Slashdot: People Should Know About the ‘Beliefs’ LLMs Form About Them While Conversing

May 24, 2025

—

Source URL: https://slashdot.org/story/25/05/24/1946203/people-should-know-about-the-beliefs-llms-form-about-them-while-conversing
Source: Slashdot
Title: People Should Know About the ‘Beliefs’ LLMs Form About Them While Conversing

Feedly Summary:

AI Summary and Description: Yes

Summary: The text discusses the implications of using large language models (LLMs) like Llama that exhibit human-like biases based on user interactions. This raises critical policy and ethical issues related to data privacy, security, and the inherent stereotypes present in model outputs.

Detailed Description: The article highlights several major points around the use of LLMs and their implications for security and privacy professionals, particularly in how AI systems gauge individual user characteristics during interactions.

– **Observability Tool:** Researchers at Harvard have developed a dashboard tool using observability techniques to monitor how Llama predicts a user’s attributes (age, wealth, education, gender) based on conversation context.

– **Stereotypes and Biases:** The LLM outputs suggested varying gift items based on perceived socioeconomic status, illustrating the presence of stereotypes. For example:
– When assumed to be young and female, it recommended luxury baby products.
– When the context changed to a working-class individual, it suggested practical gifts.

– **Impact on Policy and Ethical Considerations:** The article suggests that the ability to see model behaviors could shine a light on disparities that go unnoticed and calls for discussion around policy implications:
– The opacity of AI decision-making raises challenges in accepting models’ outputs, impacting accountability and trust.
– There are concerns about LLMs gathering user information through conversations, which poses significant privacy risks.

– **Data Collection Concerns:** With LLMs collecting unguarded personal information through conversational contexts without adequate oversight, there are calls for a review of policies and practices to ensure user privacy and data security.

This discussion not only emphasizes the technical aspects of LLMs but also raises practical implications for security and compliance professionals to consider in their governance frameworks to address AI-related privacy and policy issues.

1 2 24 3 4 5 a account accountability Act actions AI AI systems alt and Arch art as attribute based Behavior Bi bias biases board by C CERN challenges CI class co Col compliance compliance professionals concerns Context conversation critical D dashboard data data collection data privacy data security de decision decision-making DoT dual e education end ethical ethical considerations ethical issues for framework frameworks g Gen Go governance governance framework governance frameworks H high Highlight HR http HTTPS human implications implications for security in information inter interaction interactions io issue ite J k l language language model language models large large language model large language models Large Language Models (LLMs) led Li llama llm llms lm M making man Mode model model behavior model behaviors model outputs models Monitor N no nomic o observability observability tool oE of on only ory oS out output Outputs over oversight personal information point policies policy policy implications practical implications pre privacy privacy professionals privacy risk privacy risks product products professionals Q R Raise RCE red research researchers Risk risks Ro RSA Rust s search sec security security and compliance side Sig size SoC source SSE stereotypes system systems T tech techniques text the to tool Tor TP trust type US use user user information user interaction user interactions user privacy V Wi x