As far back as of 2018, it was when Amazon’s facial recognition software inaccurately labeled 28 members of the U.S. Congress as criminals, triggering widespread alarm about the implications of AI errors. Fast-forward six years, and the AI-triggered risks persist. A Canadian tribunal ruled that Air Canada must honor a discount promised by its AI chatbot to a customer, rejecting the airline’s defense that the chatbot was a separate legal entity and not liable for false information.
These blunders underscore the pressing need to address AI risks comprehensively.
The AI landscape is fraught with challenges, and GRC practitioners must be equipped to manage these risks effectively to protect their organizations’ bottom lines and success. This blog will unpack the most significant AI risks businesses face today and provide actionable guidance for the reliable, trustworthy, and responsible deployment of AI systems.
Understanding the AI Risk Landscape
All emerging technologies inherently carry risks, introducing new and often unpredictable elements that can impact security, privacy, and functionality. But what makes AI uniquely risky compared to traditional technologies?
AI introduces unique risks that differ from traditional technologies in several ways. Unlike conventional systems that follow predefined rules, AI systems learn and evolve, making their behavior less predictable and more susceptible to unforeseen failures.
AI systems also suffer from hallucinations, in which they infer patterns or information that aren’t true, creating nonsensical or inaccurate outputs. A recent study estimated that AI chatbots can hallucinate between 3% and 27% of the time.
Defining AI Risk
McKinsey has identified eight main categories of AI-related risks. These categories encompass both inbound risks and those directly resulting from adopting AI tools and applications such as the popular Generative AI apps. Every entity should establish a version of these to facilitate understanding and communication about the risks of implementing AI.
- Impaired fairness: Algorithmic bias stems from unrepresentative training data, model performance issues, or misrepresenting AI-generated content as human-created.
- Intellectual property (IP) infringement: Breaching copyright or other legal protections or accidentally leaking IP into the public domain.
- Data privacy and quality: Unauthorized use or disclosure of personal or sensitive information or using incomplete or inaccurate data for model training.
- Malicious use: Creation of harmful AI-generated content such as false information, deepfakes, scams/phishing, or hate speech. Security threats: Vulnerabilities in generative AI systems, such as payload splitting to evade safety filters or manipulating open-source models.
- Performance and explainability: Failure to adequately explain model outputs or inaccuracies, including factually incorrect or outdated responses or hallucinations.
- Strategic: Risks related to noncompliance with standards or regulations, societal impacts, and reputational damage.
- Third-party: Risks linked to using third-party AI tools, such as proprietary data being utilized by public models.
Broaded AI Risks
Other broader risks need to be considered, too.
- Operational Risks: Errors and failures in AI systems that can disrupt business operations.
- Reputational Risks: AI errors can damage a company’s reputation, as seen in high-profile cases like Amazon’s misidentification incident.
- Financial Risks: Incorrect AI decisions can lead to substantial financial losses, such as Air Canada’s incorrect ticket price.
- Regulatory Risks: Keeping up with evolving AI regulations across different jurisdictions is a constant challenge. Also, non-compliance with AI regulations can result in hefty fines and legal repercussions.
- Ethical Risks: AI systems can perpetuate biases and inequities, raising ethical concerns.
- Existential Risks: Highly advanced AI systems could pose existential risks if their goals are not aligned with human values.
In addition, because AI risks are deeply interconnected, a failure in one area, such as an operational mishap, can cascade into reputational damage, financial loss, regulatory scrutiny, and ethical dilemmas. This is why a holistic approach to AI risk management is crucial.
There are valuable resources that can be used to help understand AI vulnerabilities. For instance, the OWASP Top 10 for Large Language Models provides such a framework. It includes issues such as data leakage, inadequate access controls, adversarial attacks, model bias, and insufficient audit logging. This framework helps entities understand and mitigate potential vulnerabilities in their AI systems.
A Deep Dive into Specific AI Risks
Biased Decision-Making
Bias in AI systems is a significant concern. AI models trained on biased data can perpetuate and even amplify discrimination. There have been cases where biased facial recognition systems have been shown to misidentify people of color at higher rates, leading to payouts, wrongful accusations and arrests. Similarly, AI-driven loan approval systems can deny loans to minority applicants based on biased historical data.
To mitigate bias in AI decision-making, use diverse and representative data sets. Also, conduct regular bias audits, employ techniques to de-bias algorithms, and comply with regulations like the NYC Bias Act, which mandates bias audits for AI systems used in hiring.
Privacy Breaches and Data Misuse
AI systems require vast amounts of data, which raises concerns about privacy consent and data misuse. Without consent, AI can inadvertently expose sensitive personal information or use data for unintended purposes, such as targeted advertising.
To prevent this, ensure compliance with data protection regulations like GDPR and CCPA, implement robust data governance frameworks to control data access and usage, and use data anonymization techniques to protect individual privacy. It is worth noting that the EU AI Act, which will soon become enforceable in phases, mandates that high-risk AI systems comply with the GDPR, as stated in Article 47 and Annex V.
Unexpected Operational Failures
The “black box” nature of many AI systems means their decision-making processes are not transparent, and therefore, operational failures are hard to predict and prevent. These systems can make the wrong decisions with severe real-world consequences, such as autonomous vehicles causing accidents or AI trading algorithms triggering market crashes.
To counter this, develop explainable AI models that provide insights into their decision-making processes. Thorough testing and validation must also be conducted to ensure AI systems perform reliably under various scenarios. Finally, human oversight is required to ensure that all outcomes are as expected and explainable. Article 14 of the EU AI Act notes, “Human oversight shall aim to prevent or minimize the risks to health, safety or fundamental rights that may emerge” while using AI systems.
Assessing and Prioritizing AI Risks
AI risk assessments are essential to effectively assessing and prioritizing AI risks. The EU AI Act highlights those businesses employing AI systems, especially high-risk ones, must establish an AI risk management system.
As with any other type of risk management program, the AI risk management system is “a continuous iterative process planned and run throughout the entire lifecycle of a high-risk AI system, requiring regular, systematic review and updating.” The risk management system should:
- Identify and analyze known and reasonably foreseeable risks.
- Estimate and evaluate the risks that may emerge.
- Evaluate other risks possibly arising.
- Adopt appropriate and targeted risk management measures to address the risks identified.
This approach ensures that the identification and evaluation of risks is done with a line of sight on how to address or accept those risks based on business context, criticality, and regulatory impact.
Key Questions for GRC Professionals
When navigating the complex landscape of AI governance and risk management, GRC professionals should consider several key questions to effectively manage and mitigate associated risks. These are:
- What are the potential harms of AI failure?
- What is the likelihood of those harms occurring?
- What existing controls are in place?
- What additional controls are needed?
It is also advisable to use an impact versus probability matrix to prioritize AI risks. This approach helps pinpoint which risks require immediate attention based on their potential impact and the likelihood of their happening.
The Importance of Proactive GRC
AI offers immense potential but comes with significant risks. GRC professionals play a crucial role in managing these risks to ensure AI systems are reliable, fair, and trustworthy. By understanding and addressing AI’s unique challenges, businesses can harness its power responsibly.
This is why proactive AI risk management is not just about preventing disasters; it’s a competitive advantage. Firms that effectively manage AI risks can leverage AI’s benefits while minimizing potential downsides.
What’s the most pressing AI risk your organization faces today? Reflecting on this question can help prioritize your risk management efforts. To dive deeper into AI governance and risk management, schedule a consultation with Elevate. Together, we can develop robust strategies to navigate the complex AI landscape and safeguard your business.