Addressing Risks of Social Misattributions in Large Language Models

Core Concepts

Addressing the risks of social misattributions in Large Language Models (LLMs) is crucial for promoting ethically responsible development and use of AI technology.

Abstract

Abstract: HCXAI advocates for integrating social aspects into AI explanations. The Social Transparency (ST) framework aims to make AI systems' socio-organizational context accessible. Extending the ST framework to address social misattributions in LLMs is proposed. Introduction: AI systems are viewed under a socio-technical lens. HCXAI focuses on explaining AI with a social component. Social Transparency can mitigate risks from social misattributions of LLMs. LLMs, functions, and role-playing: LLMs perform context-aware text generation. They serve technical and socio-functions, including role-playing. Users' social attributions may lead to risks and incorrect perceptions. Social misattributions of LLMs: Users may assign inappropriate roles and personas to LLMs. Incorrect attributions can lead to unwarranted trust and harmful consequences. An example from the mental health domain illustrates the risks. Adapting Social Transparency to address social misattributions: Proposing a 5W model to identify justified and user-assigned social attributions. Developing taxonomies of social attributions and implementing detection techniques are suggested. References: Various studies and works related to AI, LLMs, and social attributions.

Stats

LLMs are essentially role-play devices, simulating roles and personas. ChatGPT-3.5 prescribed medications to individuals with anxiety or depression. Users may hold unwarranted expectations about LLM capabilities.

Quotes

"LLMs can be sometimes attributed with certain personas, leading to social misattributions." "Fostering warranted trust in AI systems is a desideratum of human-AI interactions." "Users' expectations will be disappointed and their trust unwarranted if LLMs lack the capabilities necessary to fulfill assigned roles and personas."

Key Insights Distilled From

Addressing Social Misattributions of Large Language Models

by Andrea Ferra... at arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17873.pdf

Deeper Inquiries

How can the 5W model be practically implemented to address social misattributions in LLMs?

The 5W model, an extension of the Social Transparency framework, can be practically implemented to address social misattributions in Large Language Models (LLMs) by incorporating an additional 'W-question' focused on identifying justified social attributions for LLMs in a given context and the attributions assigned by users. Practical implementation involves developing a taxonomy of social attributions that outlines appropriate and inappropriate roles and personas for LLM-based applications. Organizations should provide examples of these attributions to guide users in assigning roles and personas accurately. This taxonomy should be developed through participatory design, involving experts from various fields like epistemology, psychology, sociology of AI, and human-computer interaction. Additionally, techniques can be implemented to detect and prevent social misattributions dynamically during user interactions with LLMs. Algorithms can be designed to identify potential misattributions in conversations and provide warnings to users when inappropriate attributions are detected. These warnings can refer users to the taxonomy of social attributions for further clarification.

What ethical considerations should be taken into account when developing taxonomies of social attributions for AI systems?

When developing taxonomies of social attributions for AI systems, several ethical considerations must be taken into account to ensure responsible and ethical use of AI technology. Transparency and Accountability: The taxonomy should be transparent, clearly outlining the roles and personas that AI systems can and cannot fulfill. Users should be informed about the limitations of AI systems to prevent unwarranted trust and potential harm. Fairness and Bias: The development of taxonomies should consider fairness and avoid perpetuating biases in assigning roles and personas. Care must be taken to ensure that attributions are not discriminatory or harmful to certain groups. User Consent and Autonomy: Users should have the autonomy to choose the roles and personas they assign to AI systems. Consent should be obtained before attributions are made, and users should be informed about the implications of their choices. Data Privacy and Security: The collection and use of data to inform social attributions should adhere to data privacy regulations. Users' personal information should be protected, and data security measures should be in place to prevent misuse. Accountability and Oversight: There should be mechanisms in place to hold organizations accountable for the attributions assigned to AI systems. Oversight and governance structures should ensure compliance with ethical guidelines and standards.

How can the risks of social misattributions in AI systems be mitigated beyond the proposed methodologies?

Beyond the proposed methodologies of developing taxonomies and implementing detection algorithms, additional strategies can be employed to mitigate the risks of social misattributions in AI systems: Continuous Monitoring and Evaluation: Regular monitoring and evaluation of AI systems' interactions with users can help identify and address instances of social misattributions in real-time. Feedback mechanisms can be implemented to gather user input and improve system performance. User Education and Awareness: Educating users about the capabilities and limitations of AI systems can help prevent misattributions. Providing clear guidelines and instructions on how to interact with AI systems can enhance user awareness and reduce the likelihood of inappropriate attributions. Interdisciplinary Collaboration: Collaboration between experts from diverse fields, including ethics, psychology, sociology, and AI, can provide valuable insights into the social implications of AI systems. Interdisciplinary teams can work together to develop comprehensive strategies for mitigating social misattributions. Ethical Impact Assessments: Conducting ethical impact assessments before deploying AI systems can help identify potential risks and ethical concerns, including social misattributions. These assessments can inform decision-making and guide the development of mitigation strategies. Regulatory Frameworks: Implementing regulatory frameworks that govern the use of AI systems, including guidelines on social attributions, can provide a structured approach to addressing ethical issues. Compliance with regulations can help ensure responsible and ethical use of AI technology.