A Model Interpretability Framework is a set of tools and methods that help people understand how machine learning models make decisions. It provides ways to explain which features or data points most affect the model’s predictions, making complex models easier to understand. This helps users build trust in the model, check for errors, and ensure…
Category: Explainability & Interpretability
Root Cause Analysis
Root Cause Analysis is a problem-solving method used to identify the main reason why an issue or problem has occurred. Instead of just addressing the symptoms, this approach digs deeper to find the underlying cause, so that effective and lasting solutions can be put in place. It is commonly used in business, engineering, healthcare, and…
Data Visualization
Data visualisation is the process of turning numbers or information into pictures like charts, graphs, or maps. This makes it easier for people to see patterns, trends, and differences in the data. By using visuals, even complex information can be quickly understood and shared with others.
Feature Importance Analysis
Feature importance analysis is a technique used in data science and machine learning to determine which input variables, or features, have the most influence on the predictions of a model. By identifying the most significant features, analysts can better understand how a model makes decisions and potentially improve its performance. This process also helps to…
AI Model Interpretability
AI model interpretability is the ability to understand how and why an artificial intelligence model makes its decisions. It involves making the workings of complex models, like deep neural networks, more transparent and easier for humans to follow. This helps users trust and verify the results produced by AI systems.
Cognitive Bias Mitigation
Cognitive bias mitigation refers to strategies and techniques used to reduce the impact of automatic thinking errors that can influence decisions and judgements. These biases are mental shortcuts that can lead people to make choices that are not always logical or optimal. By recognising and addressing these biases, individuals and groups can make more accurate…
AI Model Calibration
AI model calibration is the process of adjusting a model so that its confidence scores match the actual likelihood of its predictions being correct. When a model is well-calibrated, if it predicts something with 80 percent confidence, it should be right about 80 percent of the time. Calibration helps make AI systems more trustworthy and…
AI Explainability Frameworks
AI explainability frameworks are tools and methods designed to help people understand how artificial intelligence systems make decisions. These frameworks break down complex AI models so that their reasoning and outcomes can be examined and trusted. They are important for building confidence in AI, especially when the decisions affect people or require regulatory compliance.
Dynamic Inference Paths
Dynamic inference paths refer to the ability of a system, often an artificial intelligence or machine learning model, to choose different routes or strategies for making decisions based on the specific input it receives. Instead of always following a fixed set of steps, the system adapts its reasoning process in real time to best address…
Uncertainty Quantification
Uncertainty quantification is the process of identifying and measuring the unknowns in a system or model. It helps people understand how confident they can be in predictions or results by showing the possible range of outcomes and where things might go wrong. This is important in fields like engineering, science, and finance, where decisions are…