Discover Decision-Making Frameworks and AI Models

Learning Objectives

After completing this unit, you’ll be able to:

List three decision-making models.
Explain how to apply data-driven decision frameworks to real-world situations.
Describe the three fundamental AI models and their applications.
Recognize bias and ensure fairness in data and AI models.

Now let’s take a look at decision-making models, data-driven frameworks, and AI models–and the critical imperative of addressing bias and fairness in all of them.

Decision-Making Models

There are a diverse array of decision-making models and theories. These frameworks are designed to help organizations and individuals make more informed, objective, and effective decisions based on empirical evidence.

Here are some of the primary models.

Rational decision-making model: This model assumes that individuals make logical and consistent decisions by weighing all possible alternatives and selecting the one with the highest utility. Rationality is the cornerstone of this model, and it seeks to make the optimal choice.

Bounded rationality model: Bounded rationality recognizes that in real-life scenarios, decision-makers often operate with limited information and cognitive constraints. They make decisions that are “good enough” rather than seeking the optimal choice.

Intuitive decision-making: Intuitive decision-making relies on gut feelings and personal experiences. It is often used when quick decisions are required or when information is incomplete.

Data-Driven Decision Frameworks

Data-driven decision frameworks provide a structured approach to decision-making, using data as a primary driver. Here are two major decision frameworks in the industry.

Tool	What Is it?
Decision trees	These graphical representations help you visualize and evaluate decision choices, outcomes, and probabilities.
Pugh matrix	This decision matrix helps you compare multiple alternatives by quantifying criteria and evaluating their impact.

Decision Tree

A decision tree gives you a systematic approach to making choices or decisions based on a set of criteria. It provides a structured way to evaluate different options and their potential outcomes, helping you select the most suitable course of action. Decision trees consist of nodes and branches, where nodes represent decisions or choices, and branches represent possible outcomes or consequences.

For example, a decision tree can help you determine whether to play a sports game (yes or no) based on weather conditions. It can show you that if it’s sunny, first check humidity, and that if humidity is high, no, do not play, but if it’s low, yes, play. Or, if it’s cloudy, yes, play, and so on through different conditions.

This framework is commonly used in business, finance, healthcare, and machine learning to model decision-making processes and optimize choices based on data and logic.

Example of decision tree to determine whether to play a sports game.

Pugh Matrix

The Pugh matrix, also known as the Pugh method or decision matrix, is a decision-making tool you use to evaluate and compare multiple alternatives or options based on a set of criteria. It gives you a systematic approach to make informed decisions by quantifying and ranking different options. The method was developed by Stuart Pugh, a British engineer and design theorist.

The Pugh matrix helps you make rational decisions by considering multiple factors and their relative importance in a structured manner. The example Pugh matrix below shows criteria that start with a baseline of 0 with alternatives compared to the baseline as positive (+), negative (-), or equal (0). These comparisons are totaled to rank and optimize solutions. This method is a valuable tool in fields such as engineering, product design, and business management.

Pugh Matrix example

These data-driven decision frameworks give you the ability to make objective and evidence-based choices.

In logistics and supply chain management, data-driven decision frameworks can be applied to optimize the movement of goods. Companies can use data on inventory levels, demand forecasts, transportation costs, and lead times to make decisions about the most cost-effective shipping routes, storage locations, and reorder points. This minimizes costs, reduces stockouts, and ensures that products reach customers efficiently.

Introduction to AI Models

Now let’s look at AI models. As you’ve learned, artificial intelligence and machine learning (AI/ML) are powerful tools that can support your decision-making processes.

Types of AI Models

There are three primary types of AI models: supervised learning, unsupervised learning, and reinforcement learning.

Supervised machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurate over time. For example, an algorithm is trained with pictures of dogs and other things, all labeled by humans, and the machine learns ways to identify pictures of dogs on its own. Supervised machine learning is the most common type used today.

In unsupervised machine learning, a program looks for patterns in unlabeled data. Unsupervised machine learning can find patterns or trends that people aren’t explicitly looking for. For example, an unsupervised machine-learning program can look through online sales data and identify different types of clients making purchases.

Reinforcement machine learning trains machines through trial and error to take the best action by establishing a reward system. Reinforcement learning can train models to play games, or train autonomous vehicles to drive, by telling the machine when it made the right decisions, which helps it learn over time what actions it should take.

Generative AI models use neural networks to identify the patterns and structures within existing data to generate new and original content. One of the breakthroughs with generative AI is its ability to leverage different learning approaches, including unsupervised or semi-supervised learning for training. Generative AI uses all three techniques together.

Click the use case tabs to see how various industries use these AI models in decision-making processes. (We take a closer look at these use cases in the next unit.)

Bias and Fairness in AI

The integration of artificial intelligence into decision-making processes across various domains has brought about unprecedented advancements in efficiency and accuracy. But as AI increasingly becomes an integral part of our lives, we need to address the issue of bias and fairness in AI systems. The importance of ensuring fairness and upholding ethical guidelines cannot be overstated. Bias, discrimination, and ethical dilemmas can arise when AI systems are not developed and used responsibly.

Recognizing Bias and Ensuring Fairness in Data and AI Models

Bias refers to the presence of systematic and unfair distinctions or preferences that can lead to discriminatory outcomes. Recognizing bias is the first step in addressing and mitigating its impact–once bias is identified, developers, data scientists, and business users can take corrective measures. Click each tab to learn actions organizations can take to recognize bias and promote fairness in data and AI models.

In this unit, you explored various decision-making models, data-driven frameworks, and AI models, and the ethical imperative of mitigating bias and promoting fairness. In the next unit, take a look at real-world applications of AI in diverse scenarios.