Contact Center Disclosure Issues in an AI-Driven World
Companies that use chatbots, call recording, and AI-driven contact center tools are increasingly facing class action lawsuits under state and federal wiretap laws. These lawsuits typically focus on alleged failures to provide clear disclosure or obtain consent for common contact center features, and targeted disclosures to individuals interacting with the contact center can materially reduce this risk.
Chatbot Claims and Measures to Addresslink to this section
Many class actions have been filed, particularly in California, against companies based on allegations that they failed to disclose and obtain consent for chatbot conversations being recorded and analyzed. California is a two-party consent state, meaning both parties must consent to a call or communication being recorded.
As a practical matter, companies using chatbots should assume that the recording, analysis, storage, or monitoring of chatbot interactions requires clear, upfront disclosure and affirmative consent in two-party consent states.
Companies using chatbots should assume that the recording, analysis, storage, or monitoring of chatbot interactions requires clear, upfront disclosure and affirmative consent in two-party consent states.
Brendan Kasper
While none of these chatbot cases have resulted in decisions favorable to the plaintiffs, companies that disclose that chatbot interactions are recorded and analyzed are significantly less likely to become targets for class plaintiffs in the first place.
Below is an example of a chatbot disclosure that informs users that chatbot interactions are monitored and recorded, may be shared with third-party vendors, and require consent before a user interacts with the chatbot.
Providing this type of disclosure at the start of the interaction significantly reduces the risk of wiretap claims based on lack of notice or consent.
While the primary focus of chatbot class litigation has been a lack of relevant disclosure or consent to conversations being recorded at the time the interaction is initiated, plaintiffs have also raised claims related to the sharing of chatbot data with third-party providers without adequate disclosure. Plaintiffs have increasingly focused on undisclosed data sharing as an independent basis for wiretap and privacy claims.
Having clear disclosures in a customer privacy notice about what data is shared with third-party providers can help undermine these claims. In the example above, the chatbot disclosure provides a link to the privacy notice, which covers personal data disclosed to third parties, making it easier for individuals to review how their data may be used before interacting with the chatbot.
Contact Center AI Claims and Measures to Addresslink to this section
Similar disclosure-based claims are now being brought against companies that use third-party contact center AI technologies that record, transcribe, or analyze calls.
More recently, class plaintiffs have filed wiretap complaints against companies for using third-party contact center AI technologies that record, transcribe, or analyze calls. A notable recent example is the class-action lawsuit filed against Heartland Dental, the largest dental support organization in the U.S.
The lawsuit alleges that Heartland used AI-powered call tracking and transcription tools from RingCentral without obtaining informed consent from patients. According to the complaint, patients who called Heartland-affiliated dental offices had their conversations recorded, transcribed, and analyzed for sentiment without being informed. The lawsuit claims this conduct violates the U.S. Federal Wiretap Act.
This case follows a similar pattern to Gills v. Patagonia, where the plaintiff alleged that Patagonia violated California wiretapping laws by allowing its contact center service provider, TalkDesk, to listen to, record, and analyze the plaintiff’s conversations with Patagonia customer support without disclosing this activity or obtaining consent. TalkDesk provides contact center features such as call transcription, sentiment analysis, and real-time agent assistance.
Like the chatbot claims, none of these newer claims involving contact center AI technologies have resulted in decisions favorable to the plaintiffs. However, expanded disclosures to contact center callers can help companies avoid becoming targets of similar class action claims.
In Gills, Patagonia provided a standard disclosure stating that calls “may be recorded for quality training purposes.” The plaintiff argued that this disclosure did not inform her that a third-party provider was monitoring, recording, and analyzing her conversation using AI-driven tools, nor that her data could be used beyond basic call recording at the time the call was initiated.
An expanded disclosure to contact center callers would make it more difficult for class counsel to pursue these types of claims. For example, an expanded disclosure such as the following would significantly reduce this risk:
“Calls, call data, and case data may be monitored, recorded, stored, and analyzed by [Company] and by third-party vendors and tools utilizing AI technology for quality assurance, training purposes, and the improvement of products and services.”
Class counsel in Gills also argued that Patagonia’s privacy notice did not clearly disclose that it shared audio or other communications with a third-party provider such as TalkDesk. The Heartland lawsuit raises similar allegations, asserting that RingCentral uses patient calls to train its own AI models and develop products for other customers without disclosing this practice to patients.
Companies should ensure that their privacy notices clearly disclose that contact center communications data, including audio and text, may be shared with third parties. This includes whether third-party providers use such data to train or improve their own AI models. Clear privacy notice disclosures are an important supplement to expanded disclosures delivered at the time a caller interacts with a contact center.
Aligning live disclosures, privacy notices, and vendor practices is one of the most effective ways to reduce exposure to these claims.
Aligning live disclosures, privacy notices, and vendor practices is one of the most effective ways to reduce exposure to these claims.
