How Explainability Transforms Insights and Decision-Making in the AI World

In today’s AI (Artificial Intelligence) landscape, IT, data, finance and business leaders rely on data more than ever to make strategic decisions and drive business performance. While deriving insights from LLM (Large Language Model) based tools like ChatGPT (by OpenAI), Gemini (by Google), and Copilot (by GitHub) have become much easier, effective communication of the insights derived to propel action remains a challenge for most leaders. Communicating insights goes beyond simply presenting data or KPIs or insights. It encompasses collecting the right data, selecting appropriate models, deriving insights, targeted messaging, creating trust and cultural awareness, engaging with the right stakeholders, creating impactful visuals, and more. The process of deriving and communicating insights in today’s AI centric world is as shown below.

How Explainability Transforms Insights and Decision-Making in the AI World

Figure 1: Communicating Insights in AI World

In this backdrop, business leaders should be equiped them with strategies, techniques, and tools to explain or communicate complex data, models, and insights to the stakeholders for enhanced business performance such as improved revenue, reduced costs, and mitigated risks. Explainability in AI or Explainable Artificial Intelligence (XAI) refers to the ability to understand how an AI model derives insights and makes decisions. XAI allows stakeholders to understand why that recommendation or decision was made, which is crucial for ensuring fairness, reducing bias, and increasing trust in AI systems. XAI includes 4 key elements:

  1. Transparency: Explaining how the model works, what data it uses, and how it produces its results.
  2. Interpretability: Making the model’s decisions understandable to humans.
  3. Trust: Ensuring that users can trust the AI systems to make fair and accurate decisions by providing insight into how those decisions are reached.
  4. Accountability: Ensuring that AI systems can be held accountable for their decisions, especially in high-stakes scenarios.

So, how can XAI be achieved? XAI can be achieved through a combination of (A) Interpretable models (B) Visualization tools, and (C) Model-agnostic explanation techniques like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations).

1. Interpretable models are models that are easy to understand and explain, both for humans and AI systems. These models provide clear insights into how inputs (features) are related to outputs (predictions) in a way that humans can follow and trust, apart from being audited. Examples of interpretable models include:

  • Linear Regression: A statistical method that models the relationship between a dependent variable and one or more independent or predictor variables using a linear equation.
  • Decision Trees: A decision support tool that uses a tree-like graph of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
  • Logistic Regression: A statistical model that estimates the probability of a binary outcome based on one or more predictors or independent variables.

2. They say a picture is worth a thousand words. Visualization tools are software or techniques that present data, model, outcomes, or decision-making processes in a graphical or visual format. Visualization tools are particularly useful for explaining how models make decisions, highlighting key features, and showing relationships between input parameters and outputs. Examples of data visualization include:

  • Bar Chart: A graphical representation of data using bars of different heights.
  • Scatter Plot: A graph in which the values of two variables are plotted along two axes, revealing any patterns or correlations.
  • Pie Chart: A circular chart divided into sectors, illustrating numerical proportions.

3. Model-agnostic explanation techniques like LIME and SHAP can explain AI models regardless of the algorithm used. These techniques can be applied to any model such as regression, decision trees, SVM (Support Vector Machines), etc. LIME shows which features contributed most to the output, making the model’s output more transparent, while SHAP assigns each feature a “Shapley value” representing its contribution to the final output. Shapley values explain the contribution of each feature to a model’s prediction or output. Basically, both LIME and SHAP do the same, but the approaches are different. LIME is based on Local approximation, while SHAP is based on Game theory. For example, both LIME and SHAP explain how much factors like age or customer service contributed to customer churn.

As AI grows more sophisticated, its decision-making processes often resemble a “black box,” making it difficult for human beings to understand how the algorithm derived the insights to make decisions. XAI is a set of processes and techniques that demystifies this process, enabling humans to comprehend, trust, validate and communicate AI-driven results. By promoting model accuracy, fairness, transparency, and accountability, XAI builds confidence in deploying AI models ensuring its decisions are not only insightful but also trusted and impactful.


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