Obafemi-Ajayi Tayo, Perkins Andy, Nanduri Bindu, Wunsch Ii Donald C, Foster James A, Peckham Joan
Engineering Program, Missouri State University, Springfield, MO USA.
Department of Computer Science and Engineering, Mississippi State University, Starkville, MS USA.
AI Ethics. 2022;2(4):635-643. doi: 10.1007/s43681-021-00118-4. Epub 2021 Nov 29.
Today Artificial Intelligence (AI) supports difficult decisions about policy, health, and our personal lives. The AI algorithms we develop and deploy to make sense of information, are informed by data, and based on models that capture and use pertinent details of the population or phenomenon being analyzed. For any application area, more importantly in precision medicine which directly impacts human lives, the data upon which algorithms are run must be procured, cleaned, and organized well to assure reliable and interpretable results, and to assure that they do not perpetrate or amplify human prejudices. This must be done without violating basic assumptions of the algorithms in use. Algorithmic results need to be clearly communicated to stakeholders and domain experts to enable sound conclusions. Our position is that AI holds great promise for supporting precision medicine, but we need to move forward with great care, with consideration for possible ethical implications. We make the case that a no-boundary or convergent approach is essential to support sound and ethical decisions. No-boundary thinking supports problem definition and solving with teams of experts possessing diverse perspectives. When dealing with AI and the data needed to use AI, there is a spectrum of activities that needs the attention of a no-boundary team. This is necessary if we are to draw viable conclusions and develop actions and policies based on the AI, the data, and the scientific foundations of the domain in question.
如今，人工智能（AI）为政策、健康及个人生活等方面的艰难决策提供支持。我们开发和部署的用于理解信息的AI算法，以数据为依据，并基于捕捉和使用所分析人群或现象相关细节的模型。对于任何应用领域，更重要的是在直接影响人类生活的精准医学中，运行算法所依据的数据必须经过妥善获取、清理和整理，以确保结果可靠且可解释，并确保它们不会延续或放大人类偏见。这必须在不违反所用算法基本假设的情况下完成。算法结果需要清晰地传达给利益相关者和领域专家，以便得出合理结论。我们的立场是，AI在支持精准医学方面具有巨大潜力，但我们需要谨慎前行，考虑可能的伦理影响。我们认为，无边界或融合的方法对于支持合理且符合伦理的决策至关重要。无边界思维有助于由具有不同视角的专家团队来定义和解决问题。在处理AI及使用AI所需的数据时，一系列活动需要无边界团队的关注。如果我们要基于AI、数据及相关领域的科学基础得出可行的结论并制定行动和政策，这是必要的。
