My personal & group project work for MIDS W231. I focused primarily on housing issues in the United States such as bias in algorithmic lending products.

The Unequal American Dream: Hidden Bias in Mortgage Lending AI/ML Algorithms

Home ownership is a cornerstone of the American Dream, but this is not equally accessible by people of color. Hidden bias exists in digital mortgage lending products based on historical segregation practices as well as the criteria of individuals within the risk evaluation process led by traditional banks and federal guidelines. In order to reduce or eliminate this hidden bias, Data Scientists should consider alternative credit models, incorporating human loan officers within the final evaluation, and additional data points that do not add additional bias into these algorithms. By doing so, potential home owners will continue to have equal access to increase personal wealth equally regardless of their race.

Analysis of Grubhub's Privacy Policy

I reviewed Grubhub's terms of service/privacy policy. Then, analyzed what I read through the lens of the three privacy frameworks I learned about in class: Solove's Taxonomy, Nissenbaum's Contextual Integrity, and Mulligan et al.'s analytic.

Report: Legal/Ethical Analysis

Our Data Science team has analyzed the publicly available arrest log dataset from the Berkeley Police Department for legal or ethical issues and concerns pertaining to privacy. For our analysis, we utilized three separate privacy frameworks (Solove’s Taxonomy, Nissenbaum’s Contextual Integrity, and Mulligan/Koopmans’ Privacy Analytic) to further frame our perspective. Additionally, we performed a reidentification exercise to explore if individuals from the current log chosen at random could be further identified in other public domains as this may further increase privacy risks and issues.

Racial Bias in the Housing Market: Mortgage Lending

Our group applied additional concepts and frameworks as discussed in W231 class to demonstrate a thorough understanding of course materials as well as highlighting the importance of this topic.

Intro to the legal, policy, and ethical implications of data, including privacy, surveillance, security, classification, discrimination, decisional-autonomy, and duties to warn or act. Examines legal, policy, and ethical issues throughout the full data-science life cycle collection, storage, processing, analysis, and use with case studies from criminal justice, national security, health, marketing, politics, education, employment, athletics, and development. Includes legal and policy constraints and considerations for specific domains and data-types, collection methods, and institutions; technical, legal, and market approaches to mitigating and managing concerns; and the strengths and benefits of competing and complementary approaches.