Doosti, Hassan.
Ethics in Statistics : Opportunities and Challenges. - 1st ed. - 1 online resource (597 pages)
Intro -- Preface -- Setting the Tone: -- Expressing Gratitude: -- Explaining the Motivation: -- Insights into the Editing Process: -- Chapter 1 Ethics in Statistics: Opportunities and Challenges -- Dr. Andrea Vicini0F( -- Introduction -- Privacy, profiling and personal data: where is the limit? -- Methods and transparency: why is adequate attention not paid to the methodology adopted in preliminary foresight studies? -- Governance of statistical institutions: what is the role of statistical institutions in society, and how must their relationship with the government be regulated in a democracy? -- References -- Chapter 2 Learning Outcomes Supporting the Integration of Ethical Reasoning into Quantitative Courses: Three Tasks for Use in Three General Contexts -- Rochelle E. Tractenberg1F( -- Abstract -- Getting "ethics" into quantitative courses: neither simple nor straightforward -- Background: Education Sciences -- What is a learning outcome (LO)? -- Role of LOs in curriculum and instructional design -- Formulating LOs -- Scaffolding and Blooms -- Achieving psychometric validity through instructional design -- How to use Messick's three questions: -- Contexts in quantitative courses for three activities or tasks that comprise Ethical Reasoning -- Stakeholder Analysis: a template for considering the impact of decisions in mathematical and quantitative practice. -- Learning outcomes featuring Stakeholder Analysis, codes/ guidelines, and ethical reasoning at three Bloom's levels of complexity -- Learning outcomes early in course (low Blooms) -- Learning outcomes midway through course (middle Blooms) -- Learning outcomes at end of course (high Blooms) -- Discussion -- References -- Chapter 3 Ethics in Image Processing: Opportunities and Challenges -- Helia Farhood4F(, Fariba Lotfi*5F(, Matineh Pooshideh*, Amin Beheshti*, and Samuel Muller*. Abstract -- Introduction -- Consent for the Use of Image Data -- Consent and Ethical Obligation -- Consent for Academic Purpose -- Consent for Commercial Purpose -- Image Manipulation -- Facial Image Manipulation -- Detecting Fake Images -- Ethical Items in Image Collection and Datasets -- Medical Imaging -- Non-medical Imaging (Biometric) -- Conclusion -- Bibliography -- Chapter 4 What is "Ethical AI"? Leading or Participating on an Ethical Team and/or Working in Statistics, Data Science, and Artificial Intelligence -- Rochelle E. Tractenberg26F( -- Abstract -- Introduction -- What is AI -- What does "ethical AI" mean? -- Association of Computing Machinery (ACM) Code of Ethics and Professional Conduct (2018) -- ASA Ethical Guidelines for Statistical Practice (2022) -- Discussion -- Conclusions -- References -- Chapter 5 Data Governance and Quality: Involving Data Sources' Owners -- Irena Križman and Bruno Tissot29F( -- Abstract -- Introduction -- The impact of the data revolution -- Substituting or complementing official statistics? -- Quality principles: from official statistics to the private sector -- Towards a set of self-commitments for providers of alternative data? -- Coordination between data providers -- Partnership between data providers and official statisticians - good practice examples from Slovenia -- References -- Chapter 6 Facilitating the Integration of Ethical Reasoning into Quantitative Courses: Stakeholder Analysis, Ethical Practice Standards, and Case Studies -- Rochelle E. Tractenberg39F(, Suzanne Thornton40F(( -- Abstract -- Introduction -- Options for "teaching ethics" -- Case studies -- Teaching Ethical Reasoning instead of "ethics" -- Stakeholder analysis: a relevant subset of Ethical Reasoning -- Using the ASA Ethical Guidelines explicitly: a relevant subset of Ethical Reasoning. Integrating ethical content in a Mathematical Statistics course -- Syllabus outline -- Notes on syllabus elements: Unit One. Motivate and contextualize -- Notes on syllabus elements: Unit Two. From examples to practice -- Notes on syllabus elements: Unit Three. Independent and