Data Conscience : algorithmic siege on our humanity / Brandeis Hill Marshall.

Format:

Book

Author/Creator:

Marshall, Brandeis (Brandeis Hill), author.

Language:

English

Subjects (All):

Information technology--Social aspects.

Information technology.

Information technology--Philosophy.

Information society--Philosophy.

Information society.

Physical Description:

1 online resource (355 pages)

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, Incorporated, [2022]

Summary:

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.

Contents:

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

Chapter 3 Bias

Types of Bias

Before You Code

Bias Messaging

Chapter 4 Computational Thinking in Practice

Ready to Code

Algorithmic Justice Practice

Code Cloning

Part II Accountability

Chapter 5 Messy Gathering Grove

Ask the Why Question

Collection

Reformat

Chapter 6 Inconsistent Storage Sanctuary

Ask the "What" Question

Files, Sheets, and the Cloud

Modeling Content Associations

Manipulating with SQL

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

Chapter 8 Double-Edged Visualization Sword

Ask the "When" Question

Critiquing Visual Construction

Pretty Picture Mirage

Part III Governance

Chapter 9 By the Law

Federal and State Legislation

International and Transatlantic Legislation

Regulating the Tech Sector

Chapter 10 By Algorithmic Influencers

Group (Re)Think

Flyaway Fairness

Moderation Modes

Chapter 11 By the Public

Freeing the Underestimated

Learning Data Civics

Condemning the Original Stain

Tech Safety in Numbers

Appendix A Code for app.py

A

B

C

D

Appendix B Code for screen.py

A.

B

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

EULA.

Notes:

Includes index.

Description based on print version record.

ISBN:

1-394-32072-8

1-119-82119-3

OCLC:

1341394218