Data justice and data solidarity

Stimulating solidarity to improve knowledge on medications used during pregnancy : A contribution from the ConcePTION project

BACKGROUND

Pregnant people have been overlooked or excluded from clinical research, resulting in a lack of scientific knowledge on medication safety and efficacy during pregnancy. Thus far, both the opportunities to generate evidence-based knowledge beyond clinical trials and the role of pregnant people in changing their status quo have not been discussed. Some scholars have argued that for rare disease patients, for whom, just like pregnant people, a poor evidence base exists regarding treatments, solidarity has played an important role in addressing the evidence gap. This paper explores whether and how the enactment of solidarity among pregnant people can be stimulated to help address the poor evidence base on medications used during pregnancy.

METHOD

We use the concept of solidarity formulated by Prainsack and Buyx and enrich their concept by providing an account for stimulating the enactment of solidarity. Then we apply this account to the case of pregnant people who use medication.

RESULTS

Solidarity means enacted commitment on the part of an individual to assisting others with whom the person recognizes a similarity in a relevant respect. Although solidarity cannot be imposed, we argue that the empowerment of people is a crucial concept in understanding how solidarity can be stimulated. Empowerment in the context of pregnant people means creating awareness about their status quo, explaining how scientific research can help close the knowledge gap, and how pregnant people can themselves contribute. In particular, how pregnant people can contribute to the collection of health data to strengthen the evidence base for medications used during pregnancy.

CONCLUSIONS

We conclude that acting in solidarity can help change the status quo for pregnant people. Furthermore, we argue that the empowerment of pregnant people and other relevant stakeholders is a way to stimulate the enactment of solidarity. The process of empowerment starts by raising awareness about the lack of evidence on medications used during prengnacy and by explaining to pregnant people how they can contribute to changing the way knowledge is being generated by, for example, sharing data on the health effects of medications.

Reflections on Putting AI Ethics into Practice: How Three AI Ethics Approaches Conceptualize Theory and Practice

Critics currently argue that applied ethics approaches to artificial intelligence (AI) are too principles-oriented and entail a theory-practice gap. Several applied ethical approaches try to prevent such a gap by conceptually translating ethical theory into practice. In this article, we explore how the currently most prominent approaches of AI ethics translate ethics into practice. Therefore, we examine three approaches to applied AI ethics: the embedded ethics approach, the ethically aligned approach, and the Value Sensitive Design (VSD) approach. We analyze each of these three approaches by asking how they understand and conceptualize theory and practice. We outline the conceptual strengths as well as their shortcomings: an embedded ethics approach is context-oriented but risks being biased by it; ethically aligned approaches are principles-oriented but lack justification theories to deal with trade-offs between competing principles; and the interdisciplinary Value Sensitive Design approach is based on stakeholder values but needs linkage to political, legal, or social governance aspects. Against this background, we develop a meta-framework for applied AI ethics conceptions with three dimensions. Based on critical theory, we suggest these dimensions as starting points to critically reflect on the conceptualization of theory and practice. We claim, first, that the inclusion of the dimension of affects and emotions in the ethical decision-making process stimulates reflections on vulnerabilities, experiences of disregard, and marginalization already within the AI development process. Second, we derive from our analysis that considering the dimension of justifying normative background theories provides both standards and criteria as well as guidance for prioritizing or evaluating competing principles in cases of conflict. Third, we argue that reflecting the governance dimension in ethical decision-making is an important factor to reveal power structures as well as to realize ethical AI and its application because this dimension seeks to combine social, legal, technical, and political concerns. This meta-framework can thus serve as a reflective tool for understanding, mapping, and assessing the theory-practice conceptualizations within AI ethics approaches to address and overcome their blind spots.

Transparent human – (non-) transparent technology? The Janus-faced call for transparency in AI-based health care technologies

The use of Artificial Intelligence and Big Data in health care opens up new opportunities for the measurement of the human. Their application aims not only at gathering more and better data points but also at doing it less invasive. With this change in health care towards its extension to almost all areas of life and its increasing invisibility and opacity, new questions of transparency arise. While the complex human-machine interactions involved in deploying and using AI tend to become non-transparent, the use of these technologies makes the patient seemingly transparent. Papers on the ethical implementation of AI plead for transparency but neglect the factor of the “transparent patient” as intertwined with AI. Transparency in this regard appears to be Janus-faced: The precondition for receiving help - e.g., treatment advice regarding the own health - is to become transparent for the digitized health care system. That is, for instance, to donate data and become visible to the AI and its operators. The paper reflects on this entanglement of transparent patients and (non-) transparent technology. It argues that transparency regarding both AI and humans is not an ethical principle per se but an infraethical concept. Further, it is no sufficient basis for avoiding harm and human dignity violations. Rather, transparency must be enriched by intelligibility following Judith Butler’s use of the term. Intelligibility is understood as an epistemological presupposition for recognition and the ensuing humane treatment. Finally, the paper highlights ways to testify intelligibility in dealing with AI in health care ex ante, ex post, and continuously.