多样性、公平与包容,以及在国防部内部署人工智能

Sara Darwish, Alison Bragaw-Butler, Paul Marcelli, Kaylee Gassner
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摘要

人工智能(AI)的应用在各行各业都有大幅增长。本文探讨了人工智能在美国国防部(DoD)中不断升级的应用,以及多样性、公平性和包容性(DEI)对整个国防部的军人和文职人员的影响。更具体地说,本文探讨了人工智能技术对个人、团队和国防部战备状态的 DEI 影响。国防部的人工智能应用涵盖各种战略和作战能力,但本文探讨的是两个关键领域:医疗保健和征兵。在医疗保健领域,人工智能为早期疾病检测、增强诊断能力和简化管理流程带来了希望。然而,由同质化训练数据产生的潜在偏见威胁着这些系统的准确性和可靠性,危害着军人的健康,削弱了对人工智能辅助医疗决策的信任,并有可能影响整个国防部。在征兵方面,虽然人工智能有望提高识别理想候选人的效率,但其部署可能会使偏见长期存在,特别是当所使用的训练数据不能代表所有人口统计数据时。尽管努力通过排除人口数据来设计 "无偏见 "的系统,但这种策略可能会无意中忽视边缘化群体所面临的独特挑战,从而进一步巩固现有的差距。随着国防部继续将人工智能整合到其行动中,本文的建议强调有必要持续进行发展指数评估,以确保人工智能成为一种资产而非负债。作者建议如下:1. 数据多样性与审查2.持续监控和校准3.利益相关者的参与4.在人工智能道德框架内采用 DEI 要求5.进一步研究
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diversity, Equity, and Inclusion, and the Deployment of Artificial Intelligence Within the Department of Defense
Artificial Intelligence (AI) adoption has seen substantial growth across industries. This paper explores the escalating use of AI within the United States Department of Defense (DoD) and the implications that diversity, equity, and inclusion (DEI) have on Service members and Civilians across the Department. More specifically, this paper explores the DEI considerations within AI technologies on individual, team, and Department readiness. The DoD's AI usage spans various strategic and operational capabilities, however this paper explores two critical domains: healthcare and recruitment. In healthcare, AI offers the promise of early disease detection, enhanced diagnostic capabilities, and streamlined administrative processes. However, potential biases stemming from homogenous training data threaten the accuracy and reliability of these systems, jeopardizing Service member health and eroding trust in AI-assisted medical decision-making and potentially the DoD at large. In recruitment, while AI promises efficiency in identifying ideal candidates, its deployment can perpetuate biases, especially when the training data used is not representative of all demographics. Despite efforts to design "unbiased" systems by excluding demographic data, such strategies may inadvertently overlook the unique challenges faced by marginalized communities, further entrenching existing disparities. Both case studies underscore the importance of considering DEI in the development and deployment of AI systems. As the DoD continues to integrate AI into its operations, this paper’s recommendations stress the necessity of continuous DEI assessment to ensure that AI serves as an asset rather than a liability. The authors recommend the following: 1. Data diversity & review 2. Continuous monitoring and calibration 3. Stakeholder engagement 4. Adoption of DEI requirements within Ethical AI Frameworks 5. Further research
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