Navigating Disclosure Risks and Benefits in Conversational AI: User Perspectives on Privacy Challenges with Large Language Models

핵심 개념

Users constantly face trade-offs between privacy, utility, and convenience when using large language model-based conversational agents. However, users' erroneous mental models and dark patterns in system design limit their awareness and control over privacy risks, leading to unintended sensitive disclosures.

초록

The study examined how users navigate disclosure risks and benefits when using large language model (LLM)-based conversational agents (CAs) like ChatGPT.
The researchers first analyzed a dataset of real-world ChatGPT conversations to understand users' sensitive disclosure behaviors (RQ1). They found that users disclosed various types of personally identifiable information (PII) about themselves and others, suggesting interdependent privacy issues. The researchers developed a typology of disclosure scenarios based on context, topic, purpose, and prompt strategy.
To further explore users' perspectives (RQ2, RQ3), the researchers conducted semi-structured interviews with 19 LLM-based CA users. They found that users' disclosure intentions were primarily affected by their perceived capabilities of the AI, the convenience of operation, and their assessment of data sensitivity. Many users felt resigned to the idea that their data was already accessible elsewhere, and thus the marginal risk of sharing with CAs was low.
However, users also expressed concerns about data misuse by institutions, others finding out about their CA use, and idea theft. The interviews revealed that users had varied and often flawed mental models about how LLMs work, which impacted their ability to reason about privacy risks. Additionally, the human-like interactions encouraged more sensitive disclosures, complicating users' ability to navigate the trade-offs.
The findings suggest the need for design interventions to improve user awareness, perceived control, and actual control over privacy when using LLM-based systems. Addressing the fundamental misunderstandings and dark patterns in the current systems will also require regulatory and structural changes.

통계

"I'm doing the same risk by using the app like Instagram or Facebook." (P1)
"Telling ChatGPT I live in [city name redacted], it's kind of like, saying I live on the earth." (P10)
"He asked me to talk to him about my brother. It's like a full conversation. He wanted to know everything." (P16)

인용구

"I hope they (my professors) will never know I used AI to do that (write emails)." (P8)
"I don't know if ChatGPT uses it (the fiction that I wrote) as inspiration for other people, or spits it out as it wrote it itself instead of me." (P2)

핵심 통찰 요약

"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents

by Zhiping Zhan... 게시일 arxiv.org 04-03-2024

https://arxiv.org/pdf/2309.11653.pdf

더 깊은 질문

How can we design LLM-based systems to better align with users' mental models and privacy preferences, without compromising the systems' functionality and convenience?

To design LLM-based systems that align with users' mental models and privacy preferences, several strategies can be implemented:

Transparency and Control: Provide users with clear and transparent information about how their data is used and stored by the system. Allow users to have control over their data, including the ability to opt-out of data collection for model training.

Privacy-Enhancing Features: Implement privacy-enhancing features such as data encryption, differential privacy, and data minimization techniques to reduce the risk of data exposure and leakage.

User Education: Educate users about the capabilities and limitations of LLM-based systems, as well as the potential privacy risks associated with sharing sensitive information. This can help users make informed decisions about what data to disclose.

Granular Privacy Settings: Offer users granular privacy settings that allow them to customize their privacy preferences based on the type of information being shared and the intended use of the data.

Ethical Design Practices: Incorporate ethical design principles into the development of LLM-based systems, ensuring that user privacy and data protection are prioritized throughout the design process.

By implementing these strategies, LLM-based systems can better align with users' mental models and privacy preferences while maintaining functionality and convenience.

What are the broader societal implications of the interdependent privacy issues arising from the use of LLM-based CAs, and how can we address them through policy and regulation?

The interdependent privacy issues arising from the use of LLM-based CAs have significant societal implications, including:

Data Security Concerns: The potential for data breaches and misuse of personal information can lead to identity theft, financial fraud, and other forms of cybercrime.

Ethical Considerations: The use of LLM-based CAs raises ethical questions about data privacy, consent, and the responsible use of AI technologies.

Trust and Transparency: Lack of transparency in how user data is collected, stored, and used can erode trust in technology companies and AI systems.

To address these issues through policy and regulation, the following steps can be taken:

Data Protection Laws: Implement and enforce robust data protection laws that govern the collection, storage, and use of personal data by AI systems.

Ethical Guidelines: Develop ethical guidelines and standards for the use of AI technologies, including LLM-based CAs, to ensure responsible and ethical practices.

Transparency Requirements: Require companies to be transparent about their data practices and provide users with clear information about how their data is used.

User Rights: Empower users with rights to access, control, and delete their personal data from AI systems, including the right to opt-out of data collection for training purposes.

By implementing these policy and regulatory measures, we can mitigate the societal implications of interdependent privacy issues associated with LLM-based CAs and ensure the responsible and ethical use of AI technologies.

Given the fundamental challenges in fixing flawed mental models, what other paradigm shifts in technology, law, and society are needed to protect user privacy in the age of powerful AI assistants?

In addition to addressing flawed mental models, several paradigm shifts are needed in technology, law, and society to protect user privacy in the age of powerful AI assistants:

Technological Innovation: Develop privacy-preserving technologies such as federated learning, homomorphic encryption, and secure multi-party computation to enable data sharing and collaboration without compromising user privacy.

Regulatory Frameworks: Implement comprehensive data protection regulations that govern the use of AI technologies and ensure that user privacy rights are upheld.

Ethical AI Development: Promote ethical AI development practices that prioritize user privacy, fairness, transparency, and accountability in the design and deployment of AI systems.

User Empowerment: Empower users with greater control over their data through tools and mechanisms that allow them to manage and protect their privacy in the digital age.

Public Awareness and Education: Increase public awareness and education about AI technologies, data privacy risks, and best practices for protecting personal information online.

By embracing these paradigm shifts and working collaboratively across technology, law, and society, we can create a more privacy-respecting and user-centric environment in the era of powerful AI assistants.