TA-fall-2023

Your Guide to Natural Language Processing (NLP) _ by Diego Lopez Yse _ Towards Data Science.pdf

Automating Inequality: How high-tech tools profile, police, and punish the poor. Chapter 3 (AE-Chapter3.pdf ) Invisible Women: Data Bias in a world designed for men. Chapter 1: Can Snow-Clearing be Sexist? (ebsco link)

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, by Cathy O’Neil. Chapter 3 (WOMD-Chapter3.pdf ) Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights (https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/2016_0504_data_discrimination.pdf)

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, by Cathy O’Neil. Chapter 5. (please see link to PDF in Pweb under “readings” tab.) Algorithms, Correcting Biases, by Cass R. Sunstein (https://muse.jhu.edu/article/732187) Predictive Policing is still racist (https://www.technologyreview.com/2021/02/05/1017560/predictive-policing-racist-algorithmic-bias-data-crime-predpol/)

Machine Bias (https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing) How we analyzed the COMPAS Recidivism Algorithm (https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm) Can you make AI fairer than a judge? (https://www.technologyreview.com/2019/10/17/75285/ai-fairer-than-judge-criminal-risk-assessment-algorithm/) Optional: Watch this video on fairness (https://www.youtube.com/watch?v=jIXIuYdnyyk&t=0s&ab_channel=ArvindNarayanan)

Optimize What? (https://communemag.com/optimize-what/) You are not expected to understand this, edited by Torie Bosch. Chapter 9 (https://www.degruyter.com/document/doi/10.1515/9780691230818/html#contents) Your Computer is On Fire, edited by Thomas S. Mullaney, Benjamin Peters, Mar Hicks, and Kavita Philip. Introductions (one is by Mullaney, one is by Hicks) Optional: Code is Law (https://www.harvardmagazine.com/2000/01/code-is-law-html)

ACM Code of Ethics and Professional Conduct (https://www.acm.org/binaries/content/assets/about/acm-code-of-ethics-and-professional-conduct.pdf) Google Responsibilities and Principles (https://ai.google/responsibility/principles/) Ethical OS Risk Mitigation Checklist (https://ethicalos.org/wp-content/uploads/2018/08/EthicalOS_Check-List_080618.pdf)

Algorithmic Injustice: a relational ethics approach, by Abeba Birhane (https://www.sciencedirect.com/science/article/pii/S2666389921000155?via%3Dihub) (you can either read it online or download the PDF) Feynman’s Error: On Ethical Thinking and Drifting (https://www.danmunro.ca/blog/2018/11/29/feynmans-error-on-ethical-thinking-and-drifting-nbsp)

Intersectionality Resource Guide, pages 8 – 14 (https://www.unwomen.org/sites/default/files/2022-01/Intersectionality-resource-guide-and-toolkit-en.pdf) Watch “The urgency of intersectionality” Ted Talk (https://www.ted.com/talks/kimberle_crenshaw_the_urgency_of_intersectionality#t-173089) How to do Intersectionality (https://narrativeinitiative.org/blog/how-to-do-intersectionality/)

The Priviledged Poor: How Elite Colleges are failing disadvantaged students, by Anthony Abraham Jack. Chapter 1: Come with me to Italy! pages 25 – 78v Listen to Code Switch Podcast titled A Glimpse at How the Other Half Eats