether privilege is affected may depend on the circumstances such as how the AI provider handles information and why the tool is being used. Therefore, businesses should be more cautious about allowing AI notetakers into meetings that involve legal advice or other professionally protected information.
The same concern applies to trade secrets and other confidential business information. Meetings involving sensitive product plans or internal strategies and other proprietary information may not be appropriate for AI transcription unless the tool and its data practices have been reviewed carefully.
What should businesses take away?
AI notetakers can be useful workplace tools, but businesses should have clear policies in place before employees begin using them regularly. Below are some high-level tips that businesses may want to consider when onboarding a new AI notetaker:
- Approve specific tools: Employees should know which AI notetakers are permitted and how those tools record, transcribe, store and process meeting information.
- Create clear notice and consent procedures: Businesses may consider the laws that may apply to meeting participants and make sure notices accurately reflect how AI is being used.
- Limit use in sensitive meetings: Legal, HR, disciplinary investigation and other confidential discussions may require additional approval or no AI notetaker at all.
- Review vendor data practices: Companies should understand how meeting information is used, stored, retained and deleted, including whether it may be used to train or improve AI systems.
- Set rules for transcripts and summaries: Businesses may want to determine who can access or share AI-generated notes, how long they are kept and whether they are treated as official company records.
Healthcare AI data breaches: Why companies need to protect more than patient records
Healthcare and life science companies are not only protecting traditional patient records – things like names, diagnoses, treatment notes, lab results, and insurance information. They may also be protecting clinical trial data, research data, vendor systems, proprietary models, and other information used to support patients and drug development. When these companies and systems are involved in data breaches, the legal risks can become broader than a standard data breach.
A useful example is the recent Novo Nordisk incident. Novo Nordisk is the pharmaceutical company that makes drugs such as Ozempic and Wegovy and recently disclosed a cyber incident involving patient data from clinical trials. It has also been reported that a hacking group claimed to have stolen over a terabyte of data and attempted to extort the company, though Novo Nordisk has not confirmed the entire scope of those claims.
The Novo Nordisk incident shows that healthcare breaches can involve more than names and medical records. Companies also need to consider whether a cyber incident involves clinical trial information, healthcare provider data, research files, AI model information, or other confidential business materials. When this type of data is involved, companies may need to take precautions to protect the data, document their response, and comply with applicable privacy and cybersecurity obligations.
Recent health-related breaches also highlight how these incidents create major legal and business consequences. The Change Healthcare cyberattack that occurred in 2024 shows the scale of healthcare cybersecurity risks with reports that 197.2 million individuals were impacted by this breach. For scale, 197.2 million people would be more than half of Americans. The 23andMe breach also highlights the litigation risks tied to sensitive genetic information, with a bankruptcy judge approving a $46.75 million settlement for victims in the breach. While the facts of these examples differ, they all highlight why companies handling health-related data need to treat cybersecurity, AI governance and breach response as connected compliance and a priority in business practices.
Why are AI-related healthcare breaches unique?
A standard healthcare breach analysis often focuses on whether names, medical records, insurance information, or other identifiable patient information was exposed. These concerns are highly important and sensitive. But as artificial intelligence becomes more integrated into healthcare, it creates additional risks because AI tools rely on large volumes of sensitive data that may also be tied to valuable research and business information.
For example, an AI model used in drug development may involve clinical trial data, molecular research, imaging data, prompts, outputs and other model training information. If these systems are accessed by an unauthorized actor, the incident may raise privacy concerns and may also create IP and competitive risks.
This means healthcare AI security should not only focus on protecting patient records. Companies should also consider whether their AI models, training datasets, research systems, and vendor environments are adequately secured.
Why does de-identification matter?
De-identification can be key when healthcare data is used with AI. In simple terms, de-identification means removing information that could identify a person up to a certain legal standard. This matters because companies may want to use patient data or clinical data to train, test or improve their models.
If health data is properly de-identified before being used with AI, the company may reduce privacy risks. However, if the data is not properly de-identified, several questions remain. The company may need to ask whether its privacy notices clearly explained that health data could be used for AI training or analysis. State privacy laws may also require clear disclosures about how personal information is collected and used. For example, California’s CCPA requires covered businesses to provide notice about the categories of personal information collected and the purposes for which that information is collected or used. This is especially important when health-related data is later used in a way that patients or consumers may not have expected.
The company may also need to consider whether protected health information was shared with an outside AI vendor, whether a business associate agreement was required, and whether patient authorization was needed.
What happens if PHI may have been compromised?
Breach response is also an important issue. If a healthcare company experiences a ransomware attack or an unauthorized access incident, they may need to assess whether protected health information (PHI) was compromised, This may include reviewing what information was involved, whether it could identify an individual, who accessed it, whether it was viewed or acquired and if the risk was mitigated or not.
