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Responsible Generative AI

* "Responsible AI Leadership: A Global Summit on Generative AI" *April 2023 guide for experts and policymakers * Developing and governing generative AI systems * + 100 thought leaders and practitioners participated * Recommendations for responsible development, open innovation & social progress * 30 action-oriented recommendations aim * Navigate AI complexities

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The Presidio Recommendations
on Responsible Generative AI
J U N E 2 0 2 3
In collaboration
with AI Commons
The Presidio Recommendations on Responsible Generative AI 2
Introduction
Generative artificial intelligence (AI) has the potential to transform industries and society by boosting innovation and
empowering individuals across diverse fields, from arts to scientific research. To ensure a positive future, it is crucial
to prioritize responsible design and release practices from the beginning. As generative AI continues to advance at
an unprecedented pace, the need for collaboration among stakeholders to ensure that AI serves as a force for good
has become increasingly urgent.
On 26-28 April 2023, the summit “Responsible AI Leadership: A Global Summit on Generative AI” took place at the
World Economic Forum’s Centre for the Fourth Industrial Revolution based in the Presidio in San Francisco, USA.
The event was hosted by the Forum in partnership with AI Commons to guide technical experts and policy-makers
on the responsible development and governance of generative AI systems.
The summit emphasized the importance of open innovation and international collaboration as essential enablers
for responsible generative AI. The focus was on moving beyond insightful discussions to generate actionable and
practical recommendations for various AI stakeholders that could significantly influence the design, construction and
deployment of generative AI.
Over 100 AI thought leaders and practitioners participated in the summit, including chief scientific officers,
responsible AI and ethics leads, academic leaders, AI entrepreneurs, policy-makers, tech investors and members
of civil society. Participants engaged in discussions on numerous aspects of generative AI’s design, development,
release and societal impact, and deliberated on key recommendations. These recommendations emerged from
interactive panel discussions and working sessions through a bottom-up process, with participants reaching
consensus on critical areas related to the governance of generative AI.
This summary presents a set of 30 action-oriented recommendations aimed at guiding generative AI towards
meaningful human progress. The recommendations address three key themes that cover the entire life cycle
of generative AI: responsible development and release; open innovation and international collaboration; and
social progress.
By implementing these recommendations, stakeholders can navigate the complexities of AI development and
harness its potential responsibly and ethically. Join us in shaping a more innovative, equitable and prosperous future
that leverages the power of generative AI and mitigate its risks to benefit all.
The Presidio Recommendations on Responsible Generative AI 3
Responsible Development
and Release of Generative AI
This section critically assesses the necessity to protect our society from unforeseen outcomes induced by the
swiftly developing generative AI systems, and accordingly advocates for responsible strategies concerning their
development and deployment. These recommendations are intended for a broad spectrum of stakeholders
- ranging from AI developers to policy-makers and users. The objective is to foster accountable and inclusive
processes for AI development and deployment, thereby enhancing trust and transparency as generative AI systems
continue to proliferate.
01 Establish precise and shared terminology
All stakeholders are called upon to use precise terminology when discussing the design, development, evaluation
and measurement of generative AI models’ capabilities, limitations and issues. It is the responsibility of experts to
define and standardize this language. As soon as a consensus is reached, consistent adoption of this terminology
by all stakeholders is essential. This approach will boost clarity and promote effective communication, leading to
a shared understanding among different parties. Ultimately, it will facilitate the establishment of strong, standards,
guidelines and regulations for a range of generative AI applications.
02 
Build public awareness of AI capabilities and their limitations
Public and private stakeholders should prioritize the task of enhancing public understanding. This includes making
the terminology related to generative AI models understandable to the general public. Additionally, stakeholders
should inform users about the probabilistic (meaning their outputs are not deterministic but based on probability)
and stochastic (implying their operation involves a degree of random behavior) nature of generative AI models, while
setting accurate expectations for their performance.
03 
Focus on human values and preferences
The challenge to align generative AI models with human values and preferences needs to be further acknowledged
and addressed. Developers of AI systems should be engaged in discussions about normative values and
preferences when designing AI models.
