Mastering the Machine: Navigating the Top 10 AI Risks

Artificial intelligence (AI) continues to reshape how organisations do business, offering unprecedented opportunities to improve efficiency.  AI can enhance decision-making and revolutionise entire sectors. However, the use and integration of AI also brings a new set of risks and challenges that organisations must address. As our reliance on AI grows, so do the AI risks.  Building trust in AI technologies is a key priority.

In this publication, we review recent incidents that have exposed some AI vulnerabilities.  We explore the top 10 AI risks organisations face today. We provide insights into how each risk can arise and offer strategies to manage each of the AI risks identified.

Recent AI Incidents and Issues

The growing use of AI has exposed vulnerabilities through high-profile incidents, sparking concerns over the technology’s safety, fairness, and reliability. These events highlight the need for stronger risk management frameworks to ensure responsible AI deployment. Here are some examples.

The 2020 Twitter Hack and AI’s Role

In July 2020, Twitter (now X) experienced a major security breach where hackers gained access to high-profile accounts, including those of Barack Obama, Elon Musk, and others. Whilst it was a social engineering attack, AI may have played an indirect role to craft more convincing spear-phishing emails and messages. This incident raised alarms about AI risks and AI’s potential to enhance the sophistication of cyberattacks and manipulate human behaviour on social platforms.

Bias in AI-Powered Hiring Tools

Several companies have adopted AI to streamline their hiring processes. However, in 2018, Amazon scrapped its AI recruiting tool when it was revealed it was biased against women. The system, trained on past hiring data, penalised resumes that included the word “women’s” and disproportionately favoured male candidates. This incident highlighted how AI systems, when trained on biased datasets, can perpetuate and even exacerbate existing inequalities.

AI and Autonomous Vehicles

The development of autonomous vehicles is another area where AI failures have serious consequences. In 2018, an Uber self-driving car killed a pedestrian in Arizona. The investigation revealed that the car’s AI failed to properly classify the pedestrian as a hazard and react appropriately. This incident underscores the potential for AI to make critical errors in real-world applications, particularly in safety-critical industries like transportation.

GPT-3 and Content Misinformation

Open AI’s GPT-3, once one of the most advanced language models, has shown remarkable abilities in generating human-like text. However, it has also demonstrated the potential for generating false information, hate speech, and harmful content when used without adequate oversight. GPT-3’s ability to generate misleading or harmful content at scale presents significant risks, especially in areas like media, customer support, and content creation.

This list is not comprehensive and the examples reflect only a fraction of the challenges associated with AI. That is why, understanding the broader landscape of AI risks is critical for managing and mitigating potential hazards.

Top 10 AI Risks

AI technologies are rapidly evolving, which makes it difficult to keep up with all the potential AI risks. New algorithms, models, and applications can introduce unforeseen risks, making risk management a constantly moving target.  Organisations must understand the key risks associated with AI to effectively manage them/  Here is our list of the top 10 AI risks.

1. Risk of Bias and Discrimination

AI systems rely heavily on large datasets and learn from the data they are trained on. If this data is biased—reflecting historical inequalities, discrimination, or stereotypes—the AI will likely perpetuate or even amplify those biases. Poor-quality or incomplete data can also lead to incorrect predictions and decisions.

As we saw with the Amazon incident above, biases can result in unfair treatment in various contexts, such as hiring, lending, law enforcement, and healthcare. Specifically, predictive policing algorithms have been criticised for disproportionately targeting minority communities due to biases in historical data.

Ensuring that the data is accurate and AI systems are trained on diverse and representative data is essential for reducing bias.

Strategies to manage the risk of bias and discrimination include:

• Ensuring diverse and representative training data sets that accurately reflect the diversity of real-world populations and scenarios.
• Implementing fairness testing throughout the AI development and deployment lifecycle.
• Regular auditing to detect bias that may develop over time or as the system encounters new data.

2. AI Security Vulnerabilities

AI can introduce new types of vulnerabilities in cybersecurity. The systems may be susceptible to new forms of cyberattacks, such as adversarial attacks, where small alterations to inputs cause the AI to make incorrect decisions. AI-driven systems, particularly those involving machine learning, may be vulnerable to adversarial attacks. These attacks involve manipulating input data in ways that deceive the AI system into making incorrect decisions.

