Research, teaching, and practice in science, technology, engineering, mathematics and medicine (STEMM) often rest on the unquestioned assertion of the impartial analyses of facts. This course will take a data-informed approach to understanding how human biases can, and have, affected science and its applications in a range of fields, with a particular focus on biology. Case studies may include reviews of how science has been used to justify or sustain racism, colonialism, enslavement, and the exploitation of marginalized groups. Links will be drawn to contemporary societal challenges, practices, and technologies. Topics will include how biases can shape science in terms of the questions under study, scientific inferences, and the types of knowledge and assumptions that inform applications, shape teaching, and influence popular understanding. Data on bias and societal costs of bias will be reviewed, as well as evidence-informed practices, structures, and individual actions which could ensure that STEMM disrupts, rather than enables, social inequities.