What is AI governance? How to get started?

Improving efficiencies, saving time, and decreasing costs are why businesses are employing AI. Forbes Advisor found that over 50% of businesses use AI for cybersecurity and fraud management, while almost two-thirds believe AI will improve customer relationships. Here are some key benefits of AI for businesses:

• Automating workflows: AI can make a business workflow more efficient by automating routine operations.
• Virtual assistance: An AI virtual assistant can understand voice commands and perform tasks for the user.
• Understanding behavior: AI can help companies learn more about how their customers act and what they like.
• Improving customer experience: Businesses can personalize buying experiences and increase customer satisfaction with the use of AI.
• Enhancing user experience: AI can help businesses improve user experience by providing personalized recommendations and content.
• Increasing productivity and operational efficiencies: AI can help businesses automate processes and reduce the time and cost of manual tasks.
• Making faster business decisions based on outputs from cognitive technologies: AI can help businesses make data-driven decisions by analyzing large amounts of data.
• Reducing the risk of cybersecurity attacks: AI can help businesses detect and prevent cyber-attacks by identifying patterns and anomalies in data.

AI is also being uniquely used in some industry settings. Here’s just a few examples:

• Healthcare: AI is helping doctors diagnose diseases, predict patient outcomes, and develop personalized treatment plans.
• Finance: AI is helping banks detect fraud, automate customer service, and provide personalized investment advice.
• Retail: AI is helping retailers optimize pricing, forecast demand, and personalize marketing campaigns.
• Manufacturing: AI is helping manufacturers optimize production, reduce downtime, and improve quality control.
• Transportation: AI is helping transportation companies optimize routes, reduce fuel consumption, and improve safety.

Without a proper AI governance framework and strategy, organizations may be vulnerable to substantial risks from AI systems and may not realize the benefits they hope to achieve. Here’s some of the more common risks to businesses.

• Bad business decisions: AI's ability to make decisions is strongly reliant on accurate and trustworthy data. However, if any incomplete, inaccurate or biased datasets are input into an AI model, all resulting business decisions are at risk and can lead to consequences that might include missed opportunities, financial losses and a decline in competitiveness.
• Exposure of proprietary or privileged information: As organizations increasingly entrust and input their datasets into AI models, the risk of exposing personal and confidential data, as well as intellectual property, to unauthorized internal and external entities is heightened. Because AI algorithms are autonomous, there is a risk of unintended exposure of this information, as these systems may work independently and produce results that might jeopardize sensitive data.
• User distrust due to a lack of transparency: Because of their complex nature, AI systems' decision-making processes are not always easy to discern. Stakeholders and users may be suspicious of the results from AI due to this lack of transparency. And this lack of transparency can result in not realizing the benefits from AI that were planned.
• Reputational damage: In a recent report from the World Economic Forum, more than 75% of chief risk officers agreed that the use of AI technologies is posing reputational risks to their organization. AI software provides information based on patterns and relationships it has learned from the data it has been fed, making it prone to bias and misinformation. This can result in massive damage to a company’s reputation if their use of AI results in harms to employees, customers, business partners or the general public.
• Regulation and legal risks: The laws and regulations related to AI are still changing as the technology continues to evolve. Regulation and legal risks may originate from a variety of sources, such as violations of the law, non-compliance with industry standards, liability for AI-caused harms, disputes over intellectual property, disregard for human rights and ethical issues.

