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Ethical Reads # 1

July 30, 2022

You may be asking yourself, “How do I become more literate in the field of ML and AI, pertaining to ethics?” Okay, you may not be asking yourself, verbatim, the question; however, you may be curious about knowing more about algorithms, their use and abuse, how they affect you. Books are one way to become knowledgeable about AI, and there’s a list of books for all you bibliophiles [lover of books] to whet your intellectual, literary palate.

Technology Is Not Neutral: A Short Guide to Technology Ethics

By Stephanie Hare

Amazon.com: Technology Is Not Neutral: A Short Guide to Technology Ethics:  9781907994975: Hare, Stephanie: Books

Stephanie Hare is a technologist, political risk analyst, and historian who directly addresses the questions and concerns about the use of technology; she prods the reader to reflect on technological tools that would aid humanity, not destroy it. Additionally, she gives an industry perspective of AI, which may help in understanding the context and audience of this well-written book.

Race After Technology

By Dr. Ruha Benjamin

Amazon.com: Race After Technology: Abolitionist Tools for the New Jim Code  eBook : Benjamin, Ruha: Kindle Store

Dr. Ruha Benjamin, sociologist and historian at Princeton University, addresses, effectively and profoundly, the racial biases in the technology we use routinely; she explains that algorithms have the capacity to deepen the already-tried-and-true colorism and racism that have impacted people of color for decades. The “New Jim Code,” a wordplay of the word Jim Crow, is the term she refers to the catastrophic result of unchecked algorithms. Race After Technology is a call-to-action book for revolutionary minds.

Weapons of Math Destruction

By Dr. Cathy O’Neil

Weapons Of Math Destruction: O'Neil, Cathy: 9780141985411: Amazon.com: Books

Dr. Cathy O’Neil, a mathematician, data scientist, and author, describes how unregulated algorithms can inextricably undermine democracy. She illustrates that erroneous mathematical models are making real-life decisions on our behalf. Weapons of Math Destruction is a must-read book, especially for those interested in saving our democracies from unfettered, totalitarian machines.

Algorithms of Oppression

By Dr. Safiya Umoja Noble

Amazon.com: Algorithms of Oppression: How Search Engines Reinforce Racism  eBook : Noble, Safiya Umoja: Books

Dr. Safiya Umoja Noble, a professor and historian at UCLA, highlights the racial stereotypes that the Google Search Engine propagates. She emphasizes that search algorithms are replete with discriminatory implementation; these algorithms give privilege to whiteness and its results, while people of color are linked to the baser, dark instincts of mankind. Algorithms of Oppression is an eye-opening book that makes you want to investigate your search engine, possibly replace it with one that has less biased algorithms.

Machine See, Machine Do

By Dr. Patrick K. Lin

Machine See, Machine Do: How Technology Mirrors Bias in Our Criminal  Justice System by Patrick K. Lin

Dr. Patrick K. Lin, a lawyer in criminal justice reform, elucidates how machines reinforce racial stereotyping in the court of law. Have you ever wondered why two cases with similar backgrounds and motives have entirely different verdicts. Could it be that race and ethnicity had a role in the ruling? What about the jurors? Did the camera selectively choose the person with the darker pigment as the culprit? What about the data sets being used? Dr. Patrick K. Lin illustrates the aforesaid questions in a way that is understandable to tech and non-technical people alike. Machine See, Machine Do is a book that would have you question many aspects of the criminal justice system.

Devin Almonor

Devin is a one-of-a-kind person. He is passionate about his dreams to save the world, one algorithm at a time. He is currently pursuing a Masters in Machine Learning and A.I. at CSU-Global, with the aspirations of becoming a machine learning engineer. Ethical A.I. is his passion, and he does whatever it takes to ensure the algorithms and technology are representative of their constituents.

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