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Abstract

This chapter argues that establishing a “culture of academic integrity,” in the era of digitally-situated plagiarism like contract cheating, begins with an institutional approach to student data and student work that is rooted in ethics. If “students cheat when they feel cheated” (Christensen Hughes, 2017, p. 57), then the ethical failures inherent in a system-wide move toward for-profit homework systems and plagiarism checkers sets a dangerous model for students to follow. We are responsible for modelling for our students what it looks like to be a contributing member of an academic community, and we do so by taking seriously our students, their data, and their work, and not only when it comes time to run it through a plagiarism detector or check their IDs against a proctoring software. This chapter argues that a more responsible relationship to student data, and a less cozy relationship with for-profit educational technologies, is required if our institutions are serious about fostering a culture of academic integrity.

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Dissecting racial bias in an algorithm used to manage the health of populations

Ziad Obermeyer, Brian Powers, Christine Vogeli … (2019)

Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.