04 
Encourage alignment and participation
Public and private sector stakeholders should recognize that AI systems necessitate quality feedback that is diverse
and representative of the user base to be truly aligned. Policy-makers should promote the involvement of diverse
stakeholders, including non-technical stakeholders, in AI research and development to ensure alignment with
human values. AI developers should work to facilitate interactions and feedback from a broad range of participants
to create a more inclusive and human-centric development process.
05 
Uphold AI accountability with rigorous benchmarknig and use
case-specific testing while exploring new metrics and standards
AI developers should commit to the importance of not only holding models accountable against the highest
established benchmarks, but also finding new metrics beyond traditional ones and towards other human-centric
dimensions. Benchmarking should be complemented by application-specific and task-defined testing to ensure a
comprehensive evaluation of generative AI models.
The Presidio Recommendations on Responsible Generative AI 4
06 

Employ diverse red teams
Red teaming, a method of critically analysing perspective to identify potential weaknesses, vulnerabilities
and areas for improvement, should be integral from model design to application and release. Diversity here
implies incorporating members from varied genders, backgrounds, experiences and perspectives for a more
comprehensive critique. The public and private sectors should implement frameworks and methodologies to
facilitate thorough red teaming.
07 

Adopt transparent release strategies
Producers of AI should be held accountable to release AI models responsibly, making them available to the public
without compromising safety. Responsible release strategies should be initiated upstream during project ideation
and product design to ensure that potential risks are identified and mitigated throughout the development process.
08 

Enable user feedback
Users should be empowered with robust controls that allow them to provide real-time feedback on model outputs.
Additionally, it is relevant to enable users to have a comprehensive understanding of the limits and responsibilities
associated with the generated content.
09 

Embed model and system traceability
Developers and policy-makers should align on the importance of creating formal evaluation and auditing
structures surrounding traceability throughout the entire AI life cycle, from data provenance to training scenarios
and post-implementation.
10 
Ensure content traceability
To increase transparency and accountability, companies developing AI-generated content should be responsible
for tracing how content is generated and documenting its provenance. This will help users discern the difference
between human-generated and AI-generated content.
11 
Disclose non-human interaction
In virtual environments, humans should know whether they are interacting with a human or a machine. AI providers
should develop mechanisms to support this, for example, via watermarking.
12 
Build human-AI trust
To build trust in AI systems, developers and companies should prioritize transparency, consistency, and meeting
and managing user expectations. AI developers should be transparent in their processes and decision-making,
providing users with an understanding of how they reach their results. By focusing on these aspects, AI developers
can create systems that foster trust and facilitate positive human-AI interactions.
13 Implement a step-by-step review process
Policy-makers and businesses should create a step-by-step review process for AI models and products. This
should be similar to the detailed checks used in clinical trials or car manufacturing, both before and after a product
goes live. There should be an independent auditor or international agency to oversee this to ensure uniform
evaluations and continuous monitoring. To help limit potential risks and negative impacts, certification, or licensing
system could be used.
The Presidio Recommendations on Responsible Generative AI 5
14 Develop comprehensive, multi-level measurement frameworks
Policy-makers should emphasize ongoing efforts and incentivize developers and standardization bodies to focus on
creating and employing measurement frameworks with an emphasis on socio-technical aspects rather than solely
technical performance.
15 Adopt sandbox processes
AI developers, standard-setting bodies and regulators should cooperate on more flexible “sandbox” development
environments along with new and associated processes of governance and oversight. Sandboxing could help build
trust by demonstrating that AI systems have undergone rigorous testing and evaluation to ensure safety, reliability
and compliance.
16 

Adapt to the evolving landscape of creativity
and intellectual property
With generative AI impacting content creation, it is essential for policy-makers and legislators to re-examine and
update copyright laws to enable appropriate attribution, and ethical and legal reuse of existing content.
This section focuses on the importance of sharing scientific knowledge and enhancing international collaboration.
As frontier research capabilities tend to be concentrated in private sector companies in a select few countries, it
is vital that academic researchers remain an integral part of the exploratory process, while countries worldwide
participate and influence the governance of generative AI systems. These recommendations are designed for
a range of stakeholders, including researchers, AI developers, standard-setting bodies and policy-makers. The
overarching goal is to cultivate transparency, accountability and inclusivity in the development, implementation and
governance of generative AI.
