Who's in the Machine?
The project hopes to provoke critical examination of the sources of data and underlying biases behind algorithms and machine learning predictions as they become more prevalent in every day life. In disproportionately unbalanced populations, such as among U.S. politicians, Silicon Valley engineers, or incarcerated populations, underlying data sets could be entirely skewed by natural biases while lay users might assume an inherent equality. These "weapons of math destruction" have already been shaking up everything from hiring to criminal justice reform, but the conversations around design and data ethics is only in nascent stages. As designers increasingly called to make active decisions in our products and applications, what role will we have in defining the ethics of data use?