Organisations are investing in AI tools and strategies to optimise their processes to gain a competitive edge. In some cases, entire industries are transitioning to a reliance on AI, which comes with a heightened risk that must be managed. As artificial intelligence (AI) technology continues to advance rapidly, organisations must be prepared to respond to the significant changes it will bring to their operations on a global scale. Indeed, it is essential to understand the risks associated with AI and the best practices for managing them to ensure a successful implementation of AI technology. This blog provides an overview of AI risk management, including a summary of the risks and best management practices.
On 26 January 2023, the National Institute of Standards and Technology (NIST), a leading voice in AI standards, released AI RMF 1.0, the Artificial Intelligence Risk Management Framework. AI RMF defines risk as "the composite measure of an event's probability of occurring and the magnitude of its consequences". According to NIST, AI risks are the potential harms to people, organisations or systems resulting from developing and deploying AI systems. Examples of harm range from sexist hiring tools to uncontrollable trading algorithms causing market crashes. These risks can stem from the data used to train and test the AI system, the system itself (i.e., the algorithmic model), how it is used and its interaction with people.
Given AI systems' potential dangers, proactively monitoring AI-based products and services is essential. One way to achieve control and help ensure safety and security is by adopting a risk management solution to triage, verify, and mitigate AI risks.
An effective AI governance, risk, and compliance process enables organisations to identify and manage risks. At a high level, AI governance can be broken down into three major approaches:
Ensuring that harmful or unintended consequences are minimised or do not occur during the lifespan of AI projects requires a comprehensive understanding of the role of responsible principles during the design, implementation and maintenance of AI applications.
AI Risk Management is the process of identifying, assessing, and managing risks associated with using AI technologies. This includes addressing both technical risks (such as security vulnerabilities and algorithmic bias) and non-technical risks (such as ethical considerations and regulatory compliance). It involves understanding the potential risks and benefits of AI, developing strategies and policies to mitigate potential risks, and monitoring and responding to changes in the AI environment. Additionally, AI Risk Management also includes creating processes and systems to ensure compliance with ethical and legal standards, as well as internal and external policies.
When assessing a system, it is important to consider five main risk verticals:
AI risk management will soon be codified by regulations that are being proposed around the world.
The European Union’s proposed AI Act aims to create an ‘ecosystem of trust’ that manages AI risk and prioritises human rights in developing and deploying AI by adopting a risk-based framework to govern its use. In the United States, the White House has published a Blueprint for an AI Bill of Rights, which outlines the US government’s vision for AI governance to prevent harm. To add, China has proposed a suite of legislation to regulate different applications of AI.
Providing a global explanation for an algorithm may seem straightforward. But organisations must make substantial structural changes in anticipation of AI implementation to ensure that their automated systems operate within legal, internal, and ethical boundaries.
Therefore, organisations with robust governance and risk management are best placed to ensure compliance with the increasing number of AI or use case-specific rules. Furthermore, by embedding a risk management framework, an organisation can move away from a costly, reactive, ad hoc approach to regulation.
Although AI adoption is soaring, risk management is lagging. The trouble is that many companies need help seeing that they have a problem. According to a report released by MIT Sloan Management Review and Boston Consulting Group, AI was a top strategic priority for 42% of the report’s respondents. Still, only 19% said their organisation had implemented a responsible-AI program. The gap increases the possibility of failure and exposes companies to regulatory, financial, and reputational risks. While AI risk management can be started at any point in the project development, implementing a risk management framework sooner than later can help enterprises increase trust and scale with confidence. Advantages of AI Risk Management include:
AI risk management will define the next era of technological advancement and become essential to companies’ AI strategies. Given AI’s rapid development and increasing applications, AI risks are constantly changing and evolving, meaning that comprehensive risk management strategies are needed to avoid reputational damage and facilitate legal compliance.
Auditing and testing AI systems reveal whether issues in a system’s development, training, or deployment will lead to biased decision-making. Where any issues are found, these can be addressed with state-of-the-art mitigation techniques. In doing so, organisations can maximise their ability to innovate with confidence.
Interested in learning how to implement AI Risk Management strategy? Reach out to us at email@example.com
Authored by Ayesha Gulley, Public Policy Associate at Holistic AI
Subscribe to our newsletter!
Join our mailing list to receive the latest news and updates.
Our automated AI Risk Management platform empowers your enterprise to confidently embrace AIGet Started