Human-centered data science : an introduction - Aragon, Cecilia Rodriguez; Guha, Shion; Muller, Michael (Data scientist); Neff, Gina, 1971- (Cambridge, Massachusetts : The MIT Press, 2022)
Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists' choices are involved at every stage of the data science workflow - and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns.
1. Data Science to Human-Centered Data Science -- 2. The Data Science Cycle -- 3. Interrogating Data Science -- 4. Techniques and Tools for Data Science Models -- 5. Human-Centered Approaches to Data Science Problems -- 6. Human-Centered Data Science Methods -- 7. Collaborations across and beyond Data Science -- 8. Storytelling with Data -- 9. The Future of Human-Centered Data Science.
Kuvailun perustana verkkoaineisto; nimeke PDF-nimiösivulta (Ebook Central, katsottu 10.3.2022).
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Keywords: big data; tietojenkäsittely; moraali; etiikka; yhteiskuntatieteet; tutkimusetiikka; tietojenkäsittelytieteet; Big data -- Moral and ethical aspects; Social indicators -- Data processing; Quantitative research -- Moral and ethical aspects; Information science; e-kirjat