Preventing Hallucinations in AI Apps with Human-in-the-Loop Testing
Artificial intelligence (AI) apps are becoming increasingly crucial for individual customers and businesses alike. These apps bring many benefits, such as task automation, efficient analysis of large data sets, and data-informed decision-making, making AI-powered applications highly valuable. As a result, DevOps teams working on AI apps can’t afford poor performance.
Unlike regular apps, the performance of AI applications and systems is measured on their accuracy, precision, recall, and predictive abilities. This makes AI hallucinations a significant threat to the success of AI-powered apps.
What Are AI Hallucinations?
AI hallucination is a phenomenon in which an AI app or system generates incorrect outputs based on non-existent patterns. This means the model sees or invents a pattern it is not trained on and produces an imaginary response. AI hallucinations can occur in different forms, including text, images, and audio.
While there is an argument to be made that AI hallucinations can be reduced by giving the model large data sets to learn from, hallucinations can still occur when exposed to unfamiliar scenarios.
What Causes AI Hallucinations?
AI hallucinations occur for several reasons, such as:
- Overfitting: Overfitting is a common issue and occurs when AI models become too specialized on a specific dataset and produce errors when presented with new data.
- Poor Quality Training Data: Poor quality learning data can cause AI systems to hallucinate patterns that are not representative of real-world data. This can occur when the training data is not diverse enough, corrupted, or contains a lot of noise. Additionally, changes in the underlying distribution of the data over time can cause the model to hallucinate patterns that were present in the training data but are no longer relevant.
- Biased Data: If an AI model is trained using biased data, it may replicate those biases. This can lead to inaccurate and unfair predictions, potentially affecting the model’s overall accuracy.
The Importance of Human-in-the-Loop Testing and Validation
Testing and validation and Human-in-the-Loop (HITL) feedback are crucial in preventing AI hallucinations. Rigorous testing and validation with human testers expose the AI system to a wide range of input data and scenarios to ensure that it is making accurate predictions. This approach helps identify and address potential issues in the system before they become significant problems, reducing the likelihood of AI hallucinations.
How HITL Detects Potential Issues That Cause Hallucinations
Mitigating Biases
Although AI technology has made remarkable advancements, it still lacks the intuition and creativity necessary to identify biases accurately. On the other hand, humans are skilled at recognizing patterns and identifying biases that may not be evident to a model. They can provide feedback on the model’s predictions and highlight instances where it may make decisions based on biased or incomplete information. This feedback can help modify the model’s training data or architecture, reducing biases and preventing errors.
Understanding Context
Humans possess a contextual understanding that cannot be replicated in the data used to train AI models. Therefore, human feedback plays a crucial role in bridging the gap between the limitations of the training data and the complexities of real-world scenarios. When human feedback is incorporated into the model, it can significantly enhance its accuracy and reliability, especially when the context of the decision-making process is crucial.
Adapting to Unfamiliar Situations
AI models are typically trained on historical data and use it to make predictions and decisions about new scenarios they encounter. However, training data isn’t always comprehensive enough to cover every situation the model may face in the real world. This is where HITL comes in. Human input helps the model adapt to new scenarios and data it may not have encountered during training. This ensures the model can generalize to new situations and make informed decisions based on real-world data.
Continuously Improving the System
One of the critical advantages of AI apps is their ability to learn from data and improve their performance over time. However, even the most sophisticated AI models can make mistakes, resulting in hallucinations. In such cases, humans can play a crucial role in continually correcting errors and providing feedback to improve AI app performance. This feedback lets you update the model’s training data, enhancing its accuracy in future interactions.
Layering HITL for Optimal Performance
Leveraging the right mix of humans and machines is critical to ensure your AI app is reliable, trustworthy, and accurate. However, many AI testing solutions today do not effectively incorporate human input. Some solutions focus solely on technology, making them inflexible and limited in scope. Others rely too heavily on human testers, leading to inconsistencies, inefficiencies, and potential errors.
At Testlio, our AI testing solutions leverage the speed, scale, and accuracy of machines while tapping into the intuition and creativity of diverse humans to identify contextual nuances and create real-life test cases. Our approach guarantees efficient, relevant, and precise results that are impossible to achieve through tool-only testing.
Schedule a call to learn how we can help you eliminate biases, hallucinations, and errors from your AI apps.