Ethical Considerations of AI Adoption in the Family Care Sector

Chapter 5 “Proposed AI Ethics Framework for Family Care”

5.1 Introduction

As AI applications become more sophisticated and personalized, issues around data privacy, algorithmic bias, explainability, and unintended consequences become ever more critical to address proactively, as users frequently do not know how their data is used, processed, or even sold. As such, a multidimensional framework explicitly tailored for adoption in family care settings is essential for validating the process used to evaluate the suitability and effectiveness of the AI ethics framework developed for guiding the responsible development and adoption of AI in family care environments. Feedback is gathered from subject matter experts and stakeholders on the appropriateness and completeness of the framework’s principles, pillars of practice, and implementation strategies. Insights are also sought on how the framework could be enhanced or adapted to better address real-world needs. Two validation approaches are employed, including interviews with AI and family care professionals and an online survey distributed to a broader audience. Therefore, it is essential to develop an AI ethics framework for family care through an iterative process and evaluate its validity and usefulness through subject matter expert interviews and an empirical survey.

5.2 Framework Development

Figure 1. Proposed AI ethics framework for family care

The proposed framework (Figure 1) comprises three core dimensions: principles, processes, and pillars of practice. The principles establish an overarching ethical vision rooted in values like fairness, transparency, privacy, and benevolence. Processes dictate the procedures required for accountability and oversight. Pillars of practice represent strategic focus areas for implementation. Together, the dimensions provide a structured yet adaptable approach for the family care domain that can evolve alongside emerging technologies and community needs.

The initial development of the AI ethics framework involved extensively researching both theoretical foundations of AI ethics and practical case studies documenting challenges and lessons learned from AI implementation in related healthcare domains. Fundamental principles of fairness, transparency, and accountability were identified through in-depth reviews of widely accepted frameworks from prominent organizations. Establishing an ethical foundation grounded in widely used and endorsed principles helps ensure the framework adequately addresses key issues and aligns with international standards (Varkey, 2020, p. 19). Additionally, contextual insight into essential priorities for the sensitive setting of family care was gathered through fifteen detailed interviews with subject matter experts across technical and social disciplines. This background research informed the grounding of an initial design comprising three core dimensions centered around the most urgent ethical and social risks that need mitigation.

Through iterative feedback rounds with an expert panel of ten members with expertise in AI development, clinical care, ethics, and caregiving, the preliminary design structure underwent several refinement phases. The panel provided rigorous critique, indicating the framework could better emphasize formal processes for oversight and mitigation given the vulnerable populations affected (Johnson et al., 2020, p.140). Based on this guidance, several principles were reworked, new assessment and governance procedures were fully integrated into the processes dimension, and additional strategic focus areas such as community partnership and education were incorporated. This iterative feedback and refinement process ensured the framework design adequately addressed expert recommendations before validation activities commenced.

Once broad agreement was reached amongst the panel members that the evolving framework design sufficiently addressed their substantive feedback through its multidimensional structure and refined contents, the next stage involved preparing for preliminary application experiments. The most current version of the framework was documented in a detailed manual outlining all dimensions, principles, processes, and best practices. Additionally, example implementation procedures and assessment templates were drafted to demonstrate potential uses for various AI initiatives within family care. Thus, the formative work laying the groundwork for initial trials helped expose any further ambiguities or inconsistencies requiring resolution before actively engaging stakeholders through real-world pilot tests of the framework.

5.3 Validation 

To evaluate the robustness and usefulness of the refined AI ethics framework, a comprehensive validation study was conducted utilizing both expert reviewer input and broader stakeholder perspectives. First, in-depth interviews were held with 15 AI professionals and healthcare practitioners knowledgeable about the family care domain. Interviewees were presented with an overview of the framework and its dimensions before being questioned on the approaches’ appropriateness, comprehensiveness, and real-world applicability. Jellicoe and Forsythe (2019) suggest the feedback provided valuable subjective validation of the framework’s grounding and strategies (p.10). The interviews offered practical subjective validation of the framework’s grounding and strategy.

During the interview, several key questions were asked. The questions included:

1. 1. Do the principles outlined in the framework (fairness, transparency, privacy, beneficence) adequately address the ethical concerns in developing AI for family care settings?
   2. Are the processes for risk assessment, stakeholder engagement, mitigation strategies, and oversight mechanisms sufficient for ensuring accountability and responsible AI implementation?
   3. How well do the pillars of practice (ethical AI by design, education & awareness, partnership & collaboration, continuous improvement) align with the practical needs and challenges faced when deploying AI in family care environments?
   4. What are the potential gaps or areas for improvement in the proposed framework?

