We cordially invite domain experts from various fields to join the OpenAI Red Teaming Network in our efforts to improve the safety of OpenAI’s models. Red teaming, an essential part of our deployment process, involves rigorously evaluating and testing our AI models for potentially harmful capabilities. As we launch a more formalized initiative, we aim to deepen and broaden our collaborations with outside experts to enhance the safety of our models. By joining our network, you become part of a community of trusted experts who will be called upon to red team and evaluate our models at different stages of development, contributing your diverse expertise and perspectives to the assessment of AI systems. Apply now to be a crucial part of shaping the development of safer AI technologies and policies.
Understanding OpenAI and the Red Teaming Network
What is the OpenAI Red Teaming Network?
The OpenAI Red Teaming Network is a community of trusted and experienced experts who collaborate with OpenAI to improve the safety of their AI models. Red teaming is an essential part of OpenAI’s deployment process, and it involves rigorous evaluation and testing of AI systems by external experts. The network is designed to deepen and broaden collaborations with outside experts, enabling continuous input and making red teaming a more iterative process.
The Role of Red Teaming in OpenAI’s Deployment Process
Red teaming plays a crucial role in OpenAI’s deployment process. It involves testing AI systems and models for vulnerabilities, bias, and potentially harmful capabilities. The aim is to identify and mitigate risks before the models are released to the public. Over the years, OpenAI has expanded its red teaming efforts from internal adversarial testing to working with external experts. This collaborative approach helps develop domain-specific taxonomies of risk and enhances the safety of OpenAI’s models.
How the OpenAI Red Teaming Network is Expanding
OpenAI is expanding the OpenAI Red Teaming Network to include a wider range of experts and institutions. The network, which previously focused on one-off engagements and major model deployments, is now aiming to establish a more formal and continuous collaboration. The goal is to engage with individual experts, research institutions, and civil society organizations to gather diverse perspectives and ensure the safety of AI models.
The Influence of Individual Experts and Institutions in OpenAI
Individual experts and institutions play a significant role in shaping OpenAI’s approach to AI safety and policy. Their expertise, knowledge, and insights contribute to the evaluation and improvement of AI systems. OpenAI values the input and influence of experts, as it helps them make informed decisions regarding the risks associated with AI deployment. Collaborating with external partners enhances accountability and promotes responsible AI practices.
Structure and Function of the Red Teaming Network
The OpenAI Red Teaming Network operates as a community of trusted experts who are called upon at different stages of model and product development. Members of the network are selected based on their expertise, and their involvement in red teaming campaigns may vary depending on the specific project. The scope of participation for network members is flexible, with time commitments ranging from as few as 5-10 hours per year. Members also have the opportunity to interact with each other, exchange ideas, and discuss general red teaming practices.
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The Role of Network Members in Different Stages of Model and Product Development
Network members have an integral role in red teaming at various stages of model and product development. They contribute their expertise to assess the risks and impacts of AI systems. Their insights and evaluations help identify potential vulnerabilities and ensure the safety and reliability of the models. Whether it’s testing a new model or assessing an existing one, network members provide valuable feedback that aids in the continuous improvement of AI systems.
The Scope of Participation for Red Teaming Network Members
Network members have the opportunity to participate in red teaming projects commissioned by OpenAI. Their involvement may range from testing new models to evaluating specific areas of interest in already deployed models. OpenAI selects network members based on their fit with a particular project, ensuring a diverse range of perspectives and expertise. While not every member will be involved in every project, even a small time commitment of 5-10 hours per year can make a valuable contribution.
The Interaction of Members within and outside of OpenAI-initiated Red Teaming Campaigns
In addition to their involvement in OpenAI-initiated red teaming campaigns, network members have the opportunity to engage with each other and share insights on general red teaming practices. This interaction fosters collaboration, knowledge sharing, and continuous learning within the community. Members can exchange ideas, discuss findings, and contribute to the development of best practices in red teaming. OpenAI encourages a supportive and collaborative environment that enriches the collective expertise of the network.
Why Join the OpenAI Red Teaming Network?
Joining the OpenAI Red Teaming Network offers a unique opportunity to contribute to the development of safer AI technologies and policies. As a member of the network, you become part of a select group of subject matter experts who assess and evaluate AI models and systems. Your expertise and insights shape the direction of AI safety efforts and help mitigate potential risks. By joining the network, you can have a significant impact on the responsible deployment of AI and its influence on society.