For instance, a carefully altered image might cause an AI system to misclassify it, leading to potentially dangerous consequences in applications like facial recognition or autonomous vehicles.

Identifying and defending against cybersecurity vulnerabilities in AI systems requires a multi-faceted approach.

Strategies to better manage AI security vulnerabilities risks include:

• Implementing robust cybersecurity measures to secure AI systems from unauthorised access, manipulation, and cyber threats that could compromise their integrity or functionality.
• Conducting regular penetration testing to identify and fix security vulnerabilities in AI systems by simulating cyberattacks and malicious behaviour.
• Using encryption and access controls to ensure data confidentiality and integrity.
• Continuous monitoring for anomalies and using adversarial training techniques can help harden AI systems.

3. Lack of Transparency and Explainability

Users and stakeholders may have difficulty trusting AI systems that lack clear explainability. Why? Many AI systems, particularly deep learning models, operate as “black boxes”, where it’s difficult to understand how they arrive at specific decisions. This lack of transparency can be problematic in industries like healthcare, finance, or criminal justice, where understanding the rationale behind a decision is crucial.

For example, if an AI system denies someone a loan or insurance claim, that individual may have little recourse to challenge the decision because the reasoning behind it is opaque. Explainability and transparency are essential for ensuring trust and accountability in AI systems.

Ensuring that AI outputs can be understood and justified is essential for building trust, especially in high-stakes domains like healthcare and finance.

Strategies to manage the risks around lack of transparency and explainability include:

• Using explainability tools like LIME (Local Interpretable Model-Agnostic Explanations) or SHAP (SHapley Additive exPlanations) to enhance understanding of AI model predictions by using tools that provide insights into how models arrive at their decisions.  This means the rationale behind their decisions can be understood by both technical and non-technical stakeholders.
• Maintaining up to date documentation of AI models to maintain clear, transparent and comprehensive documentation for AI models, outlining their development, training data, features, and decision-making processes to promote accountability and understanding.
• Keeping stakeholders informed about the AI system’s decision-making processes and outcomes.

4. Autonomous Decision-Making and Accountability

AI systems may make decisions without human oversight, leading to errors or unintended outcomes. As AI systems increasingly make autonomous decisions, it becomes challenging to assign responsibility when things go wrong.  This raises questions of accountability. Who is responsible when something goes wrong? The developer, the operator, or the manufacturer?

In the Uber self-driving car case, legal and ethical questions arose regarding liability for the pedestrian’s death.

Clear accountability frameworks are

Strategies to manage autonomous decision-making and accountability risks include:

• Introducing human-in-the-loop systems (reviews) for critical decisions to ensure that human judgment and oversight are incorporated into the decision-making processes of AI systems, particularly for high-stakes AI applications.
• Regular testing and validation of AI decisions to continuously assess the performance and reliability of AI systems to ensure their outputs remain accurate and fair over time.
• Establishing accountability frameworks to create clear structures for accountability in AI decision-making processes to ensure responsibility for outcomes and adherence to ethical standards.

10. Societal Implications and Economic Disruption

AI and automation threaten to disrupt labour markets by displacing jobs, particularly in sectors that rely on routine, manual tasks. This can have societal implications and longer term economic impacts.  While AI can create new jobs, the transition may be painful for workers whose skills become obsolete.

Ensuring that the workforce is adequately prepared for this transition, through education and retraining programs, is essential for mitigating the economic risks posed by AI.

Take outs

This publication highlights the growing influence of Artificial Intelligence on business operations, offering opportunities to enhance efficiency, decision-making, and sector-wide transformation. As AI’s adoption increases, so do associated risks and challenges. Recent incidents, such as the bias in AI-powered hiring tools and examples of exploitation by threat actors, underscore vulnerabilities in AI systems, especially regarding security, fairness, and accountability.

AI risks are not static.  The top 10 AI risks presented are simply a starting point for organisations.

To manage these top 10 AI risks, we have identified a number of risk treatment strategies such as data auditing, cybersecurity measures, ethical guidelines, regular retraining, and accountability frameworks. By proactively addressing these challenges, organisations can navigate these AI risks more effectively.