17 
Incentivize public-private research coordination
Public and private stakeholders should actively work to design incentive structures that facilitate greater
coordination between academic researchers and the private sector throughout the technology development
lifecycle. Possible mechanisms to be considered include joint research programmes, data-sharing protocols and
joint IP ownership.
18 
Build a common registry of models, tools, benchmarks
and best practices
Producers and researchers of generative AI should contribute to a common and open registry of source codes,
models, datasets, tools, benchmarks and best practice guidelines, to be shared within the research community, in
order to have a platform for academic and private sector collaboration to build future models and systems that are
transparent and accountable to the public.
Open Innovation and
International Collaboration
The Presidio Recommendations on Responsible Generative AI 6
19 Support responsible open innovation and knowledge sharing
Policy-makers and AI providers should contribute to frameworks to democratize AI through responsible sharing
of resources, including data, source code, models and research findings; also encourage the sharing certification
processes, ensuring transparency and trust among stakeholders. A public-private long-term initiative could be
developed to build public-facing platforms that provide open access to compute, data and pre-trained models.
This platform could be treated as a digital public good, and usage could be promoted across borders.
20 
Enhance international collaboration on AI standards
Standard bodies must foster international collaboration on AI standards, ensuring the participation of all AI
stakeholders, including all geographical locations.
21 
Establish a global AI governance initiative
To address the challenges and potential risks posed by AI technologies, policy-makers should consider devoting
efforts towards creating a global AI governance initiative. This initiative should bring together experts from a wide
array of fields. The key focus should be on promoting global understanding of responsible generative AI, ensuring
broad inclusion, facilitating access to infrastructure, and fostering collaboration to harmonize response structures at
the national level against AI challenges and risks.
Social progress
This section examines the hurdles tied to AI-driven transformations, spanning from workforce transitions to
educational shifts, as well as the necessity of championing AI for societal benefit and advocating for equitable AI
access in developing nations. The recommendations are intended for a broad array of stakeholders, including
educational institutions, community organizations, corporations, individuals, policy-makers and governments.
The primary objective is to cultivate a society that is more informed, engaged and resilient in the face of these
emerging changes.
22 Prioritize social progress in generative AI development and adoption
All stakeholders must ensure that the technology’s societal implications remain front and centre. This
involves a focus beyond technical proficiency towards the technology’s role in enhancing social progress.
Comprehensive support must be provided to communities and workers affected by the shift to an AI-enabled
society, encompassing learning initiatives, guidance on surmounting generative AI-specific challenges and
assistance in navigating the ethical, social and technical shifts inherent in an AI-influenced environment with an
active participation of workers throughout the process.
23 Drive AI literacy across society
Educational bodies and community institutions must take the initiative to increase AI literacy among the general
public. A proactive approach is needed to demystify generative AI tools, outline their potential uses and discuss their
ethical implications. This will empower individuals to better understand, interact with and contribute to the evolving
landscape of AI, fostering a more informed and participative society.
Ad

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Responsible Generative AI

  • 1. The Presidio Recommendations on Responsible Generative AI J U N E 2 0 2 3 In collaboration with AI Commons
  • 2. The Presidio Recommendations on Responsible Generative AI 2 Introduction Generative artificial intelligence (AI) has the potential to transform industries and society by boosting innovation and empowering individuals across diverse fields, from arts to scientific research. To ensure a positive future, it is crucial to prioritize responsible design and release practices from the beginning. As generative AI continues to advance at an unprecedented pace, the need for collaboration among stakeholders to ensure that AI serves as a force for good has become increasingly urgent. On 26-28 April 2023, the summit “Responsible AI Leadership: A Global Summit on Generative AI” took place at the World Economic Forum’s Centre for the Fourth Industrial Revolution based in the Presidio in San Francisco, USA. The event was hosted by the Forum in partnership with AI Commons to guide technical experts and policy-makers on the responsible development and governance of generative AI systems. The summit emphasized the importance of open innovation and international collaboration as essential enablers for responsible generative AI. The focus was on moving beyond insightful discussions to generate actionable and practical recommendations for various AI stakeholders that could significantly influence the design, construction and deployment of generative AI. Over 100 AI thought leaders and practitioners participated in the summit, including chief scientific officers, responsible AI and ethics leads, academic leaders, AI entrepreneurs, policy-makers, tech investors and members of civil society. Participants engaged in discussions on numerous aspects of generative AI’s design, development, release and societal impact, and deliberated on key recommendations. These recommendations emerged from interactive panel discussions and working sessions through a bottom-up process, with participants reaching consensus on critical areas related to the governance of generative AI. This summary presents a set of 30 action-oriented recommendations aimed at guiding generative AI towards meaningful human progress. The recommendations address three key themes that cover the entire life cycle of generative AI: responsible development and release; open innovation and international collaboration; and social progress. By implementing these recommendations, stakeholders can navigate the complexities of AI development and harness its potential responsibly and ethically. Join us in shaping a more innovative, equitable and prosperous future that leverages the power of generative AI and mitigate its risks to benefit all.
