autogen/TRANSPARENCY_FAQS.md
What are the limitations of AutoGen? How can users minimize the impact of AutoGen’s limitations when using the system? AutoGen relies on existing LLMs. Experimenting with AutoGen would retain common limitations of large language models; including:  - Data Biases: Large language models, trained on extensive data, can inadvertently carry biases present in the source data. Consequently, the models may generate outputs that could be potentially biased or unfair. -	Lack of Contextual Understanding: Despite their impressive capabilities in language understanding and generation, these models exhibit limited real-world understanding, resulting in potential inaccuracies or nonsensical responses. -	Lack of Transparency: Due to the complexity and size, large language models can act as `black boxes,' making it difficult to comprehend the rationale behind specific outputs or decisions. -	Content Harms: There are various types of content harms that large language models can cause. It is important to be aware of them when using these models, and to take actions to prevent them. It is recommended to leverage various content moderation services provided by different companies and institutions. -	Inaccurate or ungrounded content: It is important to be aware and cautious not to entirely rely on a given language model for critical decisions or information that might have deep impact as it is not obvious how to prevent these models to fabricate content without high authority input sources. -	Potential for Misuse: Without suitable safeguards, there is a risk that these models could be maliciously used for generating disinformation or harmful content.   Additionally, AutoGen’s multi-agent framework may amplify or introduce additional risks, such as: -	Privacy and Data Protection: The framework allows for human participation in conversations between agents. It is important to ensure that user data and conversations are protected and that developers use appropriate measures to safeguard privacy. -	Accountability and Transparency: The framework involves multiple agents conversing and collaborating, it is important to establish clear accountability and transparency mechanisms. Users should be able to understand and trace the decision-making process of the agents involved in order to ensure accountability and address any potential issues or biases. -	Trust and reliance: The framework leverages human understanding and intelligence while providing automation through conversations between agents. It is important to consider the impact of this interaction on user experience, trust, and reliance on AI systems. Clear communication and user education about the capabilities and limitations of the system will be essential. -	Security & unintended consequences: The use of multi-agent conversations and automation in complex tasks may have unintended consequences. Especially, allowing LLM agents to make changes in external environments through code execution or function calls, such as install packages, could pose significant risks. Developers should carefully consider the potential risks and ensure that appropriate safeguards are in place to prevent harm or negative outcomes, including keeping a human in the loop for decision making. 