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Human-Data Interaction Framework: A Comprehensive Model for a Future Driven by Data and Humans (2407.21010v1)

Published 30 Jul 2024 in cs.CY and cs.HC

Abstract: In an age defined by rapid data expansion, the connection between individuals and their digital footprints has become more intricate. The Human-Data Interaction (HDI) framework has become an essential approach to tackling the challenges and ethical issues associated with data governance and utilization in the modern digital world. This paper outlines the fundamental steps required for organizations to seamlessly integrate HDI principles, emphasizing auditing, aligning, formulating considerations, and the need for continuous monitoring and adaptation. Through a thorough audit, organizations can critically assess their current data management practices, trace the data lifecycle from collection to disposal, and evaluate the effectiveness of existing policies, security protocols, and user interfaces. The next step involves aligning these practices with the main HDI principles, such as informed consent, data transparency, user control, algorithm transparency, and ethical data use, to identify gaps that need strategic action. Formulating preliminary considerations includes developing policies and technical solutions to close identified gaps, ensuring that these practices not only meet legal standards, but also promote fairness and accountability in data interactions. The final step, monitoring and adaptation, highlights the need for setting up continuous evaluation mechanisms and being responsive to technological, regulatory, and societal developments, ensuring HDI practices stay up-to-date and effective. Successful implementation of the HDI framework requires multi-disciplinary collaboration, incorporating insights from technology, law, ethics, and user experience design. The paper posits that this comprehensive approach is vital for building trust and legitimacy in digital environments, ultimately leading to more ethical, transparent, and user-centric data interactions.

