Human-Centered Data Science
Human-Centered Data Science

Author: Cecilia Aragon

Publisher: MIT Press

Published: 2022-03-01

Total Pages: 201

ISBN-13: 0262367599

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Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. 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.

Human-Centered Data Science
Human-Centered Data Science

Author: Cecilia Aragon

Publisher: MIT Press

Published: 2022-03-01

Total Pages: 201

ISBN-13: 0262543214

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Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. 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.

Data-Driven Personas
Data-Driven Personas

Author: Bernard J. Jansen

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 317

ISBN-13: 3031022319

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Data-driven personas are a significant advancement in the fields of human-centered informatics and human-computer interaction. Data-driven personas enhance user understanding by combining the empathy inherent with personas with the rationality inherent in analytics using computational methods. Via the employment of these computational methods, the data-driven persona method permits the use of large-scale user data, which is a novel advancement in persona creation. A common approach for increasing stakeholder engagement about audiences, customers, or users, persona creation remained relatively unchanged for several decades. However, the availability of digital user data, data science algorithms, and easy access to analytics platforms provide avenues and opportunities to enhance personas from often sketchy representations of user segments to precise, actionable, interactive decision-making tools—data-driven personas! Using the data-driven approach, the persona profile can serve as an interface to a fully functional analytics system that can present user representation at various levels of information granularity for more task-aligned user insights. We trace the techniques that have enabled the development of data-driven personas and then conceptually frame how one can leverage data-driven personas as tools for both empathizing with and understanding of users. Presenting a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes, we illustrate applying this framework via practical use cases in areas of system design, digital marketing, and content creation to demonstrate the application of data-driven personas in practical applied situations. We then present an overview of a fully functional data-driven persona system as an example of multi-level information aggregation needed for decision making about users. We demonstrate that data-driven personas systems can provide critical, empathetic, and user understanding functionalities for anyone needing such insights.

Human-Centered AI
Human-Centered AI

Author: Ben Shneiderman

Publisher: Oxford University Press

Published: 2022

Total Pages: 390

ISBN-13: 0192845292

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The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders have argued, the goal is not to replace people, but to empower them by making design choices that give humans control over technology. In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist's guide to how artificial intelligence can be used to augment and enhance humans' lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education, accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity.

Human-in-the-Loop Machine Learning
Human-in-the-Loop Machine Learning

Author: Robert Munro

Publisher: Simon and Schuster

Published: 2021-07-20

Total Pages: 422

ISBN-13: 1617296740

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Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. Human-in-the-loop machine learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to dreate training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.

Data Feminism
Data Feminism

Author: Catherine D'Ignazio

Publisher: MIT Press

Published: 2023-10-03

Total Pages: 328

ISBN-13: 026254718X

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A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

Social Sensing
Social Sensing

Author: Dong Wang

Publisher: Morgan Kaufmann

Published: 2015-04-17

Total Pages: 232

ISBN-13: 0128011319

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Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion. Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability Presents novel theoretical foundations for assured social sensing and modeling humans as sensors Includes case studies and application examples based on real data sets Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book

From Tool to Partner
From Tool to Partner

Author: JONATHAN GRUDIN

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 165

ISBN-13: 3031022181

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This is the first comprehensive history of human-computer interaction (HCI). Whether you are a user-experience professional or an academic researcher, whether you identify with computer science,human factors, information systems, information science, design, or communication, you can discover how your experiences fit into the expanding field of HCI. You can determine where to look for relevant information in other fields—and where you won't find it. This book describes the different fields that have participated in improving our digital tools.It is organized chronologically, describing major developments across fields in each period. Computer use has changed radically, but many underlying forces are constant. Technology has changed rapidly, human nature very little. An irresistible force meets an immovable object. The exponential rate of technological change gives us little time to react before technology moves on. Patterns and trajectories described in this book provide your best chance to anticipate what could come next. We have reached a turning point. Tools that we built for ourselves to use are increasingly influencing how we use them, in ways that are planned and sometimes unplanned. The book ends with issues worthy of consideration as we explore the new world that we and our digital partners are shaping.

Data-Driven Personas
Data-Driven Personas

Author: Bernard J. Jansen

Publisher: Morgan & Claypool Publishers

Published: 2021-02-05

Total Pages: 347

ISBN-13: 1636390692

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This book traces the techniques that have enabled the development of data-driven personas and how they can be leveraged as tools for empathizing and understanding users. Data-driven personas are a significant advancement in the fields of human-centered informatics and human-computer interaction. Data-driven personas enhance user understanding by combining the empathy inherent with personas with the rationality inherent in analytics using computational methods. Via the employment of these computational methods, the data-driven persona method permits the use of large-scale user data, which is a novel advancement in persona creation. A common approach for increasing stakeholder engagement about audiences, customers, or users, persona creation remained relatively unchanged for several decades. However, the availability of digital user data, data science algorithms, and easy access to analytics platforms provide avenues and opportunities to enhance personas from often sketchy representations of user segments to precise, actionable, interactive decision-making tools—data-driven personas! Using the data-driven approach, the persona profile can serve as an interface to a fully functional analytics system that can present user representation at various levels of information granularity for more task-aligned user insights. Presenting a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes, we illustrate applying this framework via practical use cases in areas of system design, digital marketing, and content creation to demonstrate the application of data-driven personas in practical applied situations. We then present an overview of a fully functional data-driven persona system as an example of multi-level information aggregation needed for decision making about users. We demonstrate that data-driven personas systems can provide critical, empathetic, and user-understanding functionalities for anyone needing such insights.

Human-Centered Software Engineering - Integrating Usability in the Software Development Lifecycle
Human-Centered Software Engineering - Integrating Usability in the Software Development Lifecycle

Author: Ahmed Seffah

Publisher: Springer Science & Business Media

Published: 2006-06-26

Total Pages: 406

ISBN-13: 1402041136

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Human-CenteredSoftwareEngineering: BridgingHCI,UsabilityandSoftwareEngineering From its beginning in the 1980’s, the ?eld of human-computer interaction (HCI) has beende?nedasamultidisciplinaryarena. BythisImeanthattherehas beenanexplicit recognition that distinct skills and perspectives are required to make the whole effort of designing usable computer systems work well. Thus people with backgrounds in Computer Science (CS) and Software Engineering (SE) joined with people with ba- grounds in various behavioral science disciplines (e. g. , cognitive and social psych- ogy, anthropology)inaneffortwhereallperspectiveswereseenasessentialtocreating usable systems. But while the ?eld of HCI brings individuals with many background disciplines together to discuss a common goal - the development of useful, usable, satisfying systems - the form of the collaboration remains unclear. Are we striving to coordinate the varied activities in system development, or are we seeking a richer collaborative framework? In coordination, Usability and SE skills can remain quite distinct and while the activities of each group might be critical to the success of a project, we need only insure that critical results are provided at appropriate points in the development cycle. Communication by one group to the other during an activity might be seen as only minimally necessary. In collaboration, there is a sense that each group can learn something about its own methods and processes through a close pa- nership with the other. Communication during the process of gathering information from target users of a system by usability professionals would not be seen as so- thing that gets in the way of the essential work of software engineering professionals.