Ngozi Okidegbe

Donweber/Gonzales Rose/Lowy: Practical and theoretical aspects of the rules of evidence. The purpose and policies underlying the evidentiary rules are stressed throughout in order to make the rules meaningful, predictable, and functional both for students interested in trial practice and for students who anticipate engaging in a more diversified practice. In addition to covering the substantive rules of evidence, the course demonstrates the significance of evidence as a tactical device at the trial and as a vital skill for the office lawyer. UPPER-CLASS WRITING REQUIREMENT: Professor Gonzales Rose’s section may be used to satisfy the requirement partially or in full. Pedro: In this course, we will examine evidence rules and principles with a focus on the U.S. Federal Rules of Evidence, relevant constitutional provisions, and cases interpreting both. In addition to covering the rules of evidence, the course explores the significance of evidence as a strategic device in civil and criminal litigation, especially at trial. Some topics covered include: hearsay and its exceptions; relevance and prejudice; examination and impeachment of witnesses; privileges; expert testimony; and scientific, character, and propensity evidence. We will frequently use anonymous polling and discussion with a problems-based approach. We will engage in thoughtful discussions about cases alleging serious criminal offenses and civil injuries that may be disturbing. The course includes mandatory, practice quizzes and a practice midterm (specific student performance on the practices do not count toward the final grade) and assessment for the course will be based upon a final examination

This series consists of two currently offered lecture series: Social Justice for Data Sciences Lecture Series and Cyber Alliance Speaker series. The Social Justice for Data Science Lecture Series, hosted by the Faculty of Computing and Data Sciences and the Law School, brings together leading scholars in law, computer science, humanities, and social science to examine the current state of data science and social justice. The goal of the series is to engage with the relationship between justice (as a historically contingent and value-laden category) and data science (with a focus on datafication, automation, predictive analytics, and algorithmic decision-making). The series holds four sessions that are each an hour and half long in the fall term. The Cyber Alliance Speaker Series hosted by the Faculty of Computing and Data Sciences, Law School, and Questrom School of Business, bring together leading scholars working in the areas of law, technology, business, and policy. The series holds five sessions that are each an hour and half long throughout the Fall and Spring term. The Colloquium will expose students to cross-disciplinary talks and presentations and enable students to think critically about law, computation, and data sciences. To prepare for each session, students will read the speaker’s paper or a prior paper and will write short reaction responses that include three questions. Lecture dates to be announced. GRADING NOTICE: This year-long course is graded Pass/Fail at the end of spring term.

Society is becoming increasingly digitized and datafied. Important decisions impacting criminal justice, housing, finance, labor, healthcare, and education are frequently determined by or are aided by artificially intelligent algorithmic technologies that are built and trained on large datasets. The rise in these technologies presents a challenge for social justice. Though often presented as neutral decision aids, these technologies often produce harmful predictions that operate to reinforce old legacies of racial, class, gender, and heteropatriarchal subordination. Datafication practices, computational techniques, legal doctrine, and policy play a key role in facilitating these disparate outcomes. This course will center on the complicated relationship between social justice and data science. The course will introduce students to the historical and current role of datafication and computation practices in social subordination. Students will leave the course having developed the skill set needed to identify and critically engage with the social justice challenges posed by these new technologies.