Recent machine-learning (ML)-based advances in single-cell data science have enabled the stratification of human tissue donors at single-cell resolution, promising to provide valuable diagnostic and prognostic insights. However, such insights are susceptible to biases. Here we discuss various biases that emerge along the pipeline of ML-based single-cell analysis, ranging from societal biases affecting whose samples are collected, to clinical and cohort biases that influence the generalizability of single-cell datasets, biases stemming from single-cell sequencing, ML biases specific to (weakly supervised or unsupervised) ML models trained on human single-cell samples and biases during the interpretation of results from ML models. We end by providing methods for single-cell data scientists to assess and mitigate biases, and call for efforts to address the root causes of biases.

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress' prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence?

The emergence of ethical concerns surrounding artificial intelligence (AI) has led to an explosion of high-level ethical principles being published by a wide range of public and private organizations. However, there is a need to consider how AI developers can be practically assisted to anticipate, identify and address ethical issues regarding AI technologies. This is particularly important in the development of AI intended for healthcare settings, where applications will often interact directly with patients in various states of vulnerability. In this paper, we propose that an 'embedded ethics' approach, in which ethicists and developers together address ethical issues via an iterative and continuous process from the outset of development, could be an effective means of integrating robust ethical considerations into the practical development of medical AI.

PurposeTo facilitate ethically acceptable and practically successful health care-embedded biobanking, the attitudes and understanding of patients and their motivation to participate need to be explored.MethodsA questionnaire study was conducted among 760 outpatients of a northern German university hospital to assess their awareness of, and motivation for giving broad consent to health care-embedded biobanking, also addressing the issue of feedback on individual-level research findings.ResultsThe overall willingness to give broad consent was high (86.9%) in our study, even though the subjective and objective understanding of patients was found to be only modest. Most participants who consented did so for prosocial reasons (altruism, solidarity, reciprocity, gratitude), whereas self-interest or worries about disadvantages played only a marginal role. Better objective understanding was associated with both a greater demand for feedback on individual research findings and a higher willingness to consent. Intermittent modification of the information material provided by the hospital led to significantly improved objective understanding.ConclusionPatient willingness to give broad consent to health care-embedded biobanking is high, with prosocial reasons driving decision making more than factual knowledge and approval or disapproval of specific consent elements. Future efforts to improve the information material used in health care-embedded biobanking should therefore emphasize prosocial reasons to consent.