02672nas a2200253 4500000000100000008004100001260004400042653004100086653003400127653002100161653001400182653003500196653003500231653001500266100001600281700001500297700001100312700001300323245013500336856009300471300000900564520182000573022002502393 2023 d bSpringer Science and Business Media LLC10aGeneral Earth and Planetary Sciences10aGeneral Environmental Science10aEthics by design10aEthics AI10aAI-enabled mobile applications10aSoftware development lifecycle10aHealthcare1 aAmugongo LM1 aKriebitz A1 aBoch A1 aLütge C00aOperationalising AI ethics through the agile software development lifecycle: a case study of AI-enabled mobile health applications uhttps://link.springer.com/content/pdf/10.1007/s43681-023-00331-3.pdf?pdf=button%20sticky a1-183 a
Although numerous ethical principles and guidelines have been proposed to guide the development of artificial intelligence (AI) systems, it has proven difficult to translate these principles into actionable practices beyond mere adherence to ethical ideas. This is particularly challenging in the context of AI systems for healthcare, which requires balancing the potential benefits of the solution against the risks to patients and the wider community, including minorities and underserved populations. To address this challenge, we propose a shift from one-size-fits-all ethical principles to contextualized case-based ethical frameworks. This study uses an AI-enabled mHealth application as a case study. Our framework is built on existing ethical guidelines and principles, including the AI4People framework, the EU High-Level Expert Group on trustworthy AI, and wider human rights considerations. Additionally, we incorporate relational perspectives to address human value concerns and moral tensions between individual rights and public health. Our approach is based on ”ethics by design,” where ethical principles are integrated throughout the entire AI development pipeline, ensuring that ethical considerations are not an afterthought but implemented from the beginning. For our case study, we identified 7 ethical principles: fairness, agility, precision, safeguarding humanity, respect for others, trust and accountability, and robustness and reproducibility. We believe that the best way to mitigate and address ethical consequences is by implementing ethical principles in the software development processes that developers commonly use. Finally, we provide examples of how our case-based framework can be applied in practice, using examples of AI-driven mobile applications in healthcare.
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