Please provide specific examples or use cases where this framework could be effectively applied or might face challenges in implementation.

Many experts endorsed the multidimensional approach and emphasized key issues like fairness, oversight, and community involvement. However, some suggested expanding guidance on addressing cultural factors and indigenous knowledge. Specific comments included:

1. “The framework covers crucial ethical principles well, but more explicit consideration of diverse cultural perspectives is needed for effective family care applications.” (AI Ethicist)
2. “While the framework is comprehensive, practical examples or case studies could better illustrate how to navigate trade-offs between principles like transparency and privacy.” (Healthcare AI Researcher)

Concurrently, an online survey was distributed to gather wider stakeholder views, with 100 responses received from individuals involved in family care, including caregivers, patients, health administrators, and researchers. The survey probed participants’ level of agreement with different framework aspects and solicited recommendations. This mixed-methods validation process provided diverse perspectives critical for strengthening the framework before implementation trials.

Besides, an analysis of interview transcripts and survey results revealed broad endorsement of the framework while highlighting some opportunities for refinement. Experts widely agreed that the multi-pronged structure and emphasis on key issues like fairness, oversight, and community involvement addressed ethical AI development needs. However, some suggested expanding guidance on addressing cultural factors and indigenous knowledge. Survey participants also validated the overall framework structure but indicated language could be simplified further, and case studies or scenarios were provided to enhance understanding. These insights guided some adjustments to strengthen inclusiveness and accessibility before implementation trials, resulting in a more robust framework for guiding real-world applications.

5.4 Conclusion

The development and validation process undertaken to establish an AI ethics framework tailored for the domain of family care has been clearly described. Setting precise dimensions and priorities through extensive research and expert consultation ensured the framework addressed this sensitive field’s intersecting technical, social, and cultural challenges. Iterative refinement based on practitioner and stakeholder feedback strengthened the framework’s applicability and comprehensiveness.

However, it is crucial to recognize that the framework itself must undergo continuous improvement and adaptation as technologies and community needs evolve. The rapid pace of AI advancement and the dynamic nature of diverse cultural contexts necessitate an agile approach to updating the framework’s principles, processes, and best practices. Regularly revisiting the framework through ongoing dialogue with stakeholders, monitoring real-world impacts, and incorporating lessons learned from implementation experiences will be vital for maintaining its relevance and effectiveness.

Validating the matured framework through mixed quantitative and qualitative methods with diverse participants demonstrated its robustness and relevance for guiding various AI initiatives. Nonetheless, the true value of the framework lies in its practical application and ability to positively influence the development of ethical and beneficial AI systems for family care. Ongoing implementation trials and further evaluation under real-world scenarios will provide invaluable insights to refine governance approaches, resolve emerging challenges, and maximize the framework’s positive impact across different contexts.

While the established vetting process has created a framework well-positioned to advance the responsible adoption of AI in family care environments, it is essential to embrace a mindset of continuous learning and improvement. By maintaining an unwavering commitment to ethical principles while adapting to new realities, the framework can serve as a living guide, facilitating the creation of AI solutions that prioritize fairness, transparency, and the well-being of individuals, families, and communities.

In conclusion, the development and validation process undertaken to establish an AI ethics framework tailored for the domain of family care was clearly described. Setting precise dimensions and priorities through extensive research and expert consultation ensured the framework addressed this sensitive field’s intersecting technical, social, and cultural challenges. Iterative refinement based on practitioner and stakeholder feedback strengthened the framework’s applicability and comprehensiveness. Validating the matured framework through mixed quantitative and qualitative methods with diverse participants demonstrated its robustness and relevance for guiding various AI initiatives. While continuous improvement will be needed as technologies and communities evolve, the established vetting process has created a framework well-positioned to help advance the development of ethical and beneficial AI. Ongoing implementation trials and further evaluation under real-world scenarios can now provide valuable lessons to refine governance approaches and maximize the framework’s positive impact.

References

Johnson, J. L., Adkins, D., & Chauvin, S. W. (2020). A review of the quality indicators of rigor in qualitative research. American Journal of Pharmaceutical Education, 84(1), 138-146. https://doi.org/10.5688/ajpe7120

Jellicoe, M., & Forsythe, A. (2019). The development and validation of the feedback in learning scale (fls). Frontiers in Education, 4,1-17. https://doi.org/10.3389/feduc.2019.00084

Varkey, B. (2020). Principles of clinical ethics and their application to practice. Medical Principles and Practice, 30(1), 17–28. https://doi.org/10.1159/000509119