The Impact of Joining the OpenAI Red Teaming Network
Joining the OpenAI Red Teaming Network allows you to directly influence the safety and policy considerations of AI systems. Your contributions as a network member help identify potential vulnerabilities, biases, and harmful capabilities within AI models. By evaluating and red teaming these models, you contribute to their continuous improvement and enhance the overall safety and reliability of AI technology. Your expertise and insights are invaluable in shaping the future of AI systems.
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The Opportunities as Part of the OpenAI Red Teaming Network
Being part of the OpenAI Red Teaming Network provides various opportunities for professional growth and knowledge sharing. You have the chance to collaborate with other experts, institutions, and civil society organizations, fostering connections and expanding your network. Additionally, you can engage in discussions, exchanges, and research projects related to AI safety, further enhancing your expertise in this critical field. The network offers a platform for continuous learning, collaboration, and advancement in AI safety practices.
The Influence You Can Exert on AI Systems Safety and Policy
As a member of the OpenAI Red Teaming Network, you have the power to influence AI systems’ safety and policy considerations. Your expertise and evaluations shape the decisions made regarding AI model deployment, risk assessment, and mitigation. By providing valuable insights and assessments, you contribute to creating AI systems that prioritize safety, accountability, and ethical considerations. Your influence helps guide the development of AI technology towards responsible and beneficial outcomes for humanity.
The Need for Diverse Expertise in the Network
Assessing AI systems requires a diverse range of expertise and perspectives. OpenAI recognizes the importance of different domains in understanding and evaluating AI models. When inviting experts to join the network, priority is given to those with expertise in various fields. OpenAI aims to gather individuals from different backgrounds, disciplines, and geographic locations to ensure a holistic evaluation of AI systems. This diversity of expertise enhances the effectiveness and comprehensiveness of red teaming efforts.
Potential Domains of Expertise Sought for the Network
OpenAI invites experts from various domains to join the Red Teaming Network. While the list is not exhaustive, some domains of expertise that OpenAI is interested in include cognitive science, chemistry, biology, physics, computer science, steganography, political science, psychology, persuasion, economics, anthropology, sociology, human-computer interaction, fairness and bias, alignment, education, healthcare, law, child safety, cybersecurity, finance, mis/disinformation, political use, privacy, biometrics, languages and linguistics. The network values a wide range of expertise to ensure comprehensive evaluations and assessments of AI systems.
The Value of Prior Experience with AI Systems or Language Models
While prior experience with AI systems or language models is not required to join the OpenAI Red Teaming Network, it can be beneficial. Familiarity with AI technologies and language models allows for a deeper understanding of their potential risks and capabilities. However, OpenAI values the willingness to engage and bring unique perspectives to the assessment of AI impacts above all else. Expertise from diverse domains, even without prior AI experience, adds value and enriches the evaluation process.
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Compensation Arrangement for OpenAI Red Teaming Network Members
All members of the OpenAI Red Teaming Network are compensated for their contributions when participating in a red teaming project. OpenAI acknowledges the expertise and time commitment required for effective evaluations and ensures that members are appropriately compensated for their valuable contributions. Compensation arrangements are discussed and agreed upon individually, based on the specific nature and scope of each project.
Understanding Non-Disclosure Agreements (NDAs) within the Context of the Network
The OpenAI Red Teaming Network recognizes the sensitivity and confidentiality of its projects. While membership in the network does not restrict members from publishing their research or pursuing other opportunities, it’s important to note that red teaming projects often require Non-Disclosure Agreements (NDAs). The purpose of NDAs is to maintain the privacy and confidentiality of the evaluation process, protecting the integrity of the project and the AI systems being assessed. Members should consider the implications of NDAs on their freedom to publish research or pursue other collaborative opportunities.
How Involvement in the Network Affects Your Freedom to Publish Research or Pursue Other Opportunities
Participating in the OpenAI Red Teaming Network does not limit your freedom to publish research or pursue other opportunities. While red teaming projects may require NDAs, they typically do not restrict your ability to share research findings or collaborate with others. OpenAI encourages the publication of red teaming findings through System Cards and blog posts, contributing to transparency and knowledge sharing. It is essential to consider the specific terms of NDAs and confidential agreements on a project-by-project basis, but overall, membership in the network does not hinder your freedom to publish or explore other research opportunities.
