As the adoption of artificial intelligence (AI) continues to grow, businesses must address the potential risks associated with improper use and lack of governance. Implementing AI governance frameworks ensures the responsible and transparent use of AI technologies, aligning with an organization’s core values and mitigating financial, operational, regulatory, and reputational risks. Foundation models, which are large-scale AI models trained on vast amounts of raw data, offer curated datasets that can enhance the use and impact of advanced AI capabilities. However, trustworthiness and responsible governance are crucial factors for organizations utilizing foundation models.

Foundation Models: Leveraging the Power of Curated Datasets

Foundation models, also known as “transformers,” are AI models trained on large amounts of unlabeled data. These models can apply what they learn from one situation to another, making them highly versatile and efficient. Curated foundation models created by industry leaders like IBM and Microsoft enable enterprises to scale and accelerate AI capabilities using reliable data from various domains. They are widely used for tasks such as classification, entity extraction, translation, summarization, and generating realistic content.

Ensuring Trustworthiness and Governance of Foundation Models

While foundation models offer advanced AI capabilities, it is crucial for organizations to ensure the trustworthiness of their predictions and content. The Stanford Institute for Human-Centered Artificial Intelligence’s Center for Research on Foundation Models (CRFM) has outlined the risks and opportunities associated with foundation models. The source and composition of training data are often overlooked, highlighting the need for curated foundation models and trusted governance to mitigate potential risks.

Implementing Foundation Models with Built-in Governance

An open lakehouse architecture and fit-for-purpose data store enable organizations to scale AI and machine learning (ML) while incorporating built-in governance tools. This type of data store combines the flexibility of a data lake with the performance of a data warehouse, allowing for automation, integration with existing databases, and simplified setup. It enables businesses to connect existing and new data, streamline data engineering, and facilitate responsible data sharing while ensuring security and compliance.

The Importance of an AI Governance Toolkit

As AI becomes integrated into daily workflows, proactive governance is essential for responsible and ethical decision-making. An AI governance toolkit enables organizations to direct, manage, and monitor AI activities without costly platform switches. It includes AI lifecycle governance, which automates the capture of model metadata and enhances predictive accuracy. The toolkit also assists in building trust in AI outcomes, aligning with ethical principles, meeting regulatory obligations, identifying and managing risks, and providing customizable dashboards and reporting.

IBM and AI Governance

IBM’s Watsonx platform offers a next-generation data and AI platform with built-in governance capabilities, allowing organizations to leverage foundation models while adhering to responsible AI governance principles. The platform enables organizations to operationalize AI workflows, track models, capture model metadata, increase trust in AI outcomes, and enable responsible and transparent data and AI workflows.

Getting Started with AI Governance and Foundation Models

To effectively utilize the power of AI while mitigating risks, organizations should implement AI governance frameworks and leverage curated foundation models. By doing so, businesses can make informed, accountable, and ethical decisions in their AI initiatives.

FAQ

What are foundation models?

Foundation models, also known as “transformers,” are AI models trained on large amounts of unlabeled data. They can apply what they learn from one situation to another, making them highly versatile and efficient tools in various domains, including language processing and robotics.

What is AI governance?

AI governance refers to the practice of directing, managing, and monitoring AI activities within an organization. It ensures the ethical, responsible, and transparent use of AI technologies while mitigating risks and complying with regulatory requirements.

Why is trustworthiness important in foundation models?

Trustworthiness is crucial in foundation models as the predictions and content they generate can impact businesses significantly. Organizations need to rely on curated foundation models and trusted governance to ensure the accuracy and reliability of AI outcomes.

How can organizations implement AI governance?

Organizations can implement AI governance by utilizing AI governance toolkits that automate the capture of model metadata, provide customizable dashboards and reporting, identify and manage risks, and align with ethical principles and regulatory obligations. These toolkits facilitate responsible and transparent AI workflows throughout the AI service lifecycle.

What is IBM’s role in AI governance?

IBM’s Watsonx platform offers a next-generation data and AI platform with built-in governance capabilities. The platform enables organizations to leverage foundation models while adhering to responsible AI governance principles, ensuring transparency, accountability, and ethical decision-making in their AI initiatives.