While some organizations have embraced artificial intelligence rapidly and broadly, a more measured approach is warranted to ensure the proper safeguards are in place. Here are some principles for responsible AI governance every organization should consider:

• Make transparency your goal: Transparency, which refers to the structure, observability, and comprehension of data, lies at the heart of successful AI initiatives. As an example, you will need your governance tools to show you how to avoid discriminatory AI and everywhere you’re storing personally identifiable information (PII). Otherwise, you will be flying blind.
• Create an AI center of excellence: To foster safe, profitable AI use, develop an in-house center of excellence focused on AI. This centralized approach will help to minimize departmental AI silos that may not gather the proper input from the business or implement the proper safeguards around data.
• Unite data science with business and data governance experts: Data scientists with a thorough grasp of data relationships create AI models. They may not, however, have the same amount of contextual business expertise as those that work across the business. It is critical to establish collaboration between data scientists, data stewards and other business professionals to ensure that AI models are generated with the appropriate context. This will assist data scientists in understanding the subtleties of the company to develop more effective models and ensure trusted, high-quality data is being leveraged in the training of AI models for business benefit.
• Capture and maintain institutional knowledge: Employee turnover can result in situations where AI models have been developed by individuals that are no longer at the company resulting in an AI black box. To prevent institutional knowledge from being lost, organizations need to use tools, procedures, and systems to store that knowledge. Even when people come and go, this practice will help to retain, sustain, and build institutional knowledge.
• Uphold AI safety principles with automation: Automation tools can make it easier for you to observe changes in your data strengthen your protections around AI, which in turn will help you uphold safety principles such as those found in the White House-led Blueprint for an AI Bill of Rights:
  
  • Protection from faulty or unsafe AI systems
  • Protection from discriminatory AI and algorithms
  • Privacy and security when dealing with AI
  • Notice and explanation when an AI system is being used and how that can affect outcomes
  • The right to opt out
• Focus on data quality: Data scientists are known to spend far more time searching for and cleaning up data than they do developing logic. An AI governance framework can reverse the ratio of time spent cleaning data against time spent leveraging data for insights by capturing the definition, structure, lineage, and quality of the data. It can direct users and analysts to reliable data rather than making them search for it.
• Use the right model for the right purpose: Companies sometimes use the right model for the wrong purpose. For instance, using a sales forecasting model that generates accurate predictions on North American sales because it was trained on North American data may offer wildly inaccurate predictions about sales in East Asia. Relevant training data is required for accurate predictions.
• Monitor and adjust for model drift: Model or data drift happens when a model is trained on a certain dataset and later used in a different context. Although anomalies and biases may be undetectable in your early testing, your AI model will continue to adapt to the new environment, leading the resultant predictions to change over time. And even if your AI models perform flawlessly, they will eventually begin making judgments that you would not foresee. That is why governance and oversight are critical. To guarantee that your AI model is effective, it is critical to monitor drift by constantly watching the movement and context of your data through data observability or continuous data monitoring tools. This allows you to detect drift and make required changes to your model to guarantee that it continues to function well and address any data quality issues quickly with data remediation to bring quality back inline.

Most business users view AI as a black box; they just enter their input and get a result. Their knowledge of the data that the software uses to make its predictions is limited. An AI governance framework is meant to help turn the black box into a glass box by helping users better understand the data the AI model relies on. The better they understand the data, they are more likely to accept predictions that are based on that data.

Given this, data intelligence, the foundation for data governance, is vital to AI success. And there are four key data intelligence components that can help you better manage and fuel your AI models for your organization’s best use:

• AI model curation and sharing: Managing, curating and sharing all the AI models you do have within one repository, such as an internal data marketplace, makes it easier to govern your AI efforts. It also ensures you are in the best future position to readily respond to regulatory requirements as they develop.
• Data quality: Data quality capabilities that automate the measurement of data quality and provide data quality visibility help to ensure the data used to train AI models is, and remains, at the level needed for AI model accuracy. Data observability tools can detect and alert you to data drifts early so that you can take appropriate action.
• Data lineage: Data lineage that enables you to see how data has been sourced and transformed, right down to the column level of data, throughout its journey through your organization can help you better understand the data’s fitness and appropriateness for your intended use.
• Data value scoring: With automated data value scoring based on data quality scoring, previous data consumer ratings and the amount of available governance detail, data scientists are in a much better position to quickly hone in on the most valuable and trusted organizational data available for AI model use.