How to Apply to the Network
To become a part of the OpenAI Red Teaming Network, you can apply using the application process outlined by OpenAI. The application process involves providing your expertise, domain knowledge, and relevant background information. OpenAI values diversity and encourages individuals from various geographic locations and domains to apply. The application period is open until December 1, 2023. To begin the application process, visit the OpenAI website and follow the instructions provided.
Contact Details for Any Enquiries about the Network or the Application Process
If you have any questions or inquiries regarding the OpenAI Red Teaming Network or the application process, you can contact OpenAI at oai-redteam@openai.com. The OpenAI team is available to provide assistance, clarifications, and guidance throughout the application process. Feel free to reach out to them for support or any additional information you may require.
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FAQs about Joining the Network
Understanding What Being a Part of the Network Entails
Being part of the OpenAI Red Teaming Network involves participating in red teaming projects to evaluate and assess AI models. Members may be contacted about opportunities to test new models or evaluate specific areas of interest in existing models. Work conducted as part of the network is conducted under a non-disclosure agreement (NDA), ensuring confidentiality. Members are compensated for their time spent on red teaming projects.
The Time Commitment for Participating in the Network
The time commitment for participating in the network is flexible and adjustable based on individual schedules. OpenAI understands that not every member will be available for every opportunity. Selections for red teaming projects are made based on the right fit for a particular project, and new perspectives are emphasized in subsequent campaigns. Even a time commitment as little as 5 hours per year can contribute valuable insights and assessments.
Criteria for Selection as Network Members
OpenAI considers various criteria when selecting network members. Some of the criteria they look for include demonstrated expertise or experience in a domain relevant to red teaming, passion for improving AI safety, absence of conflicts of interest, diverse backgrounds, geographic representation, and fluency in multiple languages. Technical ability, while not required, is also considered. OpenAI values diverse perspectives and expertise to ensure comprehensive evaluations and assessments.
Opportunities for Red Teaming in New Models
Network members have the opportunity to participate in red teaming projects for new models developed by OpenAI. The selection process is based on the right fit for a particular project, ensuring members with relevant expertise are involved. While not every network member will be asked to red team every new model, the goal is to include a diverse range of perspectives in the red teaming process and continuously enhance the safety and reliability of AI systems.
How Applicants Are Notified of Their Acceptance
OpenAI selects network members on a rolling basis, and applicants will be notified of their acceptance once the evaluation process is complete. The selection process considers the expertise and fit of applicants with the specific requirements of red teaming projects. Applicants can expect to receive notifications regarding their acceptance within a reasonable timeframe after the application period ends.
Future Opportunities to Become Part of the Network
OpenAI continually evaluates and re-evaluates the need for network members. While the current application period has a deadline, future opportunities to apply and become part of the OpenAI Red Teaming Network may arise. It’s important to stay updated with OpenAI’s announcements and guidelines for any future opportunities or calls for applications. Keep an eye on OpenAI’s official channels and communications to stay informed about future opportunities to join the network.
Collaborative Safety Opportunities Beyond the Network
The OpenAI Red Teaming Network is not the only collaborative opportunity available to contribute to AI safety. OpenAI provides additional avenues for individuals and organizations to contribute to AI safety and responsible deployment. For instance, creating and conducting safety evaluations on AI systems is encouraged. OpenAI’s open-source Evals repository offers templates and sample methods for conducting safety evaluations, enabling researchers to contribute their findings to the broader AI community. OpenAI also offers the Researcher Access Program, which supports researchers studying areas related to responsible AI deployment and mitigating associated risks.
Creating and Conducting Safety Evaluations on AI Systems
Creating and conducting safety evaluations on AI systems is a valuable contribution to AI safety. OpenAI encourages researchers to evaluate AI behaviors from various angles using methods such as Q&A tests, simulations, and more. By thoroughly evaluating AI systems, researchers can identify potential risks, biases, and vulnerabilities. These evaluations play a crucial role in improving the safety and reliability of AI technology.
The Role of OpenAI’s Open-Source Evals Repository for Safety Evaluations
OpenAI’s open-source Evals repository is a valuable resource for those conducting safety evaluations on AI systems. The Evals repository provides user-friendly templates and sample methods to jump-start the evaluation process. Researchers can utilize these resources to structure their evaluations and ensure a standardized approach. By contributing their evaluations to the open-source Evals repository, researchers enrich the collective knowledge and empower the broader AI community.
