 Название: Data Conscience: Algorithmic Siege on our Humanity
Автор: Brandeis Hill Marshall
Издательство: Wiley
Год: 2023
Страниц: 355
Язык: английский
Формат: pdf (true), epub (true)
Размер: 33.6 MB
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.
I first have to address the four-letter trendy buzzword: code. Computer programming, aka coding, is the planning, designing, implementing, executing, testing, and maintaining of algorithms in a specific computer programming language. Sounds boring, right? Let me share a jazzier explanation of coding—it's taking an idea you think of and bringing it to “life” in the digital realm to make the physical world easier for you to navigate: think online banking or e-commerce capabilities. The linchpin in coding is being good at building algorithms. The best definition of an algorithm comes from G. Michael Schneider and Judith Gersting's Invitation to Computer Science: “An algorithm is a well ordered collection of unambiguous and effectively computable operations that produces a result and halts in a finite amount of time”.
We may run our programs, receive inconsistent results, and think the “bug” is our code. Instead, it just may be part of an unmodifiable open source software module that we've depended on. And those of us in data and tech learn quickly that we can't depend on it as a resource. So you think that there are better alternatives, like slate3k, which extracts all text from PDF documents one page at a time. Now slate3k depends on another Python package, PDFMiner. If you choose this alternative, be sure to install the most recent stable version of PDFMiner and slate for your Python version. But keep in mind it has techno-ethical vulnerabilities too: Getting simple things done, like extracting the text is quite complex. The program is not designed to return Python objects, which makes interfacing with other Python datatypes irritating.
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
Contents:
Introduction
Part I: Transparency
Note
CHAPTER 1: Oppression By…
CHAPTER 2: Morality
CHAPTER 3: Bias
CHAPTER 4: Computational Thinking in Practice
Part II: Accountability
Note
CHAPTER 5: Messy Gathering Grove
CHAPTER 6: Inconsistent Storage Sanctuary
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
APPENDIX A: Code for app.py
APPENDIX B: Code for screen.py
APPENDIX C: Code for search.py
APPENDIX D: Pseudocode for faceit.py
APPENDIX E: The Data Visualisation Catalogue's Visualization Types
APPENDIX F: Glossary
Index
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