  • 3. The Presidio Recommendations on Responsible Generative AI 3 Responsible Development and Release of Generative AI This section critically assesses the necessity to protect our society from unforeseen outcomes induced by the swiftly developing generative AI systems, and accordingly advocates for responsible strategies concerning their development and deployment. These recommendations are intended for a broad spectrum of stakeholders - ranging from AI developers to policy-makers and users. The objective is to foster accountable and inclusive processes for AI development and deployment, thereby enhancing trust and transparency as generative AI systems continue to proliferate. 01 Establish precise and shared terminology All stakeholders are called upon to use precise terminology when discussing the design, development, evaluation and measurement of generative AI models’ capabilities, limitations and issues. It is the responsibility of experts to define and standardize this language. As soon as a consensus is reached, consistent adoption of this terminology by all stakeholders is essential. This approach will boost clarity and promote effective communication, leading to a shared understanding among different parties. Ultimately, it will facilitate the establishment of strong, standards, guidelines and regulations for a range of generative AI applications. 02 Build public awareness of AI capabilities and their limitations Public and private stakeholders should prioritize the task of enhancing public understanding. This includes making the terminology related to generative AI models understandable to the general public. Additionally, stakeholders should inform users about the probabilistic (meaning their outputs are not deterministic but based on probability) and stochastic (implying their operation involves a degree of random behavior) nature of generative AI models, while setting accurate expectations for their performance. 03 Focus on human values and preferences The challenge to align generative AI models with human values and preferences needs to be further acknowledged and addressed. Developers of AI systems should be engaged in discussions about normative values and preferences when designing AI models. 04 Encourage alignment and participation Public and private sector stakeholders should recognize that AI systems necessitate quality feedback that is diverse and representative of the user base to be truly aligned. Policy-makers should promote the involvement of diverse stakeholders, including non-technical stakeholders, in AI research and development to ensure alignment with human values. AI developers should work to facilitate interactions and feedback from a broad range of participants to create a more inclusive and human-centric development process. 05 Uphold AI accountability with rigorous benchmarknig and use case-specific testing while exploring new metrics and standards AI developers should commit to the importance of not only holding models accountable against the highest established benchmarks, but also finding new metrics beyond traditional ones and towards other human-centric dimensions. Benchmarking should be complemented by application-specific and task-defined testing to ensure a comprehensive evaluation of generative AI models.
