The influence of AI is growing exponentially as leaders integrate this technology in almost every industry. At the same time, concerns about data privacy, security, and how to ensure transparency and explainability for justifying and understanding AI-driven decisions are instigating these leaders on some key aspects of implementing AI:
- Enhance transparency and operationalize AI with confidence: it isn’t always easy to trace how and why decisions were made, even for the data scientists who develop the systems. The “black box models,” models are a growing concern for AI stakeholders.
- Manage risk and reputation: ensuring that results do not reveal bias around race, gender, or age is critical. Building AI systems that are transparent, explainable, fair, and inclusive, can safeguard privacy, security, customer loyalty, and trust.
- Compliance with current and future AI-related regulation: AI deployments must comply and adhere to the laws and regulations that vary by country, and which are evolving at a rapid pace. Companies operating in multiple countries are challenged to meet specific local and country rules.
The IBM research indicates that most leaders are concerned with at least one of the following points:
- Insufficient explainability of decisions made by generative AI.
- Monitoring model quality and mitigating bias.
- Ensuring safety and ethical considerations while adopting AI.
- Trustworthiness of generative AI models.
Leaders are challenged with how to operationalize AI across the AI lifecycle with confidence. While manual tools and processes for developing, deploying, managing, and monitoring the models may be enough when creating a single model as a proof of concept, the usage of automated tools and processes is needed to facilitate the AI model lifecycle, allowing transparency and visibility when scaling AI into production and across the company.
It is important to highlight that AI governance applies not only to generative AI, as all AI models need governance and attention, including machine learning models to identify trends and patterns in data. Governing and monitoring these models to ensure they are explainable and transparent are crucial. As a real example, imagine a bank that has a predictive AI model to analyze loan applications and may need detailed explanations of its decisions in case of an audit process.
Previous image demonstrate some important building blocks for an integrated AI Governance platform, such as watsonx.governance, which can be the response on how clients can automate and consolidate multiple tools, applications, and platforms while documenting the origins of data sets, metadata, and pipelines, resulting in aspects like:
- Increasing predictive accuracy of models by proactively identifying and mitigating bias, and demonstrating the need for model retraining.
- Ensure models perform well enough to meet regulatory standards for metrics such as accuracy and bias.
- Governing models, including those built and deployed in third-party tools anywhere and deployed on cloud or on-premises.
- Monitoring model quality, bias, and accuracy in real-time so businesses can manage risk and protect their reputation.
AI adoption is no longer a choice and offers an opportunity to turn data into insights and actions, amplifying human capabilities, decreasing risk, and increasing return on investment through innovations. However, a well-designed and executed AI strategy should be constructed on reliable data with automated tools designed to provide transparent and explainable outputs.
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