Businesses are increasingly turning to artificial intelligence (AI) as a tool for innovation and growth. A recent Gartner survey found that 44% of companies are now using AI in some capacity, up from 37% last year.
But with this growth comes responsibility. Without proper oversight, businesses risk mismanaging the use of AI tools, potentially leading to ethical concerns and regulatory issues. Strong AI governance is no longer optional but an essential consideration for any business looking to thrive in the AI era.
The use of AI brings new challenges for risk managers
Risk managers face numerous challenges in managing and governing AI technologies. One of the biggest hurdles is the absence of centralized AI oversight. With AI systems deployed across various departments, the task of tracking AI assets and ensuring cohesive management becomes a formidable obstacle. This fragmentation can lead to unmanaged deployments, escalating the risk of ethical lapses and regulatory non-compliance, fines, and penalties.
New AI regulations will have a substantial impact on how organizations use AI. Navigating the intricate requirements of the European Union (EU) AI Act and other regulatory frameworks can be daunting. Risk managers must continuously update policies and controls to adhere to evolving standards, which can be resource intensive and prone to errors.
Identifying, assessing, and mitigating risks, including biases in AI models, is critical to avoid legal and reputational damage. However, risk management programs tend to lack the necessary tools and expertise to conduct thorough risk assessments and audits, leaving them vulnerable to unintended consequences of AI usage.
Transparency and explainability of AI processes are crucial yet challenging to achieve. Stakeholders often struggle to understand and trust AI decision making due to the opaque nature of many AI models. Without clear explanations, gaining stakeholder buy in and ensuring accountability becomes difficult.
Furthermore, data governance is a critical area where many organizations falter. Ensuring data quality, integrity, and security throughout the AI lifecycle is essential. Maintaining high standards and complying with data protection regulations requires robust governance practices that many organizations find challenging to implement effectively.
What is AI Governance?
The purpose of AI governance is to avoid and mitigate potential harm and build trustworthy AI systems that serve the interests of your customers, employees, community, and society. AI governance is a framework of policies, processes, and controls designed to ensure that AI systems are developed, deployed, and used ethically, responsibly, and in compliance with legal and societal norms.
When AI systems are employed to make decisions affecting individuals, there is a risk of unintended harm to customers, employees, communities, or broader society. AI governance must consider the potential risks and impacts at every stage of the AI lifecycle.
Trustworthy AI has varied definitions based on perspective, yet most converge on a set of core principles:
The European Union (EU) AI Act defines trustworthy AI as being "legally compliant, technically robust, and ethically sound."
The National Institute of Standards and Technology (NIST) outlines characteristics of trustworthy AI in its AI Risk Management Framework (AI RMF), including valid and reliable, safe and secure, accountable, transparent, explainable, privacy-enhanced, and fair with regard to managing harmful bias.
Five questions to ask your risk management team to evaluate your AI readiness
How do you manage and track all AI assets across your business?
What steps have you taken to ensure compliance with the EU AI Act?
How do you assess and mitigate risk and biases in your AI models?
How transparent are your AI decision-making processes to stakeholders, and what tools do you use to ensure explainability?
How scalable are your AI Governance practices to ensure compliance with new and changing AI Governance regulations?
The answer to these questions is not a simple yes or no. They require a thoughtful and thorough evaluation of the AI initiatives in use and the policies and processes in place to govern them. This evaluation should involve collaboration between risk managers, IT leaders, data scientists, and other key stakeholders to ensure a holistic understanding of AI usage across the organization.
83% of business leaders believe they need to adopt AI governance frameworks to ensure ethical AI usage and reduce bias. World Economic Forum May 2024
By regularly evaluating and adapting AI governance practices, the risk management function can anticipate potential risks and stay ahead of regulatory changes. Employing a robust AI Governance program also demonstrates a commitment to stakeholders and promotes trust in the organization's use of AI technologies.
Introducing Archer AI Governance
Archer AI Governance empowers risk managers to tackle these challenges and ensure responsible AI use throughout the organization. Aligned with the stringent requirements of the EU AI Act, Archer AI Governance provides a robust suite of features that help to manage AI risks effectively, maintain compliance, and promote ethical AI practices.
Interested in learning how Archer AI Governance can help your organization effectively manage AI usage risks? Archer clients and partners are invited to join us on October 4 for a Free Friday Tech Huddle.
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