Examination of current developments in generative artificial intelligence (AI) within long history of information, mathematical formalism, and algorithmic culture. Investigation of social history of mathematical, philosophical, and information practices that led to development of current AI systems. Study addresses key areas of critical AI studies including fairness and bias, data politics, digital labor, classification, data colonialism, and algorithmic justice. Exploration of current architectures, philosophical inquiries, and social implications of AI systems, as well as potential algorithmic justice interventions.
Data-based computation (i.e., algorithms, artificial intelligence, predictive modeling) increasingly play a dominant role in shaping everyday experiences of culture and society. Data and data analytics define everything from social relations and public policy to juridical status and market logistics. Study pursues thinking about ethics and justice in a data-driven society but focus on concrete case studies. Students gain critical understanding of technology sector, and also learn of community-engaged models of deploying data skills for social justice.
Data-based computation (i.e., algorithms, artificial intelligence, predictive modeling) increasingly play a dominant role in shaping everyday experiences of culture and society. Data and data analytics define everything from social relations and public policy to juridical status and market logistics. Introduction to politics, ethics, applications, history, critiques, and social impact of data. Introduction to how data intersects with philosophical inquiries about justice, (in)equality, power, and freedom. Students obtain deeper historical and critical view of data in society, while gaining understanding of differing and diverse cultural frames of analysis.