How Microsoft Scales Testing and Safety for Generative AI with Sarah Bird - #691
Digest
Sarah Bird, Chief Product Officer of Responsible AI at Microsoft, joins the podcast to discuss the evolving landscape of responsible AI in the age of generative AI. She highlights the significant shift from traditional AI to generative AI, emphasizing the need for new tools and techniques to address the unique risks and challenges presented by this technology. Bird outlines a taxonomy of risks, including adversarial inputs, errors like hallucinations and omissions, the potential for generating harmful content, and the need for clear user understanding of the system's limitations. She emphasizes the importance of a layered approach to safety, including model-level safety, auxiliary safety systems, and robust testing and evaluation processes. Bird discusses the importance of red teaming, particularly for novel or high-risk applications, and advocates for the use of the NIST AI Risk Management Framework as a guide for organizations. She highlights the importance of governance, both at the leadership level and through a structured review and release process, to ensure responsible AI development and deployment. Bird concludes by expressing her excitement about the potential of generative AI to advance responsible AI practices, particularly through the development of automated testing and evaluation systems. She acknowledges the rapid pace of technological advancement and the difficulty in predicting the future, but emphasizes the ongoing need for robust testing and evaluation to ensure the safe and responsible use of generative AI.
Outlines
Introduction and Sponsor Message
This Chapter introduces the podcast and its host, Sam Charrington, and welcomes Sarah Bird, Chief Product Officer of Responsible AI at Microsoft. It also acknowledges the sponsorship of the episode by Microsoft.
The Evolving Landscape of Responsible AI
This Chapter delves into the significant shift from traditional AI to generative AI and the implications for responsible AI practices. Sarah Bird discusses the challenges of operationalizing responsible AI in this new context, highlighting the need for new tools and techniques to address the unique risks and challenges presented by generative AI.
Risks and Challenges of Generative AI
This Chapter explores the various risks and challenges associated with generative AI, including adversarial inputs, errors like hallucinations and omissions, the potential for generating harmful content, and the need for clear user understanding of the system's limitations. Sarah Bird discusses the importance of a layered approach to safety, including model-level safety, auxiliary safety systems, and robust testing and evaluation processes.
Shifting Focus from Fairness and Bias to Security
This Chapter examines the relationship between fairness and bias, and the emerging security concerns associated with generative AI. Sarah Bird discusses the importance of addressing both types of concerns, highlighting the need for a comprehensive approach to responsible AI.
Learning from Public Generative AI Failures
This Chapter analyzes the lessons learned from public generative AI failures, including Microsoft's Tay and Bing Chat, and Google's recent pizza glue fiasco. Sarah Bird emphasizes the importance of continuous learning and calculated risk-taking in this rapidly evolving field.
System Architecture for Rapid Response
This Chapter explores the system architecture necessary for rapid response to generative AI failures. Sarah Bird discusses the importance of defense in depth, layered security, and robust testing and evaluation processes to enable quick interventions and mitigate potential risks.
Conveying Expectations and User Experience
This Chapter focuses on the importance of conveying the right expectations to users and crafting a user experience that acknowledges the probabilistic nature of generative AI systems. Sarah Bird discusses the need for clear communication about potential errors and limitations, and explores opportunities for integrating user controls to influence the system's behavior.
Testing and Evaluation Responsibilities
This Chapter examines the shifting responsibilities for testing and evaluation between model publishers and model users. Sarah Bird discusses the importance of testing for capabilities, dangerous capabilities, and alignment, and highlights the need for tailored testing approaches at both the model and application levels.
The Importance of Holistic Testing and Team Collaboration
This Chapter emphasizes the importance of holistic testing, encompassing quality, adversarial, and safety tests, and the need for collaboration between different teams with specialized expertise. Sarah Bird discusses the challenges of balancing trade-offs between quality and safety, and the importance of a comprehensive view of the system's performance.
Keywords
Generative AI
Generative AI refers to a type of artificial intelligence that can create new content, such as text, images, audio, video, code, and more. It learns patterns from existing data and uses them to generate novel outputs. Examples include ChatGPT, DALL-E, and Stable Diffusion.
Responsible AI
Responsible AI encompasses the principles and practices that ensure the ethical, fair, and safe development and deployment of AI systems. It involves considering the potential impacts of AI on society, mitigating bias, ensuring transparency, and promoting accountability.
Hallucination
In the context of generative AI, hallucination refers to the phenomenon where a model generates outputs that are not grounded in the real world or the provided context. It can occur when the model makes incorrect inferences or extrapolates beyond its training data.
Adversarial Inputs
Adversarial inputs are designed to intentionally mislead or manipulate AI systems. They can take the form of malicious prompts, data poisoning, or other techniques aimed at causing the model to produce unintended or harmful outputs.
Prompt Injection Attacks
Prompt injection attacks are a type of adversarial input where malicious code or instructions are injected into a prompt, causing the model to execute unintended actions or generate harmful content.
