“Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, we’ll augment our intelligence.” – Ginni Rometty.
With businesses increasingly depending on AI models to make decisions and streamline their business operations, it is essential to know how these AI machines interpret and provide conclusions. Are the results trustworthy?
This highlights the importance of explainability. In this blog, we talk about explainable AI and why businesses must explain it to others.
What is Explainable AI (XAI)?
Explainable artificial intelligence (XAI) refers to the tools and frameworks that enable humans to understand and interpret the outputs and predictions generated by machine learning models. It helps understand how LLMs make decisions and use this information to enhance the model accuracy and generate more accurate, fair, and transparent outcomes.
Unlike traditional AI models, which work as “black boxes” with complex algorithms that provide little insight into how decisions are made, XAI aims to make the reasoning behind AI outputs clear and interpretable.
Explainability is one thing; interpreting it rightly (for the good of society) is another.” – Murat Durmus, The AI Thought Book.
How does Explainable AI work?
Here’s an overview of how XAI works:
- Model selection: Use simple models (decision trees) or complex models (neural networks) to extract explanations.
- Feature importance: Assess which input features impact predictions the most and visualize their importance.
- Local explanations: Create a simple model (LIME) around a specific prediction to explain it and assign importance values to features based on their contribution to a prediction.
- Global explanations: Analyze the model’s behavior to show how features influence predictions across all data.
- Visual tools: Use dashboards, heatmaps, and graphs to help users see which features impacted decisions.
- Natural language explanations: Offer easy-to-understand text summaries of how and why decisions were made.
- Feedback: Allow users to give feedback on explanations to improve clarity and model performance.
What are the 4 principles of explainable AI?
The four principles of explainable AI are:
Explanation: The model offers or includes supporting evidence or reasoning for its outputs and processes.
Meaningful: The system provides explanations that are comprehensible to the intended users.
Explanation accuracy: The explanation accurately represents the reasoning behind the output and reflects the system’s process.
Knowledge limits: The system functions only within the parameters for which it was designed and works when it reaches when it reaches an adequate level of confidence in its output.
“To inspire trust, the AI models that encapsulate dynamic intelligence should have a carefully configured ‘best before’ date.” ― Mukesh Borar, The Secrets of AI.
Benefits of Explainable AI
Build trust in AI: Explainable AI methods help to create reliable AI models and deploy them while ensuring they can be easily understood. With simple evaluation processes, businesses can enhance the transparency and traceability of their explainable AI models.
Reduce risk and cost involved in model governance: By having explainable AI tools and models you can ensure they are clear and understandable. Businesses must comply with regulations to manage risks and threats, preventing the need for manual checks and avoiding errors.
Speed up results: Regular monitoring of models improves business outputs. Therefore, businesses must continuously assess and enhance model performance and refine development based on ongoing assessment.
Grab new business opportunities: Understanding how a model works can help companies identify actions they might be overlooking. For example, knowing why a specific product is underperforming helps to understand the factors affecting sales. These insights can help marketing teams improve their marketing strategies or product features.
Use cases of Explainable AI
- Healthcare diagnostics and treatment recommendations: Explainable AI allows healthcare professionals to understand why an AI system recommends a specific diagnosis or treatment plan. For example, it can help them understand why the AI model predicted a higher probability of heart disease depending on health factors such as age, cholesterol levels, or lifestyle.
- Financial services, loan approvals, and credit score: Explainable AI helps applicants understand why they were denied a loan or why they received a specific credit score. XAI can show different factors, such as credit history or income, that have a huge impact on decision-making, ensuring transparency and fairness in the financial decision-making process.
Conclusion
Explainable AI helps AI development companies explain how the AI model made a decision, i.e., how it went from input to output. By implementing explainability as a core principle, organizations can set standards and guidelines for their development teams, promoting transparency, accountability, and ethical AI usage. This will not only enhance trust in AI systems but also ensure they operate responsibly and fairly.
