Actions Speak Louder Than Chats: Investigating AI Chatbot Age Gating
- URL: http://arxiv.org/abs/2602.10251v1
- Date: Tue, 10 Feb 2026 19:55:55 GMT
- Title: Actions Speak Louder Than Chats: Investigating AI Chatbot Age Gating
- Authors: Olivia Figueira, Pranathi Chamarthi, Tu Le, Athina Markopoulou,
- Abstract summary: We investigate whether popular consumer chatbots are able to estimate users' ages based solely on their conversations.<n>We find that while chatbots are capable of estimating age, they do not take any action when children are identified.
- Score: 2.363579139038687
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: AI chatbots are widely used by children and teens today, but they pose significant risks to youth's privacy and safety due to both increasingly personal conversations and potential exposure to unsafe content. While children under 13 are protected by the Children's Online Privacy Protection Act (COPPA), chatbot providers' own privacy policies may also provide protections, since they typically prohibit children from accessing their platforms. Age gating is often employed to restrict children online, but chatbot age gating in particular has not been studied. In this paper, we investigate whether popular consumer chatbots are (i) able to estimate users' ages based solely on their conversations, and (ii) whether they take action upon identifying children. To that end, we develop an auditing framework in which we programmatically interact with chatbots and conduct 1050 experiments using our comprehensive library of age-indicative prompts, including implicit and explicit age disclosures, to analyze the chatbots' responses and actions. We find that while chatbots are capable of estimating age, they do not take any action when children are identified, contradicting their own policies. Our methodology and findings provide insights for platform design, demonstrated by our proof-of-concept chatbot age gating implementation, and regulation to protect children online.
Related papers
- Ask ChatGPT: Caveats and Mitigations for Individual Users of AI Chatbots [10.977907906989342]
ChatGPT and other Large Language Model (LLM)-based AI chatbots become increasingly integrated into individuals' daily lives.<n>What concerns and risks do these systems pose for individual users?<n>What potential harms might they cause, and how can these be mitigated?
arXiv Detail & Related papers (2025-08-14T01:40:13Z) - Understanding Teen Overreliance on AI Companion Chatbots Through Self-Reported Reddit Narratives [7.829454333137073]
We analyzed 318 Reddit posts made by users who self-disclosed as 13-17 years old on the Character.AI subreddit.<n>We found teens often begin using chatbots for support or creative play, but these activities can deepen into strong attachments marked by conflict, withdrawal, tolerance, relapse, and mood regulation.<n>Disengagement commonly arises when teens recognize harm, re-engage with offline life, or encounter restrictive platform changes.
arXiv Detail & Related papers (2025-07-21T16:39:33Z) - SafeChat: A Framework for Building Trustworthy Collaborative Assistants and a Case Study of its Usefulness [4.896226014796392]
We introduce SafeChat, a general architecture for building safe and trustworthy chatbots.<n>Key features of SafeChat include: (a) safety, with a domain-agnostic design where responses are grounded and traceable to approved sources (provenance); (b) usability, with automatic extractive summarization of long responses, traceable to their sources; and (c) fast, scalable development, including a CSV-driven workflow, automated testing, and integration with various devices.
arXiv Detail & Related papers (2025-04-08T19:16:43Z) - Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards [93.16294577018482]
Arena, the most popular benchmark of this type, ranks models by asking users to select the better response between two randomly selected models.<n>We show that an attacker can alter the leaderboard (to promote their favorite model or demote competitors) at the cost of roughly a thousand votes.<n>Our attack consists of two steps: first, we show how an attacker can determine which model was used to generate a given reply with more than $95%$ accuracy; and then, the attacker can use this information to consistently vote against a target model.
arXiv Detail & Related papers (2025-01-13T17:12:38Z) - Are LLM-based methods good enough for detecting unfair terms of service? [67.49487557224415]
Large language models (LLMs) are good at parsing long text-based documents.
We build a dataset consisting of 12 questions applied individually to a set of privacy policies.
Some open-source models are able to provide a higher accuracy compared to some commercial models.
arXiv Detail & Related papers (2024-08-24T09:26:59Z) - WildChat: 1M ChatGPT Interaction Logs in the Wild [88.05964311416717]
WildChat is a corpus of 1 million user-ChatGPT conversations, which consists of over 2.5 million interaction turns.
In addition to timestamped chat transcripts, we enrich the dataset with demographic data, including state, country, and hashed IP addresses.
arXiv Detail & Related papers (2024-05-02T17:00:02Z) - User Privacy Harms and Risks in Conversational AI: A Proposed Framework [1.8416014644193066]
This study identifies 9 privacy harms and 9 privacy risks in text-based interactions.
The aim is to offer developers, policymakers, and researchers a tool for responsible and secure implementation of conversational AI.
arXiv Detail & Related papers (2024-02-15T05:21:58Z) - Evaluating Chatbots to Promote Users' Trust -- Practices and Open
Problems [11.427175278545517]
This paper reviews current practices for testing chatbots.
It identifies gaps as open problems in pursuit of user trust.
It outlines a path forward to mitigate issues of trust related to service or product performance, user satisfaction and long-term unintended consequences for society.
arXiv Detail & Related papers (2023-09-09T22:40:30Z) - You Don't Know My Favorite Color: Preventing Dialogue Representations
from Revealing Speakers' Private Personas [44.82330540456883]
We show that speakers' personas can be inferred through a simple neural network with high accuracy.
We conduct extensive experiments to demonstrate that our proposed defense objectives can greatly reduce the attack accuracy from 37.6% to 0.5%.
arXiv Detail & Related papers (2022-04-26T09:36:18Z) - StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child
Interactive Storytelling with Flexible Parental Involvement [61.47157418485633]
We developed StoryBuddy, an AI-enabled system for parents to create interactive storytelling experiences.
A user study validated StoryBuddy's usability and suggested design insights for future parent-AI collaboration systems.
arXiv Detail & Related papers (2022-02-13T04:53:28Z) - Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn
Chatbot Responding with Intention [55.77218465471519]
This paper proposes an innovative framework to train chatbots to possess human-like intentions.
Our framework included a guiding robot and an interlocutor model that plays the role of humans.
We examined our framework using three experimental setups and evaluate the guiding robot with four different metrics to demonstrated flexibility and performance advantages.
arXiv Detail & Related papers (2021-03-30T15:24:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.