This research highlights the crucial role of discrimination detection in effectively implementing anti-discrimination laws. Our main goal is to advocate for the use of Bayesian reasoning as a powerful method for detection, providing a clear mathematical definition for lawyers. We emphasize the benefits of employing Bayesian reasoning in a legal context, such as addressing the Prosecutor’s Fallacy, considering evidence dependencies, and accounting for hidden assumptions like “common sense.” To apply Bayesian principles, we carefully examine variables aligned with anti-discrimination laws like the Civil Rights Act of 1964 and the Americans with Disabilities Act. These laws encompass important areas such as race, gender, religion, and disability. Following the guidance of the Community Relations Service’s resource guide, we collect variables related to protected characteristics, plausible discrimination scenarios, and indicators of reasonable accommodations. Our approach aims to present a well-rounded framework for discrimination detection rooted in Bayesian reasoning, meeting legal requirements and acknowledging the complex nature of discrimination in society.This research highlights the crucial role of discrimination detection in effectively implementing anti-discrimination laws. Our main goal is to advocate for the use of Bayesian reasoning as a powerful method for detection, providing a clear mathematical definition for lawyers. We emphasize the benefits of employing Bayesian reasoning in a legal context, such as addressing the Prosecutor’s Fallacy, considering evidence dependencies, and accounting for hidden assumptions like “common sense.” To apply Bayesian principles, we carefully examine variables aligned with anti-discrimination laws like the Civil Rights Act of 1964 and the Americans with Disabilities Act. These laws encompass important areas such as race, gender, religion, and disability. Following the guidance of the Community Relations Service’s resource guide, we collect variables related to protected characteristics, plausible discrimination scenarios, and indicators of reasonable accommodations. Our approach aims to present a well-rounded framework for discrimination detection rooted in Bayesian reasoning, meeting legal requirements and acknowledging the complex nature of discrimination in society.