Day 3 [Panel] Quality Assurance, Governance, and Responsible AIware Development
AI Summary
Panel Introduction
- Invitation of esteemed panel members:
- PR: Discussed Brier scores and truth in AI models.
- Audris: Shared insights on supply chain implications for responsible AI.
- Jack: Expert in AI bomb and dataset license compliance.
- Bram: Authority on software engineering.
- Shinga: Advocate for global responsible AI practices.
Key Discussion Points
- Regulatory Challenges:
- Conflicting regulations (e.g., EU AI Act vs. Japan’s fair use).
- Need for software engineering to adapt to compliance requirements.
- Software Development and Compliance:
- Importance of integrating compliance considerations into development processes early on.
- Example: GDPR requirements impacting system design.
- AI Output Concerns:
- Risks of generated code potentially infringing on copyrights.
- Need for reliable assessments to evaluate generated code’s legality.
- Guardrails for AI Systems:
- Necessity of implementing guardrails to ensure ethical outputs.
- Balancing freedom of use with safety measures.
- Synthetic Data and Copyright Issues:
- Debate on whether synthetic data should have copyrights or licenses.
- Legal implications of using proprietary data for training AI models.
- Future of Regulations:
- Discussion on generative models and copyright law’s evolution.
- Anticipation of legal changes in response to emerging AI capabilities and concerns.
Conclusion
- The panel acknowledges the need for continuous discussions around AI ethics, compliance, and the role of software engineering in adapting to new regulations and challenges.