collaborative critical thinking -- Discussion and Conclusions -- Discussion -- Conclusions -- References -- APPENDIX: Syllabus for a First Course in Mathematical Statistics -- Learning objectives -- Topics outline for the semester -- Unit One - Thoughtful use of estimation techniques -- Unit Two - Statistical inference and stewardship -- Unit Three - Disciplinary best practices for common study designs and analyses -- Chapter 7 Ethical Decision-Making in Data Analysis: Navigating Challenges and Ensuring Integrity -- Dr. Noah Mutai48F( -- Abstract -- Introduction -- Ethical Considerations in Data Collection -- Protecting Sensitive Data and Maintaining Anonymity -- Balancing Benefits and Risks -- Ensuring Representation and Inclusivity -- Navigating Bias and Fairness -- Selection Bias -- Sampling Bias -- Non-Response Bias -- Measurement Bias -- Confirmation Bias -- Reporting Bias -- Response Bias -- Experimenter Bias -- Sampling Frame Bias -- Strategies for Bias Mitigation in Data Analysis -- Random Sampling -- Matching and Pairing -- Blind Analysis -- Variable Transformation -- Sensitivity Analysis -- External Validation -- Transparency and Openness -- Clear Communication of Methods in Data Analysis -- Comprehensive Method Descriptions -- Technical Detail and Jargon -- Step-by-Step Breakdown -- Rationale Behind Choices -- Assumptions and Limitations -- Avoiding Ambiguity -- Transparency in Code and Scripts -- Feedback and Peer Review -- Documentation of Procedures in Data Analysis -- Comprehensive Record Keeping -- Timestamps and Dates -- Data Cleaning and Transformation. Software and Tools -- Step-by-Step Descriptions -- Variables and Definitions -- Code and Scripts -- Assumptions and Choices -- Changes and Corrections -- Feedback Incorporation -- Archival and Storage -- Open Data Practices in Data Analysis -- Data Sharing -- Data Depositories -- Documentation -- Metadata -- Licensing and Terms of Use -- Code and Scripts -- Reproducibility and Replicability -- Version Control -- Data Privacy and Anonymization -- Data Access Restrictions -- Communication Channels -- Engaging with the Community -- Pre-Analysis Plans in Data Analysis -- Defining Research Objectives -- Hypothesis Formulation -- Data Collection Procedures -- Variable Selection and Transformation -- Analysis Methods -- Dealing with Multiple Comparisons -- Controlling for Confounding Variables -- Publication and Reporting Intentions -- Transparency in Changes -- Peer Review and Validation -- Disclosure of Limitations in Data Analysis -- Types of Limitations -- Data Limitations -- Methodological Limitations -- Sampling Limitations -- Scope and Generalizability -- Assumption and Uncertainty -- External Factors -- Handling Uncertainty and Interpretation -- Communicating Uncertainty Responsibly -- Avoiding Overinterpretation -- Contextualizing Results -- Recognizing Implications and Limitations -- Avoiding Sensationalism -- Balancing Transparency with Actionability -- Conclusion -- Addressing Conflicts of Interest -- Identifying Conflicts of Interest -- Maintaining Objectivity and Independence -- Disclosing Conflicts -- Managing Conflicts -- Balancing Ethical Considerations -- Ethical Reporting -- Conclusion -- Impact on Society and Decision-Making -- Responsible Communication of Results -- Social, Economic, and Political Consequences -- Balancing Ethical Considerations -- Mitigating Unintended Negative Consequences -- References. Chapter 8 Teaching at the Intersection of Social Justice, Ethics, and the ASA Ethical Guidelines for Statistical Practice -- Rochelle E. Tractenberg49F( -- Abstract -- Introduction -- Curriculum Development Guidelines -- The Statistics and Data Science Pipeline -- The ASA Ethical Guidelines for Statistical Practice -- The Stakeholder Analysis -- Ethical Reasoning Paradigm -- Synthesizing these tools to integrate ethics and social justice -- Dimension 1: practice ethically -- Dimension 2: Identify & -- respond to unethical act/request -- Discussion and Conclusions -- References -- Chapter 9 Validation of Statistical Results - An Ethical Perspective -- G. S. Dissanayake and T. M. R. P. Yatigammana. -- Abstract -- Introduction -- Data and models in statistics requiring validation for an ethical outcome. -- Examples for validated data and models. -- Discussion -- Conclusion -- References -- Chapter 10 Ethical Considerations for Data Involving Human Gender and Sex Variables -- Suzanne Thornton50F(, Rochelle E. Tractenberg51F(( -- Abstract -- History of gender and sex data -- Gender and sex minorities in (modern) ethical research design -- Moving from connotative categorizations towards descriptive ones -- Two-step questioning method -- Direct questioning method -- Reflections on these methods -- Ethical considerations throughout the statistics and data science pipeline -- Tasks 1-2: Planning/Designing and Data collection/munging/ wrangling -- Tasks 3-4: Analysis (perform or program to perform) and Interpretation -- Tasks 5-6: Documenting your work and Reporting your results/communication -- Discussion and conclusion -- Acknowledgements -- References -- Chapter 11 Empowering Minds to Nurture Ethical Awareness on Infographic Integrity Among Students and Educators. Salma Banu Nazeer Khan53F(, Ayse Aysin Bilgin54F((, Deborah Richards* and Paul Formosa55F(((.
Data plays a vital role in different parts of our lives. In the world of big data, and policy determined by a variety of statistical artifacts, discussions around the ethics of data gathering, manipulation and presentation are increasingly important. Ethics in Statistics aims to make a significant contribution to that debate. The processes of gathering data through sampling, summarising of the findings, and extending results to a population, need to be checked via an ethical prospective, as well as a statistical one. Statistical learning without ethics can be harmful for mankind.This edited collection brings together contributors in the field of data science, data analytics and statistics, to share their thoughts about the role of ethics in different aspects of statistical learning.
9781871891669
Electronic books.
Ethics in Statistics : Opportunities and Challenges. - 1st ed. - 1 online resource (597 pages)
Intro -- Preface -- Setting the Tone: -- Expressing Gratitude: -- Explaining the Motivation: -- Insights into the Editing Process: -- Chapter 1 Ethics in Statistics: Opportunities and Challenges -- Dr. Andrea Vicini0F( -- Introduction -- Privacy, profiling and personal data: where is the limit? -- Methods and transparency: why is adequate attention not paid to the methodology adopted in preliminary foresight studies? -- Governance of statistical institutions: what is the role of statistical institutions in society, and how must their relationship with the government be regulated in a democracy? -- References -- Chapter 2 Learning Outcomes Supporting the Integration of Ethical Reasoning into Quantitative Courses: Three Tasks for Use in Three General Contexts -- Rochelle E. Tractenberg1F( -- Abstract -- Getting "ethics" into quantitative courses: neither simple nor straightforward -- Background: Education Sciences -- What is a learning outcome (LO)? -- Role of LOs in curriculum and instructional design -- Formulating LOs -- Scaffolding and Blooms -- Achieving psychometric validity through instructional design -- How to use Messick's three questions: -- Contexts in quantitative courses for three activities or tasks that comprise Ethical Reasoning -- Stakeholder Analysis: a template for considering the impact of decisions in mathematical and quantitative practice. -- Learning outcomes featuring Stakeholder Analysis, codes/ guidelines, and ethical reasoning at three Bloom's levels of complexity -- Learning outcomes early in course (low Blooms) -- Learning outcomes midway through course (middle Blooms) -- Learning outcomes at end of course (high Blooms) -- Discussion -- References -- Chapter 3 Ethics in Image Processing: Opportunities and Challenges -- Helia Farhood4F(, Fariba Lotfi*5F(, Matineh Pooshideh*, Amin Beheshti*, and Samuel Muller*. Abstract -- Introduction -- Consent for the Use of Image Data -- Consent and