What is Trusted AI?
Trusted AI is the way people, data, and AI work together, with transparency, to create value.
What is trusted data?
Trusted data is the ability to seamlessly integrate and harmonize data across an organization. It provides a trusted foundation for reliable, accurate, and well-governed data.
Knowing where your analytics solutions' underlying data and models come from will significantly enhance trust. For example, an organization can build an Enterprise Feature Store, based on a trusted data foundation, making it easier to use previously vetted models and improve productivity.
This is the type of consistent and reliable management of data—at scale—that will meet any business's evolving needs.
Trust accelerates opportunity
Trusted AI should be incorporated throughout an organization’s analytics and AI/ML solutions. From predictive AI/ML to generative AI initiatives, Trusted AI is broadly relevant and deeply needed.
Predictive AI/ML
Trusted AI is important for the in-database functions and data pipelines used in predictive AI/ML, providing significant benefits when applied. One way to enhance the benefits: Teradata VantageCloud, the only platform to offer the massively parallel processing (MPP) architecture that enables best-in-class vertical and horizontal scaling of models.
Generative AI
AI must be transparent, so organizations must better understand their large language models (LLMs) by integrating the output with features and data sources, such as the ability to store and integrate prompts, embeddings, and retrieval augmented generation (RAG) queries. This makes an organization’s data lineage for generative AI use cases more explainable, accountable, and valuable.
Maximize the AI opportunity today
Drive value from trusted and cost-effective AI innovation across the enterprise with the most complete cloud analytics and data platform for AI.
Trusted AI principles
Accountability in all parts of the AI lifecycle
A human-centered approach to AI improves compliance with safety and privacy concerns, reinforces ethical and responsible standards, and limits potentially harmful impacts on our environment and society.
From a business perspective, a focus on people in Trusted AI means:
- Providing reliable and effective data security
- Introducing energy-efficient practices
- Protecting personally identifiable information (PII)
- Preventing bias issues with models and data training
Flexibility and faster innovation with open AI ecosystems
Simply put, transparency is being able to understand how and why an AI-driven decision was made.
It should also be clear why the decision is both fair and equitable—even if it may not have been the modeler’s own choice. Organizations do this by:
- Offering visibility into how models use data and comply with regulations
- Validating data sources as trustworthy before AI implementation
- Making model outputs explainable—and accountable—to human decision-makers
Cost-effective growth by scaling AI breakthroughs
Better reliability, speed, and accuracy will make the ROI of AI models far outweigh the cost of experimentation. It all comes down to providing the most cost-effective results and greatest positive impact for people and enterprises alike.
Value creation begins with identifying use cases. These breakthrough AI solutions could range from big ideas to incremental improvements:
- Building your own custom LLMs or integrating with partners
- Updating recommendation engines to be powered by generative AI
- Using natural language interfaces for insights, code generation, and metadata analysis
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