Boost AI Workflow Safety with Trustworthy Models

Foundation models and trustworthy governance for AI workflow risk management

Early AI adoption is underway. As more companies utilize AI systems and the technology matures, inappropriate use might pose financial, operational, regulatory, and reputational problems. AI for certain business tasks without guardrails may also conflict with an organization’s ideals.

AI governance addresses these inevitable adoption issues. AI governance involves leading, supervising, and monitoring an organization’s AI efforts. Data origin tracking, models, metadata, and audit pipelines are included.

AI and ML use is ethical, accountable, and transparent with an AI governance framework. It guides AI management in an organization and covers risk management and regulatory compliance.

Foundation models: Curated datasets power

New, large-scale AI models called foundation models, or “transformers,” are trained on massive volumes of unlabeled data. The foundation model ecosystem, the culmination of decades of machine learning, NLP, and other research, has garnered attention in computer science and AI. Open-source initiatives, academic institutions, startups, and legacy tech businesses developed foundation models.

Foundation models influence reality through language, vision, and more. Robotics, reasoning, and human-interactive tools employ them. Foundation models include OpenAI’s language prediction model GPT-3, which generates human-like text.

Foundation models use self-supervised and transfer learning to apply what they learn. Thus, instead of training many models on labeled, task-specific data, one large transformer-based model can be pre-trained and reused with fine-tuning.

With trusted data, curated foundation models like IBM and Microsoft’s let companies grow and accelerate the use and impact of advanced AI capabilities. Models are trained on code, time-series, tabular, geographic, and IT events data in addition to natural language. Domain-specific foundation models can be applied to new climate change, healthcare, HR, customer care, IT app modernization, and other use cases.

ML activities like categorization and entity extraction and generative AI tasks like translation, summarization, and realistic content need foundation models. These models explain the massive AI advances in recent years.

“With foundation models, AI for business is more powerful than ever,” stated IBM Chairman and CEO Arvind Krishna. AI deployment is more scalable, inexpensive, and efficient using foundation models.

Are foundation models reliable?

An enterprise needs responsible, transparent, and explainable AI, which is difficult to find in the early days of the technology.

ChatGPT’s large language model (LLM) was trained on internet data, as were most foundation models. Is that training data reliable? Generative AI chatbots insult customers and lie. Important to be trustworthy. Large foundation model suppliers’ projections and content must be trusted by businesses.

The Stanford Institute for Human-Centered Artificial Intelligence‘s Center for Research on Foundation Models (CRFM) recently listed foundation model hazards and prospects. They said training data, including its source and makeup, is typically missed. A curated foundation paradigm and trusted governance are needed.

Start with foundation models

AI development studios can swiftly train, validate, adjust, and deploy foundation models and build AI applications with a fraction of the data needed. How many “tokens” (words or word fragments) a dataset has is measured. They offer reliable, enterprise-ready data with negative and positive curation.

Removing questionable datasets, AI-based hate, and profanity filters is negative curation. Positive curation includes enterprise-relevant finance, legal & regulatory, cybersecurity, and sustainability items.

Scaling AL/ML with governance

A fit-for-purpose data storage with open lakehouse architecture scales AI and ML and includes governance tools. It works in on-premise and multi-cloud setups. This next-generation data store scales AI workloads everywhere by combining data lake flexibility and data warehouse performance.

It automates and integrates databases and simplifies setup and use. It also enables you optimize workloads and choose the right engine for the right workload at the right cost, lowering data warehouse costs. A data store allows a business combine old and new data for real-time analytics and business intelligence. It streamlines data engineering with fewer pipelines, simpler transformation, and enhanced data.

Responsible data sharing provides self-service access to more data for more users while guaranteeing security and compliance with governance and local legislators.

An AI governance toolbox offers

As AI becomes more integrated into organizations’ daily workflows, proactive governance throughout AI service creation, deployment, and management is even more important to ensure ethical decisions.

Governance in AI programs reduces risk and improves ethical and legal compliance: 50% of corporate leaders said the most essential part of explainable AI is satisfying external regulatory and compliance obligations, yet majority haven’t established an AI governance structure and 74% aren’t avoiding unintended biases.

A data science platform switch is unnecessary to direct, manage, and monitor AI activities with an AI governance toolset, even for third-party models. Software automation reduces risk, manages regulations, and addresses ethics. AI lifecycle governance analyzes, catalogs, and manages AI models globally. It streamlines model information capture and improves predictive accuracy to identify AI tool usage and model training needs.

An AI governance toolbox allows you develop responsible and transparent AI programs. You can comprehend and explain your AI’s decisions, which builds trust in trees and document datasets, models, and pipelines. It automates model facts and workflows to meet corporate standards, discovers, manages, monitors, and reports risk and compliance at scale, and provides dynamic dashboards and customisable results. A governance application can apply external regulations to policies for automated compliance, audit support, and configurable dashboards and reporting.

Proper AI governance allows your organization to maximize foundation models while remaining accountable and ethical as it advances AI technologies.

Governance, foundation models, IBM

Effective AI governance is essential to leveraging AI’s power while avoiding its many drawbacks. Responsible and transparent AI management includes risk management and regulatory compliance to govern its implementation in a company. Foundation models enhance AI capabilities for scalable and efficient implementation across domains.

Watsonx, a next-generation data and AI platform, helps enterprises exploit foundation models while following ethical AI governance. Your company can design responsible, transparent, and explainable AI workflows using watsonx.governance.

With Watsonx, companies can:

Implement AI procedures for scaled efficiency and accuracy. Automated, scalable governance, risk, and compliance tools cover operational risk, policy, compliance, financial management, IT governance, and internal/external audits.

• Follow models and ensure transparency.
• Manage models over your AI’s lifecycle from anywhere.
• Record model metadata for reporting.
• Automatic factsheets give model validators and approvers an up-to-date view of lifecycle details.
• Trust AI results more.
• Collaboration and dynamic user-based dashboards, infographics, and dimensional reporting improve AI process visibility.
• Watsonx.governance enables transparent, accountable data and AI workflows.

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