  • 4. The Presidio Recommendations on Responsible Generative AI 4 06 Employ diverse red teams Red teaming, a method of critically analysing perspective to identify potential weaknesses, vulnerabilities and areas for improvement, should be integral from model design to application and release. Diversity here implies incorporating members from varied genders, backgrounds, experiences and perspectives for a more comprehensive critique. The public and private sectors should implement frameworks and methodologies to facilitate thorough red teaming. 07 Adopt transparent release strategies Producers of AI should be held accountable to release AI models responsibly, making them available to the public without compromising safety. Responsible release strategies should be initiated upstream during project ideation and product design to ensure that potential risks are identified and mitigated throughout the development process. 08 Enable user feedback Users should be empowered with robust controls that allow them to provide real-time feedback on model outputs. Additionally, it is relevant to enable users to have a comprehensive understanding of the limits and responsibilities associated with the generated content. 09 Embed model and system traceability Developers and policy-makers should align on the importance of creating formal evaluation and auditing structures surrounding traceability throughout the entire AI life cycle, from data provenance to training scenarios and post-implementation. 10 Ensure content traceability To increase transparency and accountability, companies developing AI-generated content should be responsible for tracing how content is generated and documenting its provenance. This will help users discern the difference between human-generated and AI-generated content. 11 Disclose non-human interaction In virtual environments, humans should know whether they are interacting with a human or a machine. AI providers should develop mechanisms to support this, for example, via watermarking. 12 Build human-AI trust To build trust in AI systems, developers and companies should prioritize transparency, consistency, and meeting and managing user expectations. AI developers should be transparent in their processes and decision-making, providing users with an understanding of how they reach their results. By focusing on these aspects, AI developers can create systems that foster trust and facilitate positive human-AI interactions. 13 Implement a step-by-step review process Policy-makers and businesses should create a step-by-step review process for AI models and products. This should be similar to the detailed checks used in clinical trials or car manufacturing, both before and after a product goes live. There should be an independent auditor or international agency to oversee this to ensure uniform evaluations and continuous monitoring. To help limit potential risks and negative impacts, certification, or licensing system could be used.
  • 5. The Presidio Recommendations on Responsible Generative AI 5 14 Develop comprehensive, multi-level measurement frameworks Policy-makers should emphasize ongoing efforts and incentivize developers and standardization bodies to focus on creating and employing measurement frameworks with an emphasis on socio-technical aspects rather than solely technical performance. 15 Adopt sandbox processes AI developers, standard-setting bodies and regulators should cooperate on more flexible “sandbox” development environments along with new and associated processes of governance and oversight. Sandboxing could help build trust by demonstrating that AI systems have undergone rigorous testing and evaluation to ensure safety, reliability and compliance. 16 Adapt to the evolving landscape of creativity and intellectual property With generative AI impacting content creation, it is essential for policy-makers and legislators to re-examine and update copyright laws to enable appropriate attribution, and ethical and legal reuse of existing content. This section focuses on the importance of sharing scientific knowledge and enhancing international collaboration. As frontier research capabilities tend to be concentrated in private sector companies in a select few countries, it is vital that academic researchers remain an integral part of the exploratory process, while countries worldwide participate and influence the governance of generative AI systems. These recommendations are designed for a range of stakeholders, including researchers, AI developers, standard-setting bodies and policy-makers. The overarching goal is to cultivate transparency, accountability and inclusivity in the development, implementation and governance of generative AI. 17 Incentivize public-private research coordination Public and private stakeholders should actively work to design incentive structures that facilitate greater coordination between academic researchers and the private sector throughout the technology development lifecycle. Possible mechanisms to be considered include joint research programmes, data-sharing protocols and joint IP ownership. 18 Build a common registry of models, tools, benchmarks and best practices Producers and researchers of generative AI should contribute to a common and open registry of source codes, models, datasets, tools, benchmarks and best practice guidelines, to be shared within the research community, in order to have a platform for academic and private sector collaboration to build future models and systems that are transparent and accountable to the public. Open Innovation and International Collaboration