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References (136)
  1. Data-driven smart manufacturing. Journal of Manufacturing Systems, 2018.
  2. Pak-Hang Wong. Democratizing algorithmic fairness. Philosophy & Technology, 33:225 – 244, 2019.
  3. Human-data interaction: The human face of the data-driven society. RELX Group (Netherlands), 01 2014.
  4. Arianna Rossi and G. Lenzini. Transparency by design in data-informed research: A collection of information design patterns. Comput. Law Secur. Rev., 37:105402, 2020.
  5. L. Edwards and Michael Veale. Enslaving the algorithm: From a “right to an explanation” to a “right to better decisions”? IEEE Security & Privacy, 16:46–54, 2018.
  6. Shoshana Zuboff. Big other: surveillance capitalism and the prospects of an information civilization. Journal of Information Technology, 30(1):75–89, 2015.
  7. Mark Andrejevic. Big data, big questions| the big data divide. International Journal of Communication, 8:17, 2014.
  8. The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2):2053951716679679, 2016.
  9. Americans and privacy: Concerned, confused and feeling lack of control over their personal information. Technical report, Pew Research Center, 2019.
  10. Notice and consent in a world of big data. International Data Privacy Law, 3(2):67–73, 2013.
  11. The digital divide: The internet and social inequality in international perspective. Routledge, 2013.
  12. Ai4people—an ethical framework for a good ai society: opportunities, risks, principles, and recommendations. Minds and Machines, 28(4):689–707, 2018.
  13. Global trends in privacy protection: An international survey of privacy, data protection, and surveillance laws and developments. J. Marshall J. Computer & Info. L., 18:1, 1999.
  14. Privacy and human behavior in the age of information. Science, 347(6221):509–514, 2015.
  15. Human data interaction: Historical lessons from social studies and cscw.
  16. Playing the legal card: Using ideation cards to raise data protection issues within the design process. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI ’15, page 457–466, New York, NY, USA, 2015. Association for Computing Machinery.
  17. Beyond the eula: Improving consent for data mining, 2017.
  18. Understanding human-data interaction: Literature review and recommendations for design. Elsevier BV, 134:13–32, 02 2020.
  19. Realising the right to data portability for the domestic internet of things. Personal Ubiquitous Comput., 22(2):317–332, apr 2018.
  20. Technologies for trustworthy machine learning: A survey in a socio-technical context, 2022.
  21. Marina Da Bormida. The big data world: Benefits, threats and ethical challenges, 12 2021.
  22. Designing for human data interaction in data-driven media experiences. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI EA ’21, New York, NY, USA, 2021. Association for Computing Machinery.
  23. Designing human data interactions in healthcare. Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 4(16):160459–160459, 10 2018.
  24. Martin Maguire. Socio-technical systems and interaction design – 21st century relevance. Elsevier BV, 45(2):162–170, 03 2014.
  25. The tyranny of data? the bright and dark sides of data-driven decision-making for social good, 01 2016.
  26. Data management conceptual algorithm of transnational digital scientific infrastructure as an answer to the globalization challenges. EDP Sciences, 92:05002–05002, 01 2021.
  27. Caveat emptor: The risks of using big data for human development. Institute of Electrical and Electronics Engineers, 38(3):82–90, 09 2019.
  28. A global perspective on data powering responsible ai solutions in health applications. Springer Nature, 05 2023.
  29. Mapping and comparing data governance frameworks: A benchmarking exercise to inform global data governance deliberations, 01 2023.
  30. Suresh Venkatasubramanian undefined Sorelle A Friedler, Carlos Scheidegger. The (im)possibility of fairness, 03 2021.
  31. The ethics of information transparency. Springer Science+Business Media, 11(2):105–112, 03 2009.
  32. Examining structural relationships between work engagement, organizational procedural justice, knowledge sharing, and innovative work behavior for sustainable organizations. Multidisciplinary Digital Publishing Institute, 9(2):205–205, 02 2017.
  33. A survey on network data collection. Elsevier BV, 116:9–23, 08 2018.
  34. Privacy in computer ethics: Navigating the digital age. Institute of Advanced Engineering and Science (IAES), 4(2):183–190, 07 2023.
  35. David J. Hand. Aspects of data ethics in a changing world: Where are we now? Mary Ann Liebert, Inc., 6(3):176–190, 09 2018.
  36. Katie Shilton. Values and ethics in human-computer interaction. Now Publishers, 12(2):107–171, 01 2018.
  37. Blockchain-empowered trustworthy data sharing: Fundamentals, applications, and challenges, 03 2023.
  38. Siani Pearson. Taking account of privacy when designing cloud computing services. Taking account of privacy when designing cloud computing services, 01 2009.
  39. Ethical assurance: a practical approach to the responsible design, development, and deployment of data-driven technologies. Springer Nature, 3(1):73–98, 06 2022.
  40. Human-data interaction through design: An explorative step from theory to practice using design as a vehicle. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI EA ’21, New York, NY, USA, 2021. Association for Computing Machinery.
  41. An interview method for engaging personal data. Association for Computing Machinery, 5(4):1–28, 12 2021.
  42. On weighting the components of the human development index: A statistical justification. Taylor & Francis, 12(2):183–202, 05 2011.
  43. Cyber security threats and vulnerabilities: A systematic mapping study. Springer Science+Business Media, 45(4):3171–3189, 01 2020.
  44. Nili Steinfeld. “i agree to the terms and conditions”: (how) do users read privacy policies online? an eye-tracking experiment. Elsevier BV, 55:992–1000, 02 2016.
