Knowing your AI models’ actions is essential for understanding when they go wrong. However, due to the scale of data that flows through AI systems it can be difficult to determine what exactly is happening. This is where AI observability comes in.
Observability
Observability refers to obtaining a deep understanding of your model’s performance across multiple stages of the ML lifecycle. It often involves a wide range of tools that allow you to split the data into smaller chunks which you can manually look at, or assign a high-level summary of some key metrics.
Hyperscience believes that observability is crucial to creating performant and ethical pipelines. Some tools we provide to assist are:
Training Data Management
Our Training Data Management (TDM) system lets you view, manage, and update the data used to train the models deployed to your flows. Within the TDM System, we have capabilities to help you identify the best data to include in training, identify inconsistencies within your ground truth annotations, and help you annotate the data easily. By using this system, you can better understand what data the model was trained on and therefore when the model is effective .
Model Accuracy
Creating models that precisely fulfill our intentions is crucial. Model accuracy is an excellent window into the predictions that these models are making.
Often, however, model accuracy doesn’t tell the whole story. For example, let’s look at a model that predicts if an earthquake is about to happen. If it always predicts no earthquake, its accuracy would be high, but it wouldn’t perform as we want. This is why it is important to break down accuracy by category. Hyperscience allows its users to do precisely this inside their models tab. On the model tab page, you can see a breakdown of the field accuracy and how the accuracy has changed over time.
Drift Detection
The Hyperscience document drift management works by grouping unmatched pages based on common visual templates. These pages are then automatically clustered into groups, making it easier to identify potential new layouts and determine what sorts of data you are feeding your AI model. It is important to have a diversity of documents sent in, but you need to make sure that your model can handle any new document types.
AI Bias
AI bias refers to the systematic errors that can occur in artificial intelligence systems due to the influence of human biases and inequitable practices. These biases can be introduced into AI systems in various ways and can lead to unfair or discriminatory outcomes, perpetuating existing social inequalities and reinforcing harmful stereotypes. Oftentimes, bias can fly under the radar, and this is why pairing it with observability is essential.
Bias can appear in many forms, but here are some examples:
- Some ads have been found to show more high-paying jobs to male users than female users, perpetuating gender-based pay disparities.
- One recruiting system was found to amplify existing inequitable hiring practices in technology roles, leading to discrimination against women and other underrepresented groups.
- Language models have been found to exhibit political bias, reflecting the biases of their training data.
Just as there are many forms of Bias, there are many ways to prevent it. At Hyperscience, our solution to this problem also serves as a foundational pillar of our company. That is:
Human In the Loop
Human-in-the-loop (HiTL) interactions are not only important for improving the accuracy of models and increasing the formats they can recognize, but they are also essential for fighting bias. They not only provide you with a better understanding of areas for improvement, but also bring to light snapshots of the data so a person can identify incorrect predictions. Involving human expertise helps identify nuances and contextual information that may elude the model alone. This iterative feedback loop enhances the model’s adaptability, fostering continuous improvement and alignment with real-world complexities.
Hopefully, these tools can empower you to make the best decisions for your customers. Just remember, only you can prevent bias in AI.