AI and the privacy paradox: How technology can protect online self-disclosure

By integrating actionable insights and fostering a culture of informed decision-making, AI can evolve into both a protector and enabler of online expression.

In an era where online platforms serve as confidants for millions seeking support or sharing life's nuances, the balance between openness and privacy is increasingly delicate. Whether on Reddit, Twitter, or other forums, self-disclosures - statements revealing personal information - are both empowering and perilous. But how much do individuals understand the risks tied to their digital footprints?

A recent study titled "Measuring, Modeling, and Helping People Account for Privacy Risks in Online Self-Disclosures with AI" - conducted by researchers from Carnegie Mellon University and Georgia Institute of Technology - explores how artificial intelligence (AI) can illuminate privacy risks and guide users in navigating these murky waters. The research, currently available on arXiv, is slated for publication in the Proceedings of the ACM on Human-Computer Interaction (CSCW 2025).

Hidden Risks of everyday sharing

Digital anonymity encourages openness, and platforms like Reddit thrive on the authenticity users bring through deeply personal posts. These disclosures can foster community bonds, aid mental health, and facilitate meaningful exchanges. Yet, this openness has a darker side: the information users share can be mined, misused, or linked back to their identities, exposing them to unforeseen dangers like cyberstalking, doxxing, or discrimination.

The study highlights the challenges users face in recognizing these risks. While many understand the concept of online privacy in theory, fewer are equipped to identify specific vulnerabilities in their posts. What feels like a casual admission - sharing one's age, location, or medical condition- might be a breadcrumb for malicious actors.

AI as a privacy advocate

To address these challenges, the researchers developed an AI-driven natural language processing (NLP) tool that analyzes online posts to identify potential privacy risks. Unlike traditional systems that broadly label content as risky, this tool pinpoints specific segments, or "text spans," within posts that could reveal sensitive information. For example, rather than flagging an entire post about a medical condition, the tool identifies particular phrases - like the name of a medication or a hospital visit - that could make the user vulnerable.

Participants in the study found this granularity invaluable. It allowed them to address risks while preserving the intent of their messages. Beyond identifying risks, the tool categorizes disclosures into types—such as financial, health-related, or location-based—helping users understand the specific nature of their vulnerability. This approach transforms the tool from a restrictive gatekeeper to a proactive mentor, empowering users to refine their narratives with greater control.

The tool also encourages reflection by offering contextual feedback. For instance, a user disclosing their job title alongside their city might receive an alert explaining how this combination could be used to identify their workplace. Such insights enable users to weigh the trade-offs between openness and privacy in real time.

Navigating the limitations of AI

Despite its promise, the study highlights the limitations of current AI tools. Context remains a significant challenge. For example, the tool might flag a user’s hypothetical scenario as risky, misinterpreting it as a factual disclosure. Similarly, norms within specific online communities—where certain types of sharing are common or even encouraged—are not always accounted for, leading to unnecessary alerts.

Another limitation lies in the lack of personalization. Users expressed a desire for the tool to learn from their posting history to provide more tailored recommendations. For example, someone who regularly shares health-related content might benefit from nuanced guidance about balancing their openness with protective strategies. Furthermore, participants wanted actionable suggestions, such as rephrased alternatives that maintain the original intent but reduce risk.

The tool’s inability to consider the broader context of online interactions also surfaced as a concern. For instance, it might not recognize that a disclosure in one post becomes risky only when combined with information shared in another. Addressing these gaps is critical to building trust and improving the tool's utility.

Navigating the path to safer digital interactions

The study highlights the importance of AI systems that not only detect risks but also educate users, offering guidance on framing disclosures in ways that maintain authenticity while minimizing vulnerabilities. However, addressing privacy challenges requires more than just technological innovation. A collaborative effort involving platform developers, policymakers, and users is essential. Platforms must adopt transparent policies and establish clearer guidelines for safe sharing, while education initiatives should empower individuals with digital literacy to better understand and manage their online presence.

By integrating actionable insights and fostering a culture of informed decision-making, AI can evolve into both a protector and enabler of online expression. This research paves the way for a future where self-disclosure and privacy are no longer at odds, as technology and human agency come together to create a safer, more thoughtful digital ecosystem. Here, AI can act as a trusted partner, ensuring that users can share meaningfully without compromising their safety.