Data Conscience : Algorithmic Siege on Our Humanity

DATA CONSCIENCE ALGORITHMIC S1EGE ON OUR HUM4N1TY EXPLORE HOW D4TA STRUCTURES C4N HELP OR H1NDER SOC1AL EQU1TY Data has enjoyed ‘bystander’ status as we’ve attempted to digitize responsibility and morality in tech. In fact, data’s importance should earn it a spot at the center of our thinking and strategy around building a better, more ethical world. It’s use—and misuse—lies at the heart of many of the racist, gendered, classist, and otherwise oppressive practices of modern tech. In Data Conscience: Algorithmic Siege on our Humanity , computer science and data inclusivity thought leader Dr. Brandeis Hill Marshall delivers a call to action for rebel tech leaders, who acknowledge and are prepared to address the current limitations of software development. In the book, Dr. Brandeis Hill Marshall discusses how the philosophy of “move fast and break things” is, itself, broken, and requires change. You’ll learn about the ways that discrimination rears its ugly head in the digital data space and how to address them with several known algorithms, including social network analysis, and linear regression A can’t-miss resource for junior-level to senior-level software developers who have gotten their hands dirty with at least a handful of significant software development projects, Data Conscience also provides readers with: Discussions of the importance of transparency Explorations of computational thinking in practice Strategies for encouraging accountability in tech Ways to avoid double-edged data visualization Schemes for governing data structures with law and algorithms Cover Title Page Copyright Page About the Author About the Technical Editor Acknowledgments Contents Foreword Introduction Part I Transparency Chapter 1 Oppression By. . . The Law The Science Summary Notes Recommended Reading Chapter 2 Morality Data Is All Around Us Morality and Technology Misconceptions of Data Ethics Limits of Tech and Data Ethics Summary Notes Chapter 3 Bias Types of Bias Before You Code Bias Messaging Summary Notes Chapter 4 Computational Thinking in Practice Ready to Code Algorithmic Justice Practice Code Cloning Summary Notes Part II Accountability Chapter 5 Messy Gathering Grove Ask the Why Question Collection Reformat Summary Notes Chapter 6 Inconsistent Storage Sanctuary Ask the “What” Question Files, Sheets, and the Cloud Modeling Content Associations Manipulating with SQL Summary Notes Chapter 7 Circus of Misguided Analysis Ask the “How” Question Misevaluating the “Cleaned” Dataset Overautomating k, K, and Thresholds Not Estimating Algorithmic Risk at Scale Summary Notes Chapter 8 Double-Edged Visualization Sword Ask the “When” Question Critiquing Visual Construction Pretty Picture Mirage Summary Notes Part III Governance Chapter 9 By the Law Federal and State Legislation International and Transatlantic Legislation Regulating the Tech Sector Summary Notes Chapter 10 By Algorithmic Influencers Group (Re)Think Flyaway Fairness Moderation Modes Summary Notes Chapter 11 By the Public Freeing the Underestimated Learning Data Civics Condemning the Original Stain Tech Safety in Numbers Summary Notes Appendix A Code for app.py A B C D Appendix B Code for screen.py A B C Appendix C Code for search.py A B C D Appendix D Pseudocode for faceit.py Appendix E The Data Visualisation Catalogue’s Visualization Types Appendix F Glossary Index EULA Social Justice Meets Data Science In This Startlingly Insightful Take On How To Improve The World Of Tech In Data Conscience: Algorithmic Siege On Our Humanity, Data Inclusivity Strategist And Expert Dr. Brandeis Marshall Delivers An Incisive And Eye-opening Discussion Of How To Fix Tech’s Dominant Philosophy Of “move Fast And Break Things” With A Renewed Focus On Equity And Oppression. In The Book, The Author Explores How To Address Discrimination In The Digital Data Space With Several Known Algorithms, Including Social Network Analysis, Linear Regression And Sentiment Analysis. Rebel Tech Leaders Who Want To Build A Better, More Ethical World, Will Also Find: Expansive Discussions Of The Importance Of Transparency And How To Put Computational Thinking Into Practice Strategies For Encouraging Accountability In Tech And Making Misleading Data Visualization A Thing Of The Past Governance Strategies—based On Law, Algorithms And Forever Learning—that Can Improve The Way We Interact With Data Perfect For Junior Software Developers Who Have Begun To Dip Their Toes Into The “real World,” Data Conscience Will Earn A Place In The Libraries Of Anyone Interested In The Intersection Of Social Justice And Data Science.