Red Teaming
Red teaming is a security testing technique where a team of experts simulates attacks or exploits to identify vulnerabilities and weaknesses in a system. It is often used to assess the effectiveness of security measures and identify potential risks.
NIST AI Risk Management Framework
The NIST AI Risk Management Framework is a comprehensive guide for organizations to identify, assess, and manage the risks associated with AI systems. It provides a structured approach to risk identification, measurement, management, and governance.
Reinforcement Learning with Human Feedback (RLHF)
RLHF is a technique used to train AI models by providing human feedback on their outputs. It involves rewarding desired behaviors and penalizing undesirable ones, helping to align the model's behavior with human values and preferences.
Safety Evaluation
Safety evaluation refers to the process of assessing the safety and reliability of AI systems. It involves testing for potential risks, vulnerabilities, and unintended consequences, and ensuring that the system operates within acceptable safety boundaries.
Data Augmentation
Data augmentation is a technique used to increase the size and diversity of training datasets by generating synthetic data. It can help to improve the model's performance and robustness, particularly in cases where real-world data is limited.
Q&A
What are some of the key risks and challenges associated with generative AI?
Generative AI presents a range of risks, including adversarial inputs, errors like hallucinations and omissions, the potential for generating harmful content, and the need for clear user understanding of the system's limitations. These risks require a layered approach to safety, including model-level safety, auxiliary safety systems, and robust testing and evaluation processes.
How can organizations effectively manage the risks of generative AI?
Organizations can manage the risks of generative AI by adopting a comprehensive approach that includes risk identification, measurement, management, and governance. The NIST AI Risk Management Framework provides a useful guide for this process. Key elements include red teaming, particularly for novel or high-risk applications, and robust testing and evaluation systems to ensure the safe and responsible use of generative AI.
What are the key differences in testing and evaluation responsibilities between model publishers and model users?
Model publishers are responsible for testing for capabilities, dangerous capabilities, and alignment at the model level. Model users need to tailor their testing approaches to the specific application, ensuring that the application behaves as intended and cannot be misused. Both model publishers and users need to invest in robust testing and evaluation systems to ensure the safety and reliability of their AI systems.
How can generative AI be used to advance responsible AI practices?
Generative AI offers significant opportunities to advance responsible AI practices, particularly through the development of automated testing and evaluation systems. These systems can help to identify and mitigate risks more effectively, enabling organizations to develop and deploy AI systems more responsibly.
What are some of the key considerations for designing user interfaces for generative AI systems?
User interfaces for generative AI systems need to clearly convey the system's capabilities and limitations, acknowledging the probabilistic nature of the technology. Users should be informed about potential errors and given some control over the system's behavior to ensure that it aligns with their intended use.
What are the implications of the rapid pace of technological advancement for responsible AI practices?
The rapid pace of technological advancement presents challenges for responsible AI practices, as organizations need to constantly adapt to new risks and technologies. Robust governance frameworks, continuous learning, and a commitment to ongoing testing and evaluation are essential to ensure the safe and responsible use of AI.
How can organizations ensure that their AI systems are governed responsibly?
Responsible AI governance involves establishing clear leadership, implementing structured review and release processes, and fostering a culture of ethical AI development and deployment. This requires a combination of leadership commitment, expert review, and ongoing monitoring to ensure that AI systems are used safely and ethically.
What are some of the key takeaways from the conversation with Sarah Bird?
The conversation with Sarah Bird highlights the importance of a comprehensive approach to responsible AI, encompassing risk identification, measurement, management, and governance. It emphasizes the need for robust testing and evaluation systems, particularly in the context of generative AI, and the importance of continuous learning and adaptation to ensure the safe and responsible use of this powerful technology.
What are some of the future directions for responsible AI in the age of generative AI?
The future of responsible AI in the age of generative AI is likely to involve further advancements in automated testing and evaluation systems, as well as the development of new techniques for mitigating risks and ensuring alignment with human values. The rapid pace of technological advancement will continue to present challenges, but also opportunities for innovation in responsible AI practices.
What are some of the key considerations for organizations building generative AI applications?
Organizations building generative AI applications need to prioritize responsible AI practices from the outset, including risk assessment, robust testing and evaluation, and a strong governance framework. They should also consider the implications of their applications for society and ensure that they are developed and deployed in a way that is ethical, fair, and safe.
Show Notes
Today, we're joined by Sarah Bird, chief product officer of responsible AI at Microsoft. We discuss the testing and evaluation techniques Microsoft applies to ensure safe deployment and use of generative AI, large language models, and image generation. In our conversation, we explore the unique risks and challenges presented by generative AI, the balance between fairness and security concerns, the application of adaptive and layered defense strategies for rapid response to unforeseen AI behaviors, the importance of automated AI safety testing and evaluation alongside human judgment, and the implementation of red teaming and governance. Sarah also shares learnings from Microsoft's ‘Tay’ and ‘Bing Chat’ incidents along with her thoughts on the rapidly evolving GenAI landscape.
The complete show notes for this episode can be found at https://twimlai.com/go/691.
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