Ethics and Values in Design: A Structured Review and Theoretical Critique

Opportunities for incorporating intersectionality into biomedical informatics

Many approaches in biomedical informatics (BMI) rely on the ability to define, gather, and manipulate biomedical data to support health through a cyclical research-practice lifecycle. Researchers within this field are often fortunate to work closely with healthcare and public health systems to influence data generation and capture and have access to a vast amount of biomedical data. Many informaticists also have the expertise to engage with stakeholders, develop new methods and applications, and influence policy. However, research and policy that explicitly seeks to address the systemic drivers of health would more effectively support health. Intersectionality is a theoretical framework that can facilitate such research. It holds that individual human experiences reflect larger socio-structural level systems of privilege and oppression, and cannot be truly understood if these systems are examined in isolation. Intersectionality explicitly accounts for the interrelated nature of systems of privilege and oppression, providing a lens through which to examine and challenge inequities. In this paper, we propose intersectionality as an intervention into how we conduct BMI research. We begin by discussing intersectionality's history and core principles as they apply to BMI. We then elaborate on the potential for intersectionality to stimulate BMI research. Specifically, we posit that our efforts in BMI to improve health should address intersectionality's five key considerations: (1) systems of privilege and oppression that shape health; (2) the interrelated nature of upstream health drivers; (3) the nuances of health outcomes within groups; (4) the problematic and power-laden nature of categories that we assign to people in research and in society; and (5) research to inform and support social change.

The Political Economy of Digital Health Equity: Structural Analysis

Digital technologies have produced many innovations in care delivery and enabled continuity of care for many people when in-person care was impossible. However, a growing body of research suggests that digital health can also exacerbate health inequities for those excluded from its benefits for reasons of cost, digital literacy, and structural discrimination related to characteristics such as age, race, ethnicity, and socioeconomic status. In this paper, we draw on a political economy perspective to examine structural barriers to progress in advancing digital health equity at the policy level. Considering the incentive structures and investments of powerful actors in the field, we outline how characteristics of neoliberal capitalism in Western contexts produce and sustain digital health inequities by describing 6 structural challenges to the effort to promote health equity through digital health, as follows: (1) the revenue-first incentives of technology corporations, (2) the influence of venture capital, (3) inequitable access to the internet and digital devices, (4) underinvestment in digital health literacy, (5) uncertainty about future reimbursement of digital health, and (6) justified mistrust of digital health. Building on these important challenges, we propose future immediate and long-term directions for work to support meaningful change for digital health equity.

The participatory value-sensitive design (VSD) of a mHealth app targeting citizens with dementia in a Danish municipality

The Sammen Om Demens (together for dementia), a citizen science project developing and implementing an AI-based smartphone app targeting citizens with dementia, is presented as an illustrative case of ethical, applied AI entailing interdisciplinary collaborations and inclusive and participative scientific practices engaging citizens, end users, and potential recipients of technological-digital innovation. Accordingly, the participatory Value-Sensitive Design of the smartphone app (a tracking device) is explored and explained across all of its phases (conceptual, empirical, and technical). Namely, from value construction and value elicitation to the delivery, after various iterations engaging both expert and non-expert stakeholders, of an embodied prototype built on and tailored to their values. The emphasis is on how moral dilemmas and value conflicts, often resulting from diverse people's needs or vested interests, have been resolved in practice to deliver a unique digital artifact with moral imagination that fulfills vital ethical-social desiderata without undermining technical efficiency. The result is an AI-based tool for the management and care of dementia that can be considered more ethical and democratic, since it meaningfully reflects diverse citizens' values and expectations on the app. In the conclusion, we suggest that the co-design methodology outlined in this study is suitable to generate more explainable and trustworthy AI, and also, it helps to advance towards technical-digital innovation holding a human face.

The Use of Artificial Intelligence in Clinical Care: A Values-Based Guide for Shared Decision Making

Clinical applications of artificial intelligence (AI) in healthcare, including in the field of oncology, have the potential to advance diagnosis and treatment. The literature suggests that patient values should be considered in decision making when using AI in clinical care; however, there is a lack of practical guidance for clinicians on how to approach these conversations and incorporate patient values into clinical decision making. We provide a practical, values-based guide for clinicians to assist in critical reflection and the incorporation of patient values into shared decision making when deciding to use AI in clinical care. Values that are relevant to patients, identified in the literature, include trust, privacy and confidentiality, non-maleficence, safety, accountability, beneficence, autonomy, transparency, compassion, equity, justice, and fairness. The guide offers questions for clinicians to consider when adopting the potential use of AI in their practice; explores illness understanding between the patient and clinician; encourages open dialogue of patient values; reviews all clinically appropriate options; and makes a shared decision of what option best meets the patient's values. The guide can be used for diverse clinical applications of AI.