Background During the COVID-19 pandemic we have seen various disastrous approaches regarding the use an implementation of measures and studies that performed on past and current health data. Accordingly, in this study, we criticize the lack of conceptual engineering to integrate ethical principles and values into the design and application of data-driven endeavours, with a particular examination at health data. We argue how we cannot strive for a robust ethical assessment without a critically causal framework

Methods Firstly, we analyse the translational gap and conceptual conflation of the terms: ‘bias and fairness’ and ‘transparency and explainability’, highlighting the misleading definitions and uses given to these concepts at a technical and ethical level. The main distinctions presented clarify the moral expectations given to these concepts and criticise the insufficient development of a conceptual analysis that targets them. We suggest that a fundamental part of a solution to reduce this translational gap implies embracing and applying a causal framework. Thus, we show why using causal models and, most importantly, a causal narrative cannot only help to prevent unethical effects, but it can also influence the efficiency of prediction models and their outcomes. Efficiency, in this case, transforms into an ethically laden concept that demands a causal narrative to align with ethical principles. Finally, we go through examples of COVID-19 decision-making that could have benefitted from a causal approach, highlighting the negative consequences of the NHS electronic health records platform and an OpenSAFELY publication in Nature that substantially suffers from the Table 2 Fallacy.

Discussion This analysis puts into discussion an interdisciplinary approach to increase critical ethical awareness about fairness. Providing robust and reliable frameworks to analyse and present data, especially in sensitive times like a world pandemic, requires trustworthy practices.

Conclusion Integrating ethics into data-driven solutions cannot be limited by the bias-aware fairness formalisations or the naïve applications of transparency and explainability. When it comes to the real-world application of models, their effects can harm individuals in society. Non-causal approaches tend to dissipate elements of agency and responsibility, which are fundamental to the development of what we can call ‘good science’.