Algorithmic Bias and Fairness

How training data and models reinforce inequalities.

Impacts on racialized groups, gender minorities, and privacy.

Including diverse voices in developing and testing systems.

Emerging laws, guidelines, and accountability frameworks.

Hiring, lending, policing, and medical algorithms.

Innovations in ethics, regulation, and community oversight.

Real-world examples of discrimination through tech.

Strategies for balanced, representative, and ethical datasets.

Demanding clarity on how algorithms make decisions.

Understanding how bias enters systems through data, design, or assumptions.