A blog about Product Management, Data Management and Self-Driven Learning
In Generative AI, the concept of "Acquire" extends beyond training data to include fine-tuning, retrieval contexts, and prompts. Effective governance is crucial to prevent issues like IP leakage and bias. A structured approach involves defining data usage policies, curating knowledge sources, and treating prompts as governed assets, ensuring safety and compliance in AI initiatives.
Generative AI requires a distinct design approach compared to traditional machine learning, emphasising the need to classify use cases by their impact: informational, decision-support, or action-taking. Organisations should establish guidelines addressing acceptable error levels, prohibited areas, and human oversight, enabling effective management of risks associated with GenAI outputs.
The content discusses the integration of Generative AI into machine learning (ML) governance, emphasising the importance of the Design, Acquire, Store, Use, and Delete stages in the ML lifecycle. It highlights governance practices crucial for responsible AI deployment and how existing frameworks can guide the transition to more complex AI systems.
The DASUD framework—Design, Acquire, Store, Use, Delete—serves as a valuable model for AI governance, enhancing existing data management practices. It outlines a structured approach to integrate governance throughout the AI lifecycle, ensuring clarity in decision-making, accountability, and risk management while adapting familiar processes for AI applications.
Reflecting on the decline of my personal blog during the pandemic, driven by changes in search rankings and competition made me realise that there is more to the system than meets the eye. Inspired by a podcast featuring Cory Doctorow's concept of "enshittification," I now am determined to empower others to regain control over their online experiences and focus. Future posts will explore strategies for online protection.
Nigel D'Souza 