  45. The (im)possibility of fairness, 03 2021.
  46. The collaborative critique: An inspection method for expert evaluation of user interfaces. Taylor & Francis, 31(11):843–859, 08 2015.
  47. Enabling public auditability and data dynamics for storage security in cloud computing. Institute of Electrical and Electronics Engineers, 22(5):847–859, 05 2011.
  48. Privacy and human behavior in the age of information. Science (New York, N.Y.), 347:509–14, 01 2015.
  49. Modernizing data control: Making personal digital data mutually beneficial for citizens and industry a computing community consortium (ccc) quadrennial paper.
  50. Bartha Maria Knoppers. Framework for responsible sharing of genomic and health-related data. Springer Science+Business Media, 8(1), 10 2014.
  51. Stakeholder-driven, consensus development methods to design an ethical framework and guidelines for engaged research. Public Library of Science, 13(6):e0199451–e0199451, 06 2018.
  52. Privacy by design the 7 foundational principles.
  53. Transparency mechanisms: Building publicness into public services. Wiley, 28(4):471–489, 12 2001.
  54. Kathleen Creel. Transparency in complex computational systems. Cambridge University Press, 87(4):568–589, 10 2020.
  55. Stefaan Verhulst. Reimagining data responsibility: 10 new approaches toward a culture of trust in re-using data to address critical public needs. Cambridge University Press, 3, 01 2021.
  56. Stefaan G Verhulst undefined. Reimagining data responsibility: 10 new approaches toward a culture of trust in re-using data to address critical public needs | data & policy | cambridge core, 02 2023.
  57. Why fairness cannot be automated: Bridging the gap between eu non-discrimination law and ai. RELX Group (Netherlands), 01 2020.
  58. Towards a standard for identifying and managing bias in artificial intelligence.
  59. Algorithmic fairness in computational medicine. Elsevier BV, 84:104250–104250, 10 2022.
  60. Data justice in practice: A guide for developers, 01 2022.
  61. Using accountability to reduce access policy violations in information systems. Taylor & Francis, 29(4):263–290, 04 2013.
  62. Charna R. Parkey. Who is accountable for data bias? Taylor & Francis, 34(2):39–43, 04 2021.
  63. How to use relevant data for maximal benefit with minimal risk: digital health data governance to protect vulnerable populations in low-income and middle-income countries. BMJ, 4(2):e001395–e001395, 04 2019.
  64. Soheil Human. Advanced data protection control (adpc): An interdisciplinary overview, 01 2022.
  65. A framework to implement stakeholder participation in environmental projects. Elsevier BV, 111:213–219, 11 2012.
  66. Participatory design in an era of participation. Taylor & Francis, 13(2):65–69, 04 2017.
  67. Helen Nissenbaum. Privacy as contextual integrity. Wash. L. Rev., 79:119, 2004.
  68. Ben Shneiderman. Human-centered AI. Oxford University Press, 2020.
  69. Fei-Fei Li. Human-centered ai. arXiv preprint arXiv:1810.05557, 2018.
  70. Daniel J Solove. Introduction: Privacy self-management and the consent dilemma. Harv. L. Rev., 126:1880, 2013.
  71. Dynamic consent in the digital age of biology: online initiatives and regulatory considerations. Journal of Medical Internet Research, 18(9):e5710, 2016.
  72. Opportunities and challenges in the new innovation landscape: Implications for innovation auditing and innovation management. Elsevier BV, 37(2):151–164, 04 2019.
  73. Improving compliance with regulations: Choices and outcomes for local government. Taylor & Francis, 64(3):324–334, 09 1998.
  74. Data governance and data stewardship: A success procedure. In 2020 8th International Conference on Information Technology and Multimedia (ICIMU), pages 54–61, 2020.
  75. Maler Eve. Extending the power of consent with user-managed access: A standard architecture for asynchronous, centralizable, internet-scalable consent. In 2015 IEEE Security and Privacy Workshops, pages 175–179, 2015.
  76. Raincloud plots: a multi-platform tool for robust data visualization. Wellcome, 4:63–63, 01 2021.
  77. Explaining explanations: An overview of interpretability of machine learning, 01 2018.
  78. Privacy enhancing technologies implementation: An investigation of its impact on work processes and employee perception. Telematics and Informatics, 38:13–29, 2019.
  79. Assessing staff awareness and effectiveness of educational training on it security and privacy in a large healthcare organization. Dove Medical Press, Volume 12:73–81, 01 2019.
  80. European union regulations on algorithmic decision making and a “right to explanation”. Association for the Advancement of Artificial Intelligence, 38(3):50–57, 09 2017.
  81. Chrysanthos Dellarocas. The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Institute for Operations Research and the Management Sciences, 49(10):1407–1424, 10 2003.
  82. The ethics of big data: Current and foreseeable issues in biomedical contexts. 22(2):303 – 341, 05 2015.
  83. Tracey A. Swift. Trust, reputation and corporate accountability to stakeholders. Business Ethics: A European Review, 10:16–26, 2001.
  84. How algorithms discriminate based on data they lack: Challenges, solutions, and policy implications. Penn State University Press, 8:78–115, 03 2018.
  85. Federal data strategy, 01 2023.
  86. Responsible data management. Association for Computing Machinery, 13(12):3474–3488, 08 2020.
  87. Ethical challenges associated with the development and deployment of brain computer interface technology. Springer Science+Business Media, 7(2):109–122, 07 2013.
  88. Floridi; Luciano; Cowls; Josh; Beltrametti; Monica; Chatila; Raja; Chazerand; Patrice; Dignum; Virginia; Luetge; Christoph; Madelin; Robert; Pagallo; Ugo; Rossi; Francesca; Schafer; Burkhard; Valcke; Peggy; Vayena; Effy undefined. An ethical framework for a good ai society: Opportunities, risks, principles, and recommendations, 11 2021.
  89. The opportunities and ethics of big data: practical priorities for a national council of data ethics. Royal Society, 374(2083):20160116–20160116, 12 2016.
