EQUATE is an acronym for Equity, Accountability, Trust, and Explainability. These areas are commonly referred to as FAT (fairness, accountability, and transparency). EQUATE encompasses those elements and more.
What is EQUATE?
Welcome to EQUATE
EQUATE is an initiative of CICS faculty who are engaged in research and education related to equitable algorithms and systems. Our educational efforts include coursework in ethics and algorithm design that respects the values of fairness and transparency. Our research efforts explore EQUATE topics within software systems and programming languages, machine learning, and vision, theory, and data management systems.
EQUATE is affiliated with the Center for Data Science.
Featured Research
In this project we study how the user of a machine learning (ML) algorithm (method) can place constraints on the algorithm’s behavior. We contend that standard ML algorithms are not user-friendly, in that they can require ML and data science expertise to apply responsibly to real-world applications. We present a new type of ML algorithm that […]
The big data revolution and advancements in machine learning technologies have revolutionized decision making, advertising, medicine, and even election campaigns. Yet, data is an imperfect medium, often tainted by skews and biases. Learning systems and analysis software learn and amplify these biases. As a result, discrimination shows up in many data-driven applications, such as advertisements, hotel bookings, image search, and […]