Ethical Obligation -- Consent for Academic Purpose -- Consent for Commercial Purpose -- Image Manipulation -- Facial Image Manipulation -- Detecting Fake Images -- Ethical Items in Image Collection and Datasets -- Medical Imaging -- Non-medical Imaging (Biometric) -- Conclusion -- Bibliography -- Chapter 4 What is "Ethical AI"? Leading or Participating on an Ethical Team and/or Working in Statistics, Data Science, and Artificial Intelligence -- Rochelle E. Tractenberg26F( -- Abstract -- Introduction -- What is AI -- What does "ethical AI" mean? -- Association of Computing Machinery (ACM) Code of Ethics and Professional Conduct (2018) -- ASA Ethical Guidelines for Statistical Practice (2022) -- Discussion -- Conclusions -- References -- Chapter 5 Data Governance and Quality: Involving Data Sources' Owners -- Irena Križman and Bruno Tissot29F( -- Abstract -- Introduction -- The impact of the data revolution -- Substituting or complementing official statistics? -- Quality principles: from official statistics to the private sector -- Towards a set of self-commitments for providers of alternative data? -- Coordination between data providers -- Partnership between data providers and official statisticians - good practice examples from Slovenia -- References -- Chapter 6 Facilitating the Integration of Ethical Reasoning into Quantitative Courses: Stakeholder Analysis, Ethical Practice Standards, and Case Studies -- Rochelle E. Tractenberg39F(, Suzanne Thornton40F(( -- Abstract -- Introduction -- Options for "teaching ethics" -- Case studies -- Teaching Ethical Reasoning instead of "ethics" -- Stakeholder analysis: a relevant subset of Ethical Reasoning -- Using the ASA Ethical Guidelines explicitly: a relevant subset of Ethical Reasoning. Integrating ethical content in a Mathematical Statistics course -- Syllabus outline -- Notes on syllabus elements: Unit One. Motivate and contextualize -- Notes on syllabus elements: Unit Two. From examples to practice -- Notes on syllabus elements: Unit Three. Independent and collaborative critical thinking -- Discussion and Conclusions -- Discussion -- Conclusions -- References -- APPENDIX: Syllabus for a First Course in Mathematical Statistics -- Learning objectives -- Topics outline for the semester -- Unit One - Thoughtful use of estimation techniques -- Unit Two - Statistical inference and stewardship -- Unit Three - Disciplinary best practices for common study designs and analyses -- Chapter 7 Ethical Decision-Making in Data Analysis: Navigating Challenges and Ensuring Integrity -- Dr. Noah Mutai48F( -- Abstract -- Introduction -- Ethical Considerations in Data Collection -- Protecting Sensitive Data and Maintaining Anonymity -- Balancing Benefits and Risks -- Ensuring Representation and Inclusivity -- Navigating Bias and Fairness -- Selection Bias -- Sampling Bias -- Non-Response Bias -- Measurement Bias -- Confirmation Bias -- Reporting Bias -- Response Bias -- Experimenter Bias -- Sampling Frame Bias -- Strategies for Bias Mitigation in Data Analysis -- Random Sampling -- Matching and Pairing -- Blind Analysis -- Variable Transformation -- Sensitivity Analysis -- External Validation -- Transparency and Openness -- Clear Communication of Methods in Data Analysis -- Comprehensive Method Descriptions -- Technical Detail and Jargon -- Step-by-Step Breakdown -- Rationale Behind Choices -- Assumptions and Limitations -- Avoiding Ambiguity -- Transparency in Code and Scripts -- Feedback and Peer Review -- Documentation of Procedures in Data Analysis -- Comprehensive Record Keeping -- Timestamps and Dates -- Data Cleaning and Transformation. Software and Tools -- Step-by-Step Descriptions -- Variables and Definitions -- Code and Scripts -- Assumptions and Choices -- Changes and Corrections -- Feedback Incorporation -- Archival and Storage -- Open Data Practices in Data Analysis -- Data Sharing -- Data Depositories -- Documentation -- Metadata -- Licensing and Terms of Use -- Code and Scripts -- Reproducibility and Replicability -- Version Control -- Data Privacy and Anonymization -- Data Access