11.5. pt. I Lesson 5: Responsible data work requires social dialogue, community engagement and contributions to data literacy. 1. Data in Society -- 1.1. Introduction: Who cares about data? -- 1.2. Datafication and its components -- 1.3. Data, ethics and knowledge production -- 1.4. Conclusion: The impact of Datafication -- pt. II Data Creation -- 2. Big Data in Context -- 2.1. Introduction: The rise of Big Data -- 2.2. The Big Data mythology: Data transforms society -- 2.3. A historical perspective: Society transforms data -- 2.4. Conclusion: Data do not speak for themselves -- 3. Characteristics of Data -- 3.1. Introduction: Data do not stay still -- 3.2. Data are not neutral -- 3.3. Data are context-dependent -- 3.4. Conclusion: Characteristics of data -- 4. Data, Evidence and Knowledge -- 4.1. Introduction: The representational and the relational views on data -- 4.2. What is evidence? The path from data to knowledge -- 4.3. Examples of data within the knowledge production cycle -- 4.4. Contrasting the representational and relational perspectives -- 4.5. Conclusion: Data science in a relational perspective -- pt. III Data Circulation -- 5. Putting Data to Work -- 5.1. Introduction: The complexity of putting data to work -- 5.2. The challenge of `messy' data -- 5.3. Infrastructures -- 5.4. Conventions and metadata -- 5.5. Models -- 5.6. Visualisations: Forms, tools and interfaces -- 5.7. Curation -- 5.8. Conclusion: Forms of data work -- 6. New Data Skills -- 6.1. Introduction: Data expertise -- 6.2. What is data science? -- 6.3. Data science skills -- 6.4. Bringing skills together -- 6.5. Conclusion: Becoming a data scientist today -- 7. Governance of Data Journeys -- 7.1. Introduction: What is data governance? -- 7.2. Data as private commodities: Closed data -- 7.3. Data as public goods: Open data -- 7.4. A hard case: The journeys of health-related data -- 7.5. Shifting focus to usable data: The FAIR principles -- 7.6. International data journeys and the problem of data inequities -- 7.7. Conclusion: Governance is not a silver bullet -- pt. IV Data Value, Innovation and Responsibility -- 8. Data as a Source of Value -- 8.1. Introduction: What makes data valuable? -- 8.2. Assumptions about the value of data -- 8.3. Data and innovation -- 8.4. The data economy -- 8.5. Who benefits from the value of data? -- 8.6. Allocating value, responsibility and profit -- 8.7. How does AI add value to data? -- 8.8. The value of prediction -- 8.9. The value of metrics -- 8.10. Conclusion: Making data valuable -- 9. Data Justice and Ethics -- 9.1. Introduction: From data value to data ethics -- 9.2. Which data are ethically sensitive? -- 9.3. Data justice: Implementing fairness -- 9.4. Ethics for data work: General frameworks -- 9.5. Ethics in data work: Assessing technical decisions -- 9.6. Responsibilities of data workers -- 9.7. Conclusion: From analysis to action, from rules to power -- 10. Responsible Use of Data as Evidence -- 10.1. Introduction: Data matters -- 10.2. What is evidence-based decision making? -- 10.3. Ensuring responsible use of data -- 10.4. Legal frameworks and formal regulation -- 10.5. Codes of conduct -- 10.6. Computational metrics and design -- 10.7. Organisational and cultural interventions -- 10.8. Institutional Review Boards -- 10.9. Social participation and slow science -- 10.10. Conclusion: Responsibility, monitoring and trust -- pt. V Conclusion: Data and the Knowledge We Need -- 11. Towards Good Data Science -- 11.1. Lesson 1: `Data' is a relational category -- 11.2. Lesson 2: Infrastructures and data stewardship are essential to extract knowledge from Big Data -- 11.3. Lesson 3: Data workers must use data sources with discernment and be aware of the risks of discrimination and inequality connected to data -- 11.4. Lesson 4: Ethics, security and social responsibility are a fundamental part of data work -- 11.5. Lesson 5: Responsible data work requires social dialogue, community engagement and contributions to data literacy.
Summary:
Data and Society: A Critical Introduction investigates the growing importance of data as a technological, social, economic and scientific resource. It explains how data practices have come to underpin all aspects of human life and explores what this means for those directly involved in handling data. The book fosters informed debate over the role of data in contemporary society explains the significance of data as evidence beyond the "Big Data" hype spans the technical, sociological, philosophical and ethical dimensions of data provides guidance on how to use data responsibly includes data stories that provide concrete cases and discussion questions. Grounded in examples spanning genetics, sport and digital innovation, this book fosters insight into the deep interrelations between technical, social and ethical aspects of data work. -- Provided by publisher.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.