The Novo Nordisk incident highlights why this analysis matters. Novo Nordisk stated that the clinical trial information involved was not directly linked to patient names or other direct identifiers. However, the incident still raised questions about patient-related data, de-identification, and whether any protected health information may have been compromised. This is why companies should be able to document how health-related data was stored, protected, and separated from information that could identify individuals.
What should companies take away?
For healthcare and life science-related companies, the key takeaway is that AI governance should include privacy and cybersecurity from the start. Before using health-related data with AI, companies should ask:
- What data goes into the AI system?
- Is the data identifiable or properly de-identified?
- Who can access the data, prompts, outputs, or model?
- Are outside vendors involved?
- Can the vendor use the data to train or improve its own models?
- What security controls apply if the system is breached?
Companies should also treat AI models, training datasets, prompts, outputs, and research tools as sensitive assets as well since they can carry PHI and may lead to inadvertent disclosures. Vendor contracts should address confidentiality, data retention, security controls and model training.
AI may help healthcare and life science companies innovate and operate faster, but innovation cannot replace privacy and regulatory accountability. Companies using AI with health-related data need to confirm de-identification practices, strengthen vendor contracts, and properly prepare for responses before incidents occur.
AI Use in Clinical Trials: Privacy, Confidentiality, and Compliance Risks
Artificial Intelligence (AI) is becoming increasingly common in health care clinical trial operations. Sponsors, contract research organizations, research sites and vendors may use AI for many tasks such as patient recruitment, eligibility screening, and informed consent support among other things. While this can improve efficiency, these tools may also create privacy, confidentiality and compliance risks in an already highly regulated environment.
Clinical trials often involve sensitive patient information, confidential study data, and strict documentation obligations. This means that AI use in clinical research cannot be treated like traditional workplace software. Before deploying an AI tool, organizations should understand what data the tool will access, where that data will go, whether the vendor can use it to train models, and whether the output can be explained, reviewed, and preserved.
How is AI being used in clinical trials?
AI tools may be used throughout the clinical trial process. For example, AI can be used to help identify potential participants, screen patient eligibility, summarize medical records, draft study documents, monitor data and flag adverse events. Generative AI may also be used for other tasks in the clinical trial process such as summarizing meeting notes, clinical records or other trial related documents.
These uses can be helpful, but they also raise basic compliance questions. Before using an AI tool, organizations need to ask whether the tool fits within the trial’s legal and contractual framework. Clinical trial agreements and site agreements often control who may access trial data and how that data can be used, but those contracts may have been drafted without considering the use of AI in the trial. If an AI vendor is introduced without updating those agreements, contracts that are not AI-specific may create gaps in data access, confidentiality and regulatory accountability.
What legal obligations matter?
AI use in clinical trials must also fit within existing privacy, research and documentation obligations. HIPAA and its related rules may apply when AI vendors receive, store and analyze PHI. This means, organizations may need privacy and security safeguards before sharing their trial data with the AI vendor.
FDA electronic records requirements should also be considered as clinical trial records have to be reliable, auditable and traceable. If an AI tool creates a summary or other output without prompt logs or documented human review, it may create problems for recordkeeping and data integrity.
Consent for the use of AI in the clinical trial process is also a key factor, especially when AI is used in recruitment processes or eligibility screenings. In these situations, participants should receive clear information about how AI is involved and how their information may be used.
Finally, confidentiality should remain at the forefront. Clinical trials do not only involve patient data, but may also involve sensitive commercial information, such as investigational product data, interim results, safety signals, and proprietary research methods. If an AI tool takes in this information for transcription, summarization, or analysis without clear confidentiality restrictions, it may create risks for sponsor confidentiality, trade secret protection, or attorney-client privilege.
Why are AI vendors a risk?
AI vendors can be one source of risk in clinical trials because they may receive and process very sensitive trial data. Standard AI vendor terms may not be enough when clinical trials involve protected health information or FDA-regulated records. Before using a specific AI vendor, organizations should review whether the vendor can retain prompts or outputs, use trial data to train its models, allow employee access to uploaded data, or store information outside approved systems.
Vendor contracts should clearly address data retention, model training, confidentiality, audit rights, security controls and human oversight. Additionally, companies should not rely only on what the vendor promises in the contract. They should verify how the vendor actually handles trial data before using the AI tool.
What should companies take away?
For sponsors, meaning the organizations responsible for clinical trials, the main takeaway is that AI should be reviewed before it is used with clinical trial data. The organization should review what the AI tool will be used for, whether it will access PHI or confidential study information, whether the vendor can use trial data to train its models and if AI-generated outputs can become part of the trial record.
There are AI vendors that offer privacy-protective features like PHI redaction and de-identification. However, companies should not assume these features automatically make the tools compliant. Companies still need to verify how the vendor stores, processes, deletes and protects clinical trial data before deployment.
AI may help clinical trials become more efficient, but efficiency does not replace privacy, confidentiality, consent, or regulatory accountability. Companies should update agreements, limit data exposure, verify vendor practices, document human review, and clearly disclose AI involvement where appropriate.