Machine Learning Fairness focuses on ensuring that algorithmic decisions do not result in biased or discriminatory outcomes against any group, thereby promoting equitable treatment across diverse populations. It involves the identification, measurement, and mitigation of biases in data and models to uphold ethical standards and societal norms in automated decision-making systems.

Machine Learning Fairness

Algorithmic decisions

Biased outcomes

Discriminatory outcomes happen when some people are treated unfairly or differently just because of who they are, like their skin color or if they're a boy or a girl. It's like if a teacher gave more candy to kids with blue shirts and less to kids with red shirts, even though both groups did the same work.

Discriminatory Outcomes

Equitable treatment refers to the fair and impartial handling of individuals, taking into account their unique circumstances and needs to ensure equal opportunities and outcomes. It focuses on addressing systemic inequalities and removing barriers that prevent certain groups from achieving the same success as others.

Equitable Treatment

Diverse Populations

Identification of biases

Measurement of biases

Mitigation of biases

Data Biases

Model biases

Ethical standards are guidelines that dictate the conduct of individuals and organizations to ensure actions align with moral principles and societal norms. They serve as a framework for decision-making, promoting integrity, accountability, and trust within professional and personal contexts.

Ethical Standards

Societal norms are the shared expectations of behavior that are deemed appropriate within a collective group, influencing everything from daily interactions to overarching cultural practices. These norms guide individuals in understanding what is considered acceptable and unacceptable, fostering social cohesion but also potentially perpetuating inequality and resistance to change.

Concepts

Relevant Degrees

Your Lessons