Sample Evaluations Developed by OpenAI for Evaluating AI Behaviors
OpenAI has developed sample evaluations to assess various AI behaviors. These evaluations cover areas such as persuasion, steganography (hidden messaging), and coordination between AI systems. They provide concrete examples of how to evaluate AI systems from different perspectives. By using these sample evaluations as inspiration, researchers can design their own assessments, contribute to the Evals repository, and promote safety in AI systems.
Contributing Your Evaluation to the Open-Source Evals Repo for Use by the Broader AI Community
OpenAI encourages researchers to contribute their evaluations to the open-source Evals repository. By sharing their findings, researchers can contribute to the broader AI community’s understanding of AI behaviors and safety considerations. The repository serves as a valuable resource for the development of best practices, knowledge sharing, and collective efforts to enhance the safety and reliability of AI systems.
Applying to the Researcher Access Program
OpenAI’s Researcher Access Program offers an additional opportunity for researchers to study areas related to responsible AI deployment and mitigation of associated risks. This program provides researchers with credits to support their research using OpenAI products. By participating in the Researcher Access Program, researchers can explore AI safety, policy, and ethical considerations in more depth, contributing to the responsible development and deployment of AI technology.
The Impact of Red Teaming on AI Safety
Red teaming plays a crucial role in ensuring AI safety. By rigorously testing and evaluating AI systems, red teaming helps identify potential risks, biases, and vulnerabilities. This process allows for the mitigation of harmful capabilities before the models are deployed. Red teaming acts as a complementary approach to external governance practices and enhances the reliability and safety of AI systems.
The Focus Areas for Red Teaming in OpenAI
In OpenAI, red teaming focuses on evaluating AI systems for risks, biases, and potentially harmful capabilities. The goal is to thoroughly assess the impact and behavior of AI models to ensure their safety, reliability, and ethical use. Red teaming efforts involve testing various domains, considering different perspectives, and addressing potential vulnerabilities throughout the AI system’s lifecycle. By focusing on these areas, OpenAI enhances accountability and mitigates risks associated with AI technology.
The Strategic Importance of Red Teaming for AI Model Deployment
Red teaming holds significant strategic importance in AI model deployment. By subjecting AI systems to rigorous evaluation and assessment, red teaming helps identify and mitigate potential risks and vulnerabilities. This proactive approach enhances the safety and reliability of AI models, adding a layer of accountability and ensuring ethical use. Red teaming contributes to the responsible and effective deployment of AI technology, driving progress in the field while safeguarding against potential harms.
The Role of AI Experts in Advancing AI Safety
AI experts play a vital role in advancing AI safety. Their domain-specific knowledge, technical expertise, and understanding of AI systems are essential in assessing and improving AI safety practices. AI experts contribute to the development of domain-specific taxonomies of risk, enabling comprehensive evaluations and better risk management. Their contributions and insights enhance the overall safety, accountability, and ethical considerations in the deployment of AI technology.
The Significance of Domain Experts in Improving AI Safety
Domain experts bring valuable expertise and insights to the field of AI safety. Their understanding of specific domains, such as cognitive science, chemistry, biology, physics, and more, allows for a comprehensive evaluation of AI systems’ impact. By leveraging their domain knowledge, experts contribute to the identification of risks, biases, and potential unintended consequences associated with AI technology. This multidisciplinary approach enhances AI safety practices and ensures the responsible deployment of AI models.
The Role of Experts in Developing Domain-Specific Taxonomies of Risk
Experts play a key role in the development of domain-specific taxonomies of risk. By leveraging their knowledge and expertise in specific domains, experts can identify and categorize potential risks and vulnerabilities associated with AI systems. These taxonomies provide a structured framework for assessing and evaluating AI models, enhancing the comprehensiveness and effectiveness of red teaming efforts. Experts contribute to the ongoing development of taxonomies that enable a thorough understanding of AI risks and inform mitigation strategies.
The Influence of Experts in Mitigating Potentially Harmful AI Capabilities
Experts have a significant influence in mitigating potentially harmful AI capabilities. Their insights, evaluations, and assessments contribute to identifying and addressing risks associated with AI systems. By analyzing and understanding AI capabilities, experts can propose practical solutions, guidelines, and policies to minimize potential harms. Their influence drives the development of safer AI technologies and policies, ensuring that AI systems align with ethical considerations, societal values, and human well-being.
