Algorithmic Harm

Algorithmic harm encompasses the negative societal consequences stemming from the design, deployment, and use of algorithms, focusing on issues like bias, unfairness, and lack of transparency. Current research investigates various aspects, including the development of tools to understand and mitigate biases in recommender systems and other AI applications, the ethical implications of algorithmic decision-making, and the measurement and mitigation of representational harms, particularly within large language models. This research is crucial for developing responsible AI practices, improving algorithmic fairness, and ensuring equitable access to technology and its benefits.