AI chatbots have become more and more integrated in the lives of millions since 2022. Beyond technical assistance and finding information, many have turned to these bots for emotional support and companionship. These systems are increasingly used as informal therapists, confidants, or even substitutes for friendship, especially in contexts where people cannot access mental health services. In other cases, some simply want to speak to a non-judgmental conversationalist. People share stories of trauma, anxiety, loneliness, and grief in what they believe are private, one-on-one conversations.
ChatGPT is the pioneer in bringing chatbots to the humans who need them. Companies like Meta, worried about lagging behind, have begun developing their own AI human-sounding machines.
Meta AI is a generative artificial intelligence system developed by Meta, integrated across the company’s platforms, including Facebook, Instagram, Messenger, and WhatsApp. It functions like other large language models such as OpenAI’s ChatGPT or Google’s Gemini, allowing users to interact with an assistant by asking questions, generating content, seeking advice, or prompting for creative tasks.
However, what distinguishes Meta AI is its deep integration into social media and messaging apps that users are already embedded in. Rather than being accessed through a separate platform, Meta AI appears directly within the user interface on Meta’s apps, often alongside the search bar or message threads.
While Meta AI is promoted as a helpful assistant, capable of summarizing articles, drafting messages, or answering personal queries, it also raises serious questions around data collection, consent, and transparency. User prompts, unless explicitly protected, may be stored, analyzed, or used to further train the AI. The lack of clear boundaries between personal data, platform infrastructure, and AI processing presents various privacy risks.
The core of the problem lies in how Meta’s AI interface facilitates, and at times seems to encourage, publicly sharing sensitive conversations. Many users were not aware that their interactions with the AI chatbot, which included personal, medical, financial, or even confessional content, were being published into a public feed. It wasn’t clear from the user interface that the conversations were feeding a public library of AI conversations that anyone can “explore.” Instead, the feature was presented as a personal export or log-saving function. The result is a staggering amount of private and potentially harmful information made openly available to strangers, often linked to identifiable accounts or patterns.
This dynamic is reminiscent of the infamous 2006 AOL incident, where search logs, though pseudonymized, were released and led to the identification of individuals based on their queries. Then, as now, the question is not whether the data was technically anonymized or whether the users clicked “yes” at some point. The question is whether they truly understood the consequences of such an act, and whether the platform took meaningful steps to prevent harm.
The echoes between AOL’s blunder and Meta’s current implementation are not merely rhetorical; they reveal a consistent failure by large tech platforms to treat user data as personal, contextual, and vulnerable.
Meta’s response to the backlash has been to add a warning pop-up before they use the AI feature and to allow users to delete previously shared prompts. But this is not a substantive solution. The warning appears only once and is easily overlooked. It does not correct the underlying design flaw: using vague terminology and unclear placement of the “share” function. Meta continues to reserve the right to use these shared prompts to train its models, and users are not given a clear mechanism to opt out of this data processing. The consent is bundled, non-specific, and arguably incompatible with data protection regulations such as the GDPR and the CCPA, which both require that consent be freely given, specific, informed, and unambiguous.
This situation also raises questions of “dark pattern” design. By offering an interface that subtly nudges users into sharing private content without fully comprehending the implications, Meta may be in breach of regulatory standards that prohibit deceptive or manipulative user experiences. The GDPR in particular requires that data protection by design and by default be respected, which includes presenting users with clear, non-coercive options for how their data is used and shared. In its current form, Meta AI’s design choices appear to serve the interests of data harvesting and engagement metrics over the rights and autonomy of its users.
From a regulatory and advocacy standpoint, the need is clear: platforms must move beyond reactive pop-up warnings and commit to privacy-by-design. Public sharing should never be the default. Interfaces should clearly distinguish between personal use and broadcast, and model training should be contingent on meaningful consent, not embedded in a confusing set of terms.
The lessons from 2006 should not have to be relearned in 2025. Yet Meta’s conduct shows that without pressure from regulators, watchdogs, and civil society, these lessons are ignored.
This episode is part of a larger trajectory of AI deployment within monopolized tech ecosystems, where user data is routinely exploited through calculated ambiguity. As conversational AI becomes more integrated into daily life, the stakes of these design choices become existential for privacy. The law must catch up. But more urgently, companies must be made to respect the principles of dignity, transparency, and restraint, principles that all tech companies must lead with by design.
Cover Photo by DIDEM MENTE / ANADOLU / Anadolu via AFP
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