  90. What is data ethics? Royal Society, 374(2083):20160360–20160360, 12 2016.
  91. Sustainable ai: Environmental implications, challenges and opportunities, 01 2021.
  92. Federica Lucivero. Big data, big waste? a reflection on the environmental sustainability of big data initiatives. Springer Science+Business Media, 26(2):1009–1030, 12 2019.
  93. Data integration, 11 2014.
  94. Security with privacy, 11 2014.
  95. Effects of technical it capabilities on organizational agility. Emerald Publishing Limited, 120(5):941–961, 04 2020.
  96. Ahm Shamsuzzoha. Modular product architecture for productivity enhancement. Emerald Publishing Limited, 17(1):21–41, 02 2011.
  97. Modularity and innovation in complex systems. RELX Group (Netherlands), 01 2003.
  98. Ethics training and businesspersons? perceptions of organizational ethics. Springer Science+Business Media, 52(4):391–400, 07 2004.
  99. Dynamic capabilities through continuous improvement infrastructure. Wiley, 27(6):444–461, 03 2009.
  100. A comprehensive, valid, and reliable tool to assess the degree of responsibility of digital health solutions that operate with or without artificial intelligence: 3-phase mixed methods study. JMIR Publications, 25:e48496–e48496, 08 2023.
  101. Sandra Waddock. Creating corporate accountability: Foundational principles to make corporate citizenship real, 04 2004.
  102. Responsible data governance of neuroscience big data, 04 2019.
  103. Expanding explainability: Towards social transparency in ai systems. 05 2021.
  104. Towards high-value datasets determination for data-driven development: A systematic literature review, 01 2023.
  105. Environmental ethics, environmental performance, and competitive advantage: Role of environmental training. Elsevier BV, 146:203–211, 09 2019.
  106. Bias and unfairness in machine learning models: A systematic review on datasets, tools, fairness metrics, and identification and mitigation methods. Multidisciplinary Digital Publishing Institute, 7(1):15–15, 01 2023.
  107. Authentic learning environments in higher education, 01 2006.
  108. Jim X. Chen. Learning abstract concepts through interactive playing. Elsevier BV, 30(1):10–19, 02 2006.
  109. A closer look at cross-functional r&d cooperation for innovativeness: Innovation-oriented leadership and human resource practices as driving forces. Wiley-Blackwell, 31(5):924–938, 12 2013.
  110. The right to data portability in the gdpr: Towards user-centric interoperability of digital services. Elsevier BV, 34(2):193–203, 04 2018.
  111. Misti Ault Anderson. Pedagogical support for responsible conduct of research training. Wiley-Blackwell, 46(1):18–25, 01 2016.
  112. Building professional development opportunities in data services for academic librarians. 43(1):65 – 80, 12 2016.
  113. Using evaluation to guide successful development of an online training course for healthcare professionals. Lippincott Williams & Wilkins, 14(1):66–75, 01 2008.
  114. Eric Trist. Referent organizations and the development of inter-organizational domains. Human relations, 36(3):269–284, 1983.
  115. Cross-boundary teaming for innovation: Integrating research on teams and knowledge in organizations. Human Resource Management Review, 28(4):347–360, 2018.
  116. An exploratory study into it governance implementations and its impact on business/it alignment. Information Systems Management, 26(2):123–137, 2009.
  117. Workplace changes and workplace learning: advantages of an educational micro perspective. International Journal of Lifelong Education, 26(6):675–688, 2007.
  118. Abram Anders. Team communication platforms and emergent social collaboration practices. International Journal of Business Communication, 53(2):224–261, 2016.
  119. Career-related antecedents and outcomes of job rotation. Academy of Management Journal, 37(6):1518–1542, 1994.
  120. XAI: Explainable artificial intelligence. Science Robotics, 4(37), 2019.
  121. Retention strategies in longitudinal cohort studies: a systematic review and meta-analysis. BMC Medical Research Methodology, 18, 2018.
  122. Ethics review of big data research: What should stay and what should be reformed? BMC Medical Ethics, 22, 2021.
  123. Vikas Jaiman and V. Urovi. A consent model for blockchain-based health data sharing platforms. IEEE Access, 8:143734–143745, 2020.
  124. On secure data deletion. IEEE Secur. Priv., 12:37–44, 2014.
  125. Organizational effectiveness: A behavioral view. SAGE Publishing, 22(3):538–538, 09 1977.
  126. Learning before leaping: integration of an adaptive study design process prior to initiation of betterbirth, a large-scale randomized controlled trial in uttar pradesh, india. BioMed Central, 10(1), 08 2015.
  127. This is what i need a clinical feedback system to do for me: A qualitative inquiry into therapists’ and patients’ perspectives. Routledge, 28(2):250–263, 05 2016.
  128. The safe and effective use of shared data underpinned by stakeholder engagement and evaluation practice. Georg Thieme Verlag, 27(01):025–028, 04 2018.
  129. Four principles of explainable artificial intelligence, 08 2020.
  130. Pedro Elias Lucas Ramos Meireles and Heiko Hornung. Human values in human-data interaction. 10 2016.
  131. Mike Hinchey. Tc seeks to manage complexity in computing. IEEE Computer Society, 30(4):91–92, 04 1997.
  132. Six transformations to achieve the sustainable development goals. Nature Portfolio, 2(9):805–814, 08 2019.
  133. Harnessing the data revolution to achieve the sustainable development goals, 05 2017.
  134. Eu general data protection regulation: Changes and implications for personal data collecting companies. Computer Law & Security Review, 34(1):134–153, 2018.
  135. Christopher Kuner. Transborder data flows and data privacy law. Oxford University Press, 2013.
  136. Marijn Janssen and Haiko van der Voort. Adaptive governance: Towards a stable, accountable and responsive government. Government Information Quarterly, 33(1):1–5, 2016.

