IBM - AI Governance: Break open the black box

Key players insights
IBM - AI Governance: Break open the black box
By Priya Krishnan | 5 minute read

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation. Today, AI presents an enormous opportunity to turn data into insights and actions, to amplify human capabilities, decrease risk and increase ROI by achieving break through innovations.

While the promise of AI isn’t guaranteed and doesn’t always come easy, adoption is no longer a choice. It is an imperative.
Those businesses that decide to adopt AI technology will have an immense advantage, according to 72% of decision-makers. Furthermore, 59% of executives claim AI can improve the use of big data in their organizations, facts about artificial intelligence show.
IBM Global AI Adoption Index 2022
What is stopping AI adoption today?

3 main reasons why organizations struggle with adopting AI

1. Lack of confidence to operationalize AI

Many organizations struggle when adopting AI. This is due to:
  • An inability to access the right data
  • Manual processes that introduce risk and make it hard to scale
  • Multiple unsupported tools for building and deploying models
  • Platforms and practices not optimized for AI

“According to Gartner 54% of models are stuck in pre-production because there is not an automated process to manage these pipelines and there is a need to ensure the AI models can be trusted.” (Gartner AI in organizations survey.)

Well-planned and executed AI requires reliable data backed by transparent, automated tools and explainable processes. Success in delivering scalable enterprise AI necessitates the use of AI tools and processes that are specifically made for building, deploying, monitoring and retraining models.

2. Challenges around managing risk

Customers, employees and shareholders expect organizations to use AI responsibly, and government entities are demanding it. This is critical now, as more and more share concerns about brand reputation with their use of AI. No one wants to be in the news for the wrong reasons. Increasingly we are also seeing companies making social and ethical responsibility a key strategic imperative.

3. Scaling with growing AI regulations

With the growing number of AI regulations, responsibly implementing and scaling AI is a growing challenge, especially for global entities governed by diverse requirements and highly regulated industries such as financial services, healthcare and telecom. Failure to meet regulations can lead to government intervention in the form of regulatory audits or fines, damage to the organization’s reputation with shareholders and customers, and revenue loss.

The solution: AI Governance

AI governance is an overarching framework that uses a set of automated processes, methodologies and tools to manage an organization’s use of AI. Consistent principles guiding the design, development, deployment and monitoring of models are critical in driving responsible, trustworthy AI. These principles include:

  • Know your model: Model transparency starts with the automatic capture of information on how the model was developed and deployed. This includes capturing of the metadata, tracking provenance and documenting the model lifecycle. Model transparency promotes explainable AI driving trusted results that build public confidence, promote safer practices and facilitate further AI adoption.
  • Trust your model: Complying with rules, regulations and driving AI that minimizes bias requires well defined and automatically enforced enterprise policies, standards and roles. Manual manipulation of data and models can introduce costly errors with far-reaching consequences. In addition, the automation of enforcement rules for validation drives model retraining and reliability to address drift now and over time.
  • Use your model: Transparent and explainable AI requires the automation of the analysis of model performance against KPIs while continuously monitoring in real-time for bias, fairness and accuracy. The ability to track and share model facts and documentation across the organization provides backup for analytic decisions. Having this backup is crucial when addressing stakeholder, customers and concerns from regulators.

At IBM, we believe AI governance is the responsibility of every organization, and will help businesses build more trustworthy AI that is transparent, explainable, fair, robust and respecting of individual privacy. Responsible AI requires upfront planning, automated systems and the governance necessary to drive fair, accurate, transparent and explainable results.

The three foundational capabilities of the IBM AI Governance solution

IBM AI Governance is a new one-stop solution built on IBM Cloud Pak for Data. It is designed to help businesses meet their regulatory requirements and address ethical concerns through software automation. It drives a complete governance solution without the excessive costs of switching from your current data science platform.
Everything needed to develop a consistent transparent model management process is included in IBM AI Governance. This includes repeatability and the ability to capture of model development time, metadata, post-deployment model monitoring, and to customize workflows. IBM AI Governance is built on three critical principles, meeting the needs of your organization at any step in their AI journey:

1. Lifecycle Governance: Monitor, catalog and govern AI models from anywhere and throughout the AI lifecycle
  • Automates the capture of model metadata across the AI/ML lifecycle to enable data science leaders and model validators to always have an accurate, up-to-date view of their models. Lifecycle governance enables the business to operate and automate AI at scale to ensure that the outcomes are transparent, explainable and devoid of harmful bias and drift. This increases the accuracy of predictions by identifying how AI is used and where corrective action is indicated.

2. Risk Management: Manage risk and compliance to business standards, through automated facts and workflow management
  • Model risk management is used to identify, manage, monitor and report on risk and compliance initiatives at scale. Dynamic dashboards provide clear, concise customizable results that enable a robust set of workflows, enhanced collaboration and helps to drive business compliance across multiple regions and geographies.

3. Regulatory Compliance: Help to proactively ensure compliance with current and future regulations
  • Translate external AI regulations into a set of policies for various stakeholders that can be automatically enforced to ensure compliance. Users can manage models through a dynamic dashboard and that tracks compliance status across all policies and regulations.