  • 6. The Presidio Recommendations on Responsible Generative AI 6 19 Support responsible open innovation and knowledge sharing Policy-makers and AI providers should contribute to frameworks to democratize AI through responsible sharing of resources, including data, source code, models and research findings; also encourage the sharing certification processes, ensuring transparency and trust among stakeholders. A public-private long-term initiative could be developed to build public-facing platforms that provide open access to compute, data and pre-trained models. This platform could be treated as a digital public good, and usage could be promoted across borders. 20 Enhance international collaboration on AI standards Standard bodies must foster international collaboration on AI standards, ensuring the participation of all AI stakeholders, including all geographical locations. 21 Establish a global AI governance initiative To address the challenges and potential risks posed by AI technologies, policy-makers should consider devoting efforts towards creating a global AI governance initiative. This initiative should bring together experts from a wide array of fields. The key focus should be on promoting global understanding of responsible generative AI, ensuring broad inclusion, facilitating access to infrastructure, and fostering collaboration to harmonize response structures at the national level against AI challenges and risks. Social progress This section examines the hurdles tied to AI-driven transformations, spanning from workforce transitions to educational shifts, as well as the necessity of championing AI for societal benefit and advocating for equitable AI access in developing nations. The recommendations are intended for a broad array of stakeholders, including educational institutions, community organizations, corporations, individuals, policy-makers and governments. The primary objective is to cultivate a society that is more informed, engaged and resilient in the face of these emerging changes. 22 Prioritize social progress in generative AI development and adoption All stakeholders must ensure that the technology’s societal implications remain front and centre. This involves a focus beyond technical proficiency towards the technology’s role in enhancing social progress. Comprehensive support must be provided to communities and workers affected by the shift to an AI-enabled society, encompassing learning initiatives, guidance on surmounting generative AI-specific challenges and assistance in navigating the ethical, social and technical shifts inherent in an AI-influenced environment with an active participation of workers throughout the process. 23 Drive AI literacy across society Educational bodies and community institutions must take the initiative to increase AI literacy among the general public. A proactive approach is needed to demystify generative AI tools, outline their potential uses and discuss their ethical implications. This will empower individuals to better understand, interact with and contribute to the evolving landscape of AI, fostering a more informed and participative society.
  • 7. The Presidio Recommendations on Responsible Generative AI 7 24 Foster holistic thought approaches in AI-driven environments Foster diverse modes of thinking – critical, computational and responsible – to better equip society for the generative AI era. Encourage these core competencies across sectors and communities to empower individuals to engage critically with AI-generated content, understand the underlying technology and make responsible decisions about its use. 25 Steer generative AI’s transformative impact Address the transformative influence of generative AI on societal systems. Understand its effect on human interactions, knowledge dissemination and evaluation mechanisms. Proactively adapt to the evolving landscape, supporting roles that may transform due to generative AI, and explore innovative ways to evaluate its impacts within our rapidly evolving digital ecosystem, to harness its potential for driving positive societal transformation. 26 Incentivize innovation for social good Policy-makers should encourage the development and implementation of generative AI technologies that prioritize social good and address complex and unmet societal needs, such as in healthcare and climate change, to improve the overall quality of life. 27 Address resource and infrastructure disparities Policy-makers should increase public investment in national and international research infrastructure. That includes work to ensure greater access to computing resources for researchers, especially those from underrepresented regions and institutions. The private sector is encouraged to contribute to the development of datasets and support governments in making more resources available to researchers. 28 Promote generative AI expertise within governments Governments should invest in fostering AI expertise, ensuring an informed, effective and responsible approach to public policies and regulation of these transformative technologies. By leveraging mechanisms such as targeted incentives, private sector collaborations, and exchange programs, governments can nurture AI talent. This commitment while expanding in-house AI proficiency is crucial in securing a future where these technologies advance societal progress and serve the public interest effectively. 29 Increase equitable access to AI in developing countries To ensure that the benefits of generative AI technology are accessible to all, public and private stakeholders should focus on establishing initiatives that can provide support and resources at scale, particularly in developing countries where there may be limited access to digital infrastructures. Efforts should focus on providing resources, training, and expertise to make AI more accessible and inclusive, fostering national and international partnerships across sectors to promote diversity and inclusion in the development and deployment of generative AI technology. 30 Preserve cultural heritage All stakeholders need to contribute to preserve cultural heritage. Public and private sector should invest in creating curated datasets and developing language models for underrepresented languages, leveraging the expertise of local communities and researchers and making them available. This will improve access to AI technologies to help preserve linguistic diversity and cultural heritage.