Summary

  • The paper presents a robust framework that integrates ethical principles into data management practices, emphasizing transparency, user control, and accountability.
  • The paper outlines an implementation roadmap featuring audits, policy development, technology integration, and cultural training to embed HDI principles.
  • Its framework empowers users and aligns with evolving regulations like GDPR, paving the way for future research in ethical data practices.

Overview of "Human-Data Interaction Framework: A Comprehensive Model for a Future Driven by Data and Humans"

The paper "Human-Data Interaction Framework: A Comprehensive Model for a Future Driven by Data and Humans," authored by Iván Durango, José A. Gallud, and Víctor M.R. Penichet, presents a detailed framework focused on integrating human-centric principles within data systems. This Human-Data Interaction (HDI) framework is designed to address growing concerns about data governance and the ethical use of data in increasingly digital contexts. The framework aims to empower individuals while ensuring that data-driven technologies remain transparent, fair, and aligned with societal values.

The HDI framework proposed in the paper emphasizes the seamless incorporation of ethical principles into data management practices, stressing the need for organizations to audit their current data handling practices, align them with HDI principles, and implement changes through iterative refinement. The paper provides a structured HDI framework schema, offering organizations a roadmap to embed principles such as transparency, user control, data ethics, and accountability into their operations.

Key Components of the HDI Framework

The authors outline several components of the HDI framework:

  • Foundational Principles and Structural Components: The framework is based on several ethical principles, including agency, legibility, and negotiability. These principles aim to ensure that individuals have control over their data, operations are transparent, and data practices remain negotiable. The framework includes a roadmap for aligning organizational practices with these principles.
  • Implementation Roadmap: The paper details a strategic approach for realizing HDI principles within organizations. This phased implementation process starts with an audit and planning phase and progresses to policy development, technology integration, and ongoing adaptation.
  • Training and Cultural Integration: The authors highlight the importance of integrating HDI principles within organizational culture through training programs and cross-departmental collaboration. This ensures that ethical practices become a core part of organizational ethos.
  • Data Lifecycle Management: The framework details the management of data across its lifecycle, emphasizing ethical data handling from collection to disposal, including considerations for privacy, security, and user consent.

Implications of the HDI Framework

The implications of the HDI framework extend beyond data governance and include significant societal and organizational impacts:

  • Enhanced Trust and Transparency: By prioritizing transparency, accountability, and user empowerment, organizations can build greater trust with users and stakeholders. This trust is essential for the sustainable development of data-driven systems.
  • User Empowerment and Ethical Responsiveness: The HDI framework seeks to rebalance the power between users and data systems, ensuring users have meaningful control over their personal information, which is crucial for maintaining individual privacy and autonomy.
  • Regulatory Alignment and Compliance: As data protection laws and societal expectations continue to evolve, adopting the HDI framework can help organizations remain compliant with regulations like GDPR while also addressing broader ethical concerns.
  • Future Directions for Research and Practice: The paper identifies potential avenues for future exploration, including adaptive ethical frameworks, advanced privacy technologies, and cross-cultural HDI implementations.

The HDI framework is set to become an essential tool for organizations aiming to integrate ethical considerations into data management processes effectively. It provides a comprehensive approach to align technological innovation with fundamental human values, ensuring that data systems are used responsibly and sustainably.

Conclusion

The paper makes significant contributions to the field of human-computer interaction by offering a robust HDI framework that prioritizes ethical data practices. As the digital landscape continues to evolve, the HDI framework provides a vital roadmap for organizations aiming to navigate the complexities of data ethics, privacy, and user engagement. By systematically embedding HDI principles throughout their operations, organizations can promote a more equitable and human-centric digital future.

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