Brandeis Marshall (32 results)

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Paperback. Condition: Good. 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 transparencyExplorations of computational thinking in practiceStrategies for encouraging accountability in techWays to avoid double-edged data visualizationSchemes for governing data structures with law and algorithms.…

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Paperback. Condition: New. 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 transparencyExplorations of computational thinking in practiceStrategies for encouraging accountability in techWays to avoid double-edged data visualizationSchemes for governing data structures with law and algorithms.…

Paperback. Condition: New. 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 transparencyExplorations of computational thinking in practiceStrategies for encouraging accountability in techWays to avoid double-edged data visualizationSchemes for governing data structures with law and algorithms.…

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Paperback. Condition: New. This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries.Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced.Mitigating Bias in Machine Learning addresses:Ethical and Societal Implications of Machine LearningSocial Media and Health Information DisseminationComparative Case Study of Fairness ToolkitsBias Mitigation in Hate Speech DetectionUnintended Systematic Biases in Natural Language Processing Combating Bias in Large Language ModelsRecognizing Bias in Medical Machine Learning and AI ModelsMachine Learning Bias in HealthcareAchieving Systemic Equity in Socioecological SystemsCommunity Engagement for Machine Learning.…

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Paperback. Condition: New. This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries.Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced.Mitigating Bias in Machine Learning addresses:Ethical and Societal Implications of Machine LearningSocial Media and Health Information DisseminationComparative Case Study of Fairness ToolkitsBias Mitigation in Hate Speech DetectionUnintended Systematic Biases in Natural Language Processing Combating Bias in Large Language ModelsRecognizing Bias in Medical Machine Learning and AI ModelsMachine Learning Bias in HealthcareAchieving Systemic Equity in Socioecological SystemsCommunity Engagement for Machine Learning.…

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Paperback. Condition: New. 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 transparencyExplorations of computational thinking in practiceStrategies for encouraging accountability in techWays to avoid double-edged data visualizationSchemes for governing data structures with law and algorithms.…

Paperback. Condition: New. This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries.Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced.Mitigating Bias in Machine Learning addresses:Ethical and Societal Implications of Machine LearningSocial Media and Health Information DisseminationComparative Case Study of Fairness ToolkitsBias Mitigation in Hate Speech DetectionUnintended Systematic Biases in Natural Language Processing Combating Bias in Large Language ModelsRecognizing Bias in Medical Machine Learning and AI ModelsMachine Learning Bias in HealthcareAchieving Systemic Equity in Socioecological SystemsCommunity Engagement for Machine Learning.…