  • 8. The Presidio Recommendations on Responsible Generative AI 8 Contributors Blaise Aguera Vice-President and Fellow, Google Research, Google Xavier Amatriain Vice-President of Engineering – Product AI Strategy, LinkedIn Corporation Stephen Augustus Head of Open Source, Cisco Systems Ricardo Baeza-Yates Director of Research, Institute for Experiential AI, Northeastern University Anthony Bak Head of AI and Machine Learning, Palantir Technologies Houman Behzadi President and Chief Product Officer, C3 AI Kimmy Bettinger Expert and Knowledge Communities Lead, World Economic Forum Seth Bergeson Manager, AI and Emerging Technology, PwC Jamie Berryhill Artificial Intelligence Policy Analyst, Organisation for Economic Co-operation and Development (OECD) Marc Boxser Vice-President, Policy and Communications, Chegg Inc. Kirk Bresniker Fellow and Chief Architect, Hewlett Packard Labs, Hewlett Packard Enterprise Joanna Bryson Professor of Ethics and Technology, Hertie School Sebastian Buckup Head of Network and Partnerships, Deputy Head. Centre for the Fourth Industrial Revolution; Member of the Executive Committee, World Economic Forum Jill Burstein Principal Assessment Scientist, Duolingo Summit Steering Committee Esteban Arcaute Head of Responsible AI, Meta Platforms Yoshua Bengio Head of the Montreal Institute for Learning Algorithms, University of Montreal Mona Diab Lead Responsible AI Research Scientist, Meta Platforms Michael Kearns Founding Director, Warren Center for Network and Data Sciences, University of Pennsylvania Hiroaki Kitano Senior Executive Vice-President and Chief Technology Officer; Chief Executive Officer, Sony Research, Sony Group Corporation Yann LeCun Vice-President and Chief AI Scientist, Meta Platforms Pilar Manchón Senior Director of Engineering, Google Peter Norvig Director of Research, Google Cathy Li Head of AI, Data and Metaverse; Deputy Head, Centre for the Fourth Industrial Revolution; Member of the Executive Committee, World Economic Forum Benjamin Larsen Lead, Artificial Intelligence and Machine Learning, World Economic Forum Hubert Halopé Lead, Artificial Intelligence and Machine Learning, World Economic Forum Lucia Velasco Lead, Artificial Intelligence and Machine Learning, World Economic Forum Summit Co-Chairs Amir Banifatemi Director, AI Commons Pascale Fung Chair Professor, Hong Kong University of Science Technology Francesca Rossi IBM Fellow and IBM AI Ethics Global Leader; AAAI President Joaquin Quiñonero-Candela Technical Fellow for Artificial Intelligence, LinkedIn Authors
  • 9. The Presidio Recommendations on Responsible Generative AI 9 Cansu Canca AI Ethics Lead at Institute for Experiential AI, Northeastern University Diane Chang Director of Data Science, Intuit Inc. Joshua Cohen Member of the Faculty, Apple University, Apple David Cox Director of Exploratory AI Research, IBM Corporation Natasha Crampton Chief Responsible AI Officer, Microsoft Corporation Joris Cyizere Head ad interim, Centre for the Fourth Industrial Revolution, Rwanda Umeshwar Dayal Corporate Chief Scientist, Senior Vice-President and Senior Fellow, Hitachi America Anil Dewan Senior Advisor, US Department of Homeland Security Daniel Dobrygowski Head, Governance and Trust, World Economic Forum Anne Marie Engtoft Larsen Tech Ambassador, Ministry of Foreign Affairs of Denmark Mojdeh Eskandari Founder and President, Enovant Foundation Aldo Faisal Professor of Artificial Intelligence and Neuroscience, Imperial College London Gilles Fayad Advisor, Institute of Electrical and Electronics Engineers Rebecca Finlay Chief Executive Officer, Partnership on AI Kay Firth-Butterfield Executive Director, Centre for Trustworthy Technology Gwenda Fong Deputy Secretary (Development and Regulation, Ministry of Communications and Information of Singapore Edward Fu Head of Government Affairs, Duolingo Krishna Gade Chief Executive Officer and Co-Founder, Fiddler Labs Eugenio Garcia Deputy Consul-General, San Francisco, Ministry of Foreign Affairs of Brazil Tiffany Georgievski AI Attorney, Sony Research Matthew Graviss Chief Data Officer, US Department of State Tom Gruber Co-Founder/Chief Technology Officer, Siri and Humanistic.ai Peter Hallinan Leader, Responsible