This book covers an incredibly unique and timely topic: the intersection of AI, philosophy, religion and the search for meaning. The author, Meghan O'This book covers an incredibly unique and timely topic: the intersection of AI, philosophy, religion and the search for meaning. The author, Meghan O'Gieblyn, is a former theologian who lost faith. In a new search for meaning, she stumbled upon the rise of DATAISM. The book compares the various philosophies and religions that have shaped human thought throughout history with the functioning of modern technology and AI today. Digital technologies are now shaping intellectual and religious thinking. Comparisons are drawn across 7 main themes: Image, Pattern, Network, Paradox, Metonymy, Algorithm and Virality.
Although a fascinating topic, I got bored sometimes by the philosophical reasoning. It's definitely more heavy on the philosophy and religion side, more so than AI, but that's to be expected given the author's background. Arguments are thought-provoking and I'll be referencing this for future research and courses....more
4.5/5 stars for me, I highly recommend it for both practitioners and non-practitioners. Accessible book for a wide audience covering AI Ethics. Both s4.5/5 stars for me, I highly recommend it for both practitioners and non-practitioners. Accessible book for a wide audience covering AI Ethics. Both sides of the coin are observed, from the dream/nightmare superintelligence scenarios to concerns closer rooted in today's actual challenges. I found the introduction chapters incredibly interesting, especially chapter 2 which connects our past to what drives us to think the way we do today about AI. Although I enjoyed it, I would say it raises a lot more questions than answers (understandably so given how complex the topic is). But I was hoping for more suggestions on 'how' to aim for more ethical AI....more
The BEST book I have read on data / AI ethics thus far. Written so clearly and well thought out with each proposed theory or methodology paired with eThe BEST book I have read on data / AI ethics thus far. Written so clearly and well thought out with each proposed theory or methodology paired with examples and use cases. Highly recommended for all data practitioners, and even for non-practitioners it is mostly accessible. Data ethics falls at the intersection of technology, society, ethics and algorithm design. The book dedicates a chapter each to the algorithmic problems and proposed ways to solve for: privacy, fairness, game theory, p-hacking, and finally a combined touch on more difficult to resolve interpretability, morality and the risks of a singularity. Many incredibly relevant notions are covered including but not limited to: differential privacy (centralized vs local), Pareto curves balancing predictive accuracy and fairness, fairness gerrymandering, Maxwell equations, the echo chamber equilibrium, the Bonferroni correction and the reproducibility crisis. On a side note, I love how the authors relate the famous data science phrase 'garbage in, garbage out' to their own 'bias in, bias out.' I know I'll be referencing this book for years to come, great work!...more
This book deep dives into how a world designed with men as the default has caused women to pay tremendous costs for this unconscious bias. It brings tThis book deep dives into how a world designed with men as the default has caused women to pay tremendous costs for this unconscious bias. It brings to light data discrepancies generated by gender across all fields (there are specific parts on healthcare & medicine, workplace safety, city planning, disaster relief and more). Even as a woman living through some of these situations personally, it was eye-opening for me to see how far-reaching the impact is. I wish I could give this a 4.5-star rating.. but since we can only choose whole numbers I have settled on a 4 only because I found it repetitive especially as I got closer to the end. Regardless, I highly recommend this to everyone, female or male, because I guarantee you will learn a lot! and become more aware of data bias all around us....more
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