While deep learning continues to be a powerful, transformative tool within life sciences research—spanning from basic biology and pre-clinical science to varied translational approaches and clinical studies—it is important to comment on ethical considerations. For instance, despite the fact that deep learning methods are helping to increase medical efficiency through improved diagnostic capability and risk assessment, certain biases may be inadvertently introduced into models related to patient age, race, and gender [@doi:10.1002/hast.977]. Deep learning practitioners may make use of datasets not representative of diverse populations and patient characteristics [@doi:10.1377/hlthaff.2014.0048], thereby contributing to these problems.
Therefore, it is important to think thoroughly and cautiously about deep learning applications and their potential impact to persons and society. This includes being mindful of possible harms, injustices, and other types of wrongdoing. At a minimum, practitioners must ensure that, wherever relevant, their life sciences projects are fully compliant with local research governance and approval policies, legal requirements, Institutional Review Board policies, and any other relevant bodies and standards. Moreover, we offer below three tangible, action-oriented recommendations to further empower and enrichen deep learning researchers.
First, just as it is a best practice to keep a project-specific or programming-related issue tracker detailing known bugs and other technical issues, practitioners should get into the habit of keeping an active ethics register. In this register, ethical concerns can be raised, recorded, and resolved, exactly as software problems are triaged and fixed. Because projects using deep learning usually rely on writing code, an ethics register can be a part of the issue tracker in the version control system for the software itself. By colocating the two, practitioners can operationalize the concept that ethical problems are "bugs" that must be resolved, not nice-to-haves that can be considered at some indefinite point in the future. For practitioners intending to distribute trained models, having an ethics register can also facilitate creating a model card [@doi:10.1145/3287560.3287596], a short document specifying the domains in which the model's performance was validated (for example, which model organism was used), how the performance was benchmarked, and known limitations and concerns. Second, to help foster a conscious ethics-oriented mindset, researchers should consider expanding journal clubs to include scholarly and popular articles detailing real-world ethics issues relevant to their scientific fields. This will help researchers to think more holistically and judiciously about their work and its implications. Third, we encourage individual- and team-level participation in professional societies [@https://asbh.org] and other types of organizations [@https://web.archive.org/web/20210112231619/https://ocean.sagepub.com/blog/10-organizations-leading-the-way-in-ethical-ai] and events [@https://www.aies-conference.com/] related to the domains of artificial intelligence and data ethics as well as bioethics. This will encourage a sense of community and intellectual engagement and will keep practitioners abreast of cutting-edge insights and emerging professional standards.
Furthermore, practitioners may encounter datasets that cannot be shared, such as ones for which there would be significant ethical or legal issues associated with their release [@doi:10.1371/journal.pcbi.1005399]. Examples of such data include classified or confidential data, biological data related to trade secrets, medical records, or other personally identifiable information [@doi:10.1038/s41576-020-0257-5]. While deep learning models can capture information-rich abstractions of multiple features of the data during the training process, these features may be more prone to leak the data that they were trained over if the model is shared or allowed to be queried with arbitrary inputs [@doi:10.1145/2810103.2813677; @arxiv:1610.05820]. In other words, the complex relationships learned about the input data can potentially be used to infer characteristics about the original dataset, which reflects the facts that the strengths that imbue deep learning with its great predictive capacity may also raise the level of risk surrounding data privacy. Therefore, while there is tremendous promise for deep learning techniques to extract information that cannot readily be captured by traditional methods [@arxiv:1509.09292], it is imperative not to share models trained on sensitive data. This also holds true for certain traditional machine learning methods that learn by capturing specific details of the full training data (for example, k-nearest neighbors models).
Techniques to train deep neural networks without sharing unencrypted access to data are being advanced through implementations of homomorphic encryption, which serves to enable equivalent prediction on data that is encrypted end-to-end [@doi:10.1371/journal.pcbi.1006454; @arxiv:1811.00778]. Privacy-preserving techniques [@arxiv:1811.04017], such as differential privacy [@doi:10.1145/2976749.2978318; @doi:10.1161/CIRCOUTCOMES.118.005122; @arxiv:1812.01484], can help to mitigate risks as long as the assumptions underlying these techniques are met. Other methods, such a distributed learning, in which small subsets of the data are processed independently in silos (possibly by different agents), are also promising but require careful investigation before applying to protected information [@doi:10.1016/j.compbiomed.2021.104716]. While these methods provide a path towards a future where trained models and their predictions can be shared, more software development and theoretical advances will be required to make these techniques easy to apply correctly in many settings. Unless using these techniques, researchers must not share the weights or provide arbitrary access to the predictions of models trained on sensitive data.