Fairness in decision strategies is achieved by ensuring that every action or decision is evaluated against a set of criteria that are applied consistently and are transparent. Consistency prevents discriminatory outcomes by treating similar cases alike, while transparency allows for the auditing and understanding of the decision logic to identify and mitigate potential biases. This approach forms the foundation of responsible and ethical AI, ensuring that automated decisions are equitable and justifiable.

A. Automating all decision-making processes: Automation does not inherently guarantee fairness. If the underlying logic or data is biased, automation will simply execute and scale that bias, potentially leading to widespread unfair outcomes.

C. Limiting decisions to predefined templates: Templates can be too rigid and may not account for individual nuances. If a template is inherently biased or misapplied, it can enforce unfairness rather than prevent it.

D. Reducing the complexity of customer segmentation: The complexity of segmentation is not the root cause of unfairness. Unfairness stems from the criteria used to make decisions, which can be biased even with simple segmentation.

1. Pega Academy, Pega Decisioning Consultant (PEGACPDC24V1) Mission: The "Responsible AI and the T-switch" module emphasizes the principles of Transparency, which involves making decision logic clear and understandable. This directly supports the concept of using transparent criteria to ensure fairness.

2. Pega Documentation, "Ethical bias": The section on "Defining an ethical bias policy" details how to configure transparent thresholds and criteria to consistently monitor and mitigate bias in AI models. This process is a direct implementation of evaluating actions using consistent and transparent criteria to ensure fairness.

3. Pega Documentation, "Creating a decision strategy": The documentation explains that strategies are built on components like Proposition Filters and Set Property, which apply business rules (criteria) consistently to every case that passes through the strategy, ensuring uniform evaluation.