Introducing the AI Alliance Open Innovation Principles

In the rapidly advancing field of artificial intelligence, data cleaning has become a mission-critical step in ensuring the success of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) architectures. This blog emphasizes the importance of high-quality, structured data in preventing AI model hallucinations, reducing algorithmic bias, enhancing embedding quality, and improving information retrieval accuracy. It covers essential AI data preprocessing techniques like deduplication, PII redaction, noise filtering, and text normalization, while spotlighting top tools such as IBM Data Prep Kit, AI Fairness 360, and OpenRefine. With real-world applications ranging from LLM fine-tuning to graph-based knowledge systems, the post offers a practical guide for data scientists and AI engineers looking to optimize performance, ensure ethical compliance, and build scalable, trustworthy AI systems.

Open source and open science in AI is a practical, proven approach to enabling access, innovation, trust, and value creation now. Let’s focus on that as we better define it.

We conducted a survey with 100 AI Alliance members to learn about the state of open source AI trust and safety for 2024. This blog post highlights key findings on AI applications, model popularity, safety concerns, regulatory focus, and gaps in current safety practices, while also providing an overview of notable open-source projects, tools, and research in the field of AI trust and safety.