Artificial intelligence (AI) systems are increasingly integrated into various sectors, including healthcare, finance, and customer service, making their reliability and accuracy critical. However, one lesser-known issue that can arise with AI systems is confabulation (aka hallucinations, fabrications). This blog post will delve into what confabulations in AI are, why they occur, and how they can be effectively prevented and detected during governance reviews.
What Are Confabulations in AI?
Confabulation in AI refers to a situation where an AI system generates false or misleading information as a byproduct of its operational framework. In AI, confabulations typically arise from flaws in the data processing or learning algorithms that cause the system to assert false information as truths.
How Do Confabulations Happen in AI?
Confabulations in AI can stem from several sources:
- Data Quality and Bias: Poor data quality or biased datasets can lead AI systems to make incorrect associations and predictions. For instance, an AI model trained on historical hiring data might confabulate by perpetuating past biases.
- Model Overfitting: AI models that are overfitted to their training data can fail to generalize to new, unseen scenarios, leading to confabulations when they encounter different inputs in real-world applications.
- Lack of Diversity in Training Data: If the training data are not diverse enough, the AI might not have a comprehensive understanding of different contexts, leading to erroneous outputs.
- Complex Model Architectures: Highly complex models, like deep neural networks, can sometimes behave in unpredictable ways due to their ‘black box’ nature, making it difficult to trace the reasoning behind certain decisions.
Preventing Confabulations
Preventing confabulations in AI involves several proactive steps:
- Improving Data Quality: Ensuring that the data used for training AI systems are high quality, representative, and free from biases is crucial.
- Regular Model Evaluation: Continuously evaluating the model against new and diverse datasets can help in identifying and mitigating confabulations.
- Implementing Robust AI Governance: Establishing strong AI governance frameworks that include transparency, accountability, and ethical guidelines can significantly reduce the risk of confabulations.
- Simplification of Models: While complex models are often necessary, simplifying AI models where possible can enhance their interpretability and reduce the likelihood of confabulating outputs.
Spotting Confabulations During Governance Review
Detecting confabulations during the governance review of AI systems is essential for ensuring their reliability. Here are some strategies:
- Audit Trails: Maintaining comprehensive audit trails that document the decision-making processes can help reviewers trace back the origins of a confabulation.
- Stress Testing: Subjecting AI systems to stress tests using scenarios that were not part of their training can help in spotting unexpected confabulations.
- Peer Reviews: Engaging external experts to review the AI’s performance and the decision-making process can uncover potential weaknesses that lead to confabulations.
- Utilization of Explainability Tools: Employing AI explainability tools (aka explainable AI, or XAI) can make it easier to understand how an AI system arrived at a particular decision, thereby helping to spot confabulations.
Conclusion
Confabulations in AI, though not widely discussed, pose a significant challenge to the reliability and trustworthiness of AI systems. By understanding their causes, implementing preventive measures, and employing robust detection methods during governance reviews, organizations can mitigate the risks associated with AI confabulations. This ensures that AI systems perform as expected and contribute positively to decision-making processes across various industries.
Learn how we are addressing confabulations in AgileWriter™ in adherence with the cautious guidance of the World Health Organization (WHO) in our other blog post.