AI, Amazon Web Services Ruimin He Chief Technology Advisor, Ministry of Communications and Information of Singapore Brittan Heller Fellow, Digital Forensics Research Lab, The Atlantic Council Cyrus Hodes Co-Founder of AIGC Chain and Stability AI, Harvard Kennedy School of Government Babak Hodjat Chief Technology Officer AI, Cognizant Technology Solutions US Corp. Jerremy Holland Director of AI Research, Apple Matissa Hollister Assistant Professor of Organizational Behaviour, McGill University Sara Hooker Head, Cohere for AI Eric Horvitz Chief Scientific Officer, Microsoft Xinghai Hu Head of TikTok Data US, Bytedance Anil Kamath Fellow and Vice-President AI/ML, Adobe Systems Vijay Karunamurthy Vice-President of Engineering, Scale AI Anja Kaspersen Member, Council on Extended Intelligence and Industry Activity on Life Science, Institute of Electrical and Electronics Engineers Jeffrey Ladish Head of AI Insights, Center for Humane Technology Yolanda Lannquist Director of AI Governance, The Future Society
  • 10. The Presidio Recommendations on Responsible Generative AI 10 Federico Lecumberry Associated Professor, Universidad de la República Chase Lochmiller Co-Founder and Chief Executive Officer, Crusoe Energy Systems Leland Lockhart Director, Artificial Intelligence Machine Learning, Vista Equity Partners David Luan Chief Executive Officer, Adept AI Emily McReynolds Senior Fellow, Center for Responsible AI, New York University Risto Miikkulainen Professor of Computer Science, University of Texas, Austin Steven Mills Partner and Chief Artificial Intelligence Ethics Officer, Boston Consulting Group Joshua New Technology Policy Executive, IBM Corporation Loren Newman Government Affairs Lead, World Economic Forum Vaibhav Pahwa Product Manager, Platform Fairness and Responsible AI, TikTok Gleb Papyshev PhD Candidate in Science and Technology Policy, The Hong Kong University of Science and Technology Vijay Parthasarathy Head of Artificial Intelligence and Machine Learning, Zoom Video Communications Jonnie Penn Assistant Teaching Professor of AI Ethics and Society, University of Cambridge Nazneen Rajani Research Lead, Hugging Face Martin Rauchbauer Co-Director and Founder, Tech Diplomacy Network Stuart Russell Professor of Computer Science, University of California, Berkeley Sultan Saidov Co-Founder and President, Beamery Inc. Nayat Sanchez-Pi Chief Executive Officer, INRIA Chile Supheakmungkol Sarin Head of Data and Artificial Intelligence Ecosystems, World Economic Forum Silvio Savarese Executive Vice-President, Chief Scientist, Salesforce Yoav Schlesinger Architect, Ethical AI Practice, Salesforce Craig Shank Advisor, Responsible Artificial Intelligence Institute Joanna Shields Chief Executive Officer, BenevolentAI Karen Silverman Founder and Chief Executive Officer, The Cantellus Group Sarvjeet Singh Principal Engineer/Engineering Director, Google Research, Google Navrina Singh Founder and Chief Executive Officer, Credo AI Uyi Stewart Chief Data and Technology Officer, data.org JoAnn Stonier Chief Data Officer, Mastercard Murali Subbarao Vice-President, AI Solution Success, ServiceNow Candace Sue Vice-President for Academic Relations, Chegg Arun Sundararajan Harold Price Professor of Entrepreneurship and Technology, Stern School of Business, New York University Josephine Teo Minister of Communications and Information of Singapore Kellee Tsai Dean of Humanities and Social Science,The Hong Kong University of Science and Technology Thomas Wolf Co-Founder, Hugging Face Andrea Wong Head of Platform Fairness, Bytedance Lauren Woodman Chief Executive Officer, DataKind Daniel Wroblewski Managing Director, Head of Investment Science, Canada Pension Plan Investment Board
  • 11. The Presidio Recommendations on Responsible Generative AI 11 Alice Xiang Global Head of AI Ethics, Sony Research, Sony Group Corporation Kevin Yancey Staff AI Research Engineer, Duolingo Masaru Yarime Associate Professor, The Hong Kong University of Science and Technology Grace Yee Director of Ethical Innovation, AI Ethics, Adobe Polina Zvyagina Privacy and Data Policy Manager, AI/ML Products, Responsible AI, Meta Platforms
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