Restrictions -- Communication Channels -- Engaging with the Community -- Pre-Analysis Plans in Data Analysis -- Defining Research Objectives -- Hypothesis Formulation -- Data Collection Procedures -- Variable Selection and Transformation -- Analysis Methods -- Dealing with Multiple Comparisons -- Controlling for Confounding Variables -- Publication and Reporting Intentions -- Transparency in Changes -- Peer Review and Validation -- Disclosure of Limitations in Data Analysis -- Types of Limitations -- Data Limitations -- Methodological Limitations -- Sampling Limitations -- Scope and Generalizability -- Assumption and Uncertainty -- External Factors -- Handling Uncertainty and Interpretation -- Communicating Uncertainty Responsibly -- Avoiding Overinterpretation -- Contextualizing Results -- Recognizing Implications and Limitations -- Avoiding Sensationalism -- Balancing Transparency with Actionability -- Conclusion -- Addressing Conflicts of Interest -- Identifying Conflicts of Interest -- Maintaining Objectivity and Independence -- Disclosing Conflicts -- Managing Conflicts -- Balancing Ethical Considerations -- Ethical Reporting -- Conclusion -- Impact on Society and Decision-Making -- Responsible Communication of Results -- Social, Economic, and Political Consequences -- Balancing Ethical Considerations -- Mitigating Unintended Negative Consequences -- References. Chapter 8 Teaching at the Intersection of Social Justice, Ethics, and the ASA Ethical Guidelines for Statistical Practice -- Rochelle E. Tractenberg49F( -- Abstract -- Introduction -- Curriculum Development Guidelines -- The Statistics and Data Science Pipeline -- The ASA Ethical Guidelines for Statistical Practice -- The Stakeholder Analysis -- Ethical Reasoning Paradigm -- Synthesizing these tools to integrate ethics and social justice -- Dimension 1: practice ethically -- Dimension 2: Identify & -- respond to unethical act/request -- Discussion and Conclusions -- References -- Chapter 9 Validation of Statistical Results - An Ethical Perspective -- G. S. Dissanayake and T. M. R. P. Yatigammana. -- Abstract -- Introduction -- Data and models in statistics requiring validation for an ethical outcome. -- Examples for validated data and models. -- Discussion -- Conclusion -- References -- Chapter 10 Ethical Considerations for Data Involving Human Gender and Sex Variables -- Suzanne Thornton50F(, Rochelle E. Tractenberg51F(( -- Abstract -- History of gender and sex data -- Gender and sex minorities in (modern) ethical research design -- Moving from connotative categorizations towards descriptive ones -- Two-step questioning method -- Direct questioning method -- Reflections on these methods -- Ethical considerations throughout the statistics and data science pipeline -- Tasks 1-2: Planning/Designing and Data collection/munging/ wrangling -- Tasks 3-4: Analysis (perform or program to perform) and Interpretation -- Tasks 5-6: Documenting your work and Reporting your results/communication -- Discussion and conclusion -- Acknowledgements -- References -- Chapter 11 Empowering Minds to Nurture Ethical Awareness on Infographic Integrity Among Students and Educators. Salma Banu Nazeer Khan53F(, Ayse Aysin Bilgin54F((, Deborah Richards* and Paul Formosa55F(((.
Data plays a vital role in different parts of our lives. In the world of big data, and policy determined by a variety of statistical artifacts, discussions around the ethics of data gathering, manipulation and presentation are increasingly important. Ethics in Statistics aims to make a significant contribution to that debate. The processes of gathering data through sampling, summarising of the findings, and extending results to a population, need to be checked via an ethical prospective, as well as a statistical one. Statistical learning without ethics can be harmful for mankind.This edited collection brings together contributors in the field of data science, data analytics and statistics, to share their thoughts about the role of ethics in different aspects of statistical learning.
9781871891669
Electronic books.