This framework shows that Wiley views Responsible AI as a comprehensive approach that goes beyond just technical implementation.

Question 1 of 3 for Claude: Combine the articles "Matthew Kissner, President & CEO of Wiley – Interview Series" and “Wiley’s Jarrett on how responsible AI can be a game changer in publishing industry" about Wiley and AI and tell me what the key messages are.

Claude’s answer:
I'll analyze the key messages from both interviews about Wiley's AI strategy and initiatives. Here are the main themes that emerge:

1. Strategic Embrace of AI While Preserving Legacy

• Wiley, with its 217-year history, has deliberately chosen to be an early mover in AI adoption for publishing

• Over 80% of their revenue now comes from digital content, showing their successful digital transformation

• They view AI engagement as inevitable and prefer to actively shape its development rather than resist it

2. Three Core Focus Areas for AI Implementation

• Licensing and application revenue opportunities, particularly for training AI models

• Improving internal productivity and operations

• Driving publishing innovation and workflow improvements

3. Research Integrity and Ethical Framework

• Developed specific AI principles focusing on human oversight, transparency, and accountability

• Implementing AI tools for detecting research fraud and compromised content

• Working to establish industry standards and ethical frameworks for AI use in publishing

• Strong emphasis on protecting intellectual property and copyright

4. Researcher Support and Tools

• Their survey found 69% of researchers want to keep pace with AI adoption

• Launched the AI Partnership Program to develop specialized AI tools for researchers

• Partnership with Potato AI for developing research assistants and protocols

• Focus on practical tools for literature reviews, data analysis, and research workflows

5. Content Licensing Strategy

• Creating a structured marketplace for AI licensing

• Three main focus areas: foundational LLM training, customized model fine-tuning, and Retrieval Augmented Generation (RAG)

• Emphasis on fair compensation and transparent content attribution

6. Industry Leadership Role

• Actively engaging in policy discussions with regulators and legislators

• Working to establish industry standards for AI use in scholarly publishing

• Focus on creating practical guidelines for AI tool usage disclosure and peer review processes

7. Future Vision

• AI seen as enhancing rather than replacing human capabilities in research

• Focus on democratizing science and making research more accessible

• Commitment to maintaining balance between technological advancement and human intellectual expertise

• Goal of shortening publishing cycles while maintaining quality and integrity

Both interviews highlight Wiley's proactive and strategic approach to AI adoption while emphasizing their commitment to maintaining research integrity and supporting the scholarly community through this technological transition.

Question 2 of 3 for Claude: What would be the main lessons for anyone else thinking about collaboration with AI makers?

Claude’s answer:
Based on Wiley's experience, here are the key lessons for organizations considering AI collaborations:

1. Take a Principled Approach First

• Wiley established clear AI development principles before diving into partnerships

• They focused on human oversight, trustworthiness, and accountability

• Having these guardrails in place helped guide their selection of partners and implementation approaches

2. Focus on Specific Industry Needs

• Wiley identified concrete needs in their industry (like research protocol development and literature reviews)

• They prioritized partnerships in their strongest disciplines

• They sought solutions for real user problems rather than implementing AI for its own sake

3. Address Data and IP Issues Early

• They recognized the importance of protecting intellectual property

• Developed structured approaches to content licensing

• Created clear frameworks for data attribution and usage

4. Listen to Your Users

• Wiley conducted surveys to understand researcher needs and concerns

• They found 70% of authors wanted explicit guidance on responsible AI use

• This informed their approach to developing tools and guidelines

5. Balance Innovation with Trust

• While moving quickly into AI, they maintained focus on research integrity

• Developed detection tools for AI-generated content

• Created disclosure frameworks for AI tool usage

6. Create Clear Partnership Frameworks

• Established the AI Partnership Program with specific criteria

• Combined their content and expertise with partners' technical capabilities

• Focused on co-innovation rather than just licensing or purchasing

7. Think About Scale and Integration

• They considered how AI tools would integrate with existing workflows

• Focused on solutions that could scale across their organization

• Developed internal systems to support AI adoption (like deployment in editorial processes)

8. Prepare for Change Management

• Recognized the need to bridge the gap between early adopters and cautious users

• Provided training and guidelines for AI tool usage

• Maintained transparency about AI implementation

9. Consider Industry Leadership Role

• Engaged in policy discussions and standard-setting

• Worked to establish best practices

• Collaborated with other organizations to address common challenges

10. Stay Focused on Core Value

• Maintained focus on enhancing rather than replacing human capabilities

• Kept their core mission (advancing knowledge and research) at the center of AI initiatives

• Used AI to support existing strengths rather than completely changing their business model

These lessons suggest that successful AI collaboration requires a balanced approach that considers ethical implications, user needs, practical implementation challenges, and long-term strategic goals.

Question 3 of 3 for Claude: What is Responsible AI according to Wiley?

Claude’s answer:
Based on the interviews, Wiley's vision of Responsible AI consists of several key elements:

1. Clear Principles and Oversight

• Human oversight is fundamental

• Emphasis on transparency and trustworthiness

• Focus on accountability in AI development and implementation

2. Ethical Framework for Content and Publishing

• Protection of intellectual property rights

• Clear disclosure requirements for AI tool usage in research

• Development of detection systems for AI-generated content

• Maintenance of research integrity standards

3. Data and Content Management

• Structured approach to content licensing

• Fair compensation for content used in AI training

• Transparent attribution of content sources

• Protection of data privacy

4. User-Centric Approach

• AI tools designed to enhance rather than replace human capabilities

• Focus on practical researcher needs

• Guidelines and support for responsible AI usage

• Balance between innovation and maintaining scholarly standards

5. Industry Standards Development

• Active participation in policy discussions with regulators

• Development of frameworks for AI use in scholarly publishing

• Collaboration with other organizations to establish best practices

• Creation of standards for peer review processes

6. Quality and Integrity Controls

• Implementation of AI detection tools at submission

• Processes to maintain scholarly work integrity

• Systems to identify potentially compromised content

• Focus on research reproducibility

7. Collaborative Innovation

• Partnership approach to AI development

• Focus on co-innovation rather than just technology adoption

• Integration of AI tools with existing workflows

• Balance between technological advancement and human expertise

This framework shows that Wiley views Responsible AI as a comprehensive approach that goes beyond just technical implementation to encompass ethical considerations, user needs, and industry leadership.