让协同设计更负责任:基于人工智能的痴呆症护理决策支持系统开发案例研究》。

IF 2.6 Q2 HEALTH CARE SCIENCES & SERVICES JMIR Human Factors Pub Date : 2024-07-31 DOI:10.2196/55961
Dirk R M Lukkien, Sima Ipakchian Askari, Nathalie E Stolwijk, Bob M Hofstede, Henk Herman Nap, Wouter P C Boon, Alexander Peine, Ellen H M Moors, Mirella M N Minkman
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引用次数: 0

摘要

背景:人工智能(AI)等新兴技术需要对潜在的社会和伦理影响进行早期评估,以提高其可接受性、可取性和可持续性。本文探讨并比较了其中两种评估方法:源于技术研究的负责任创新(RI)框架和源于设计研究的共同设计方法。负责任创新框架通过预测、包容、反思和响应来指导早期阶段的技术评估,而共同设计则是支持照顾体弱老年人的技术开发中普遍接受的方法。然而,人们对共同设计如何促进预期影响的了解还很有限:本文通过实证研究,探讨了在为痴呆症护理人员开发基于人工智能的决策支持系统(DSS)的共同设计过程中,如何通过对影响的明确预期来进行补充:本案例研究调查了一个国际合作项目,其重点是共同设计、开发、测试和商业化一个旨在为痴呆症患者的正式护理人员提供可操作信息的决策支持系统。在共同设计过程的同时,还开展了一项成果转化探索,其中包括研究项目成员对使用 DSS 的积极和消极影响的看法,以及解决这些影响的策略。对共同设计过程和 RI 探索的结果进行了分析和比较。此外,还对项目成员进行了回顾性访谈,以反思共同设计过程和 RI 探索:结果:我们的研究结果表明,在参与探讨 DSS 需求时,共同设计参与者自然而然地提出了负责任设计和部署的各种影响和条件:保护隐私、防止认知超载、提供透明度、赋予护理人员控制权、保障准确性以及培训用户。然而,在将共同设计的结果与 RI 探索的见解进行比较时,我们发现了共同设计结果的局限性,例如,在影响和解决影响的策略的具体化、相互关联性和上下文依赖性方面:本案例研究表明,共同设计过程如果只关注创新机会,而不平衡对积极和消极影响的关注,可能会造成与社会和道德影响以及如何解决这些影响相关的知识缺口。为了追求负责任的结果,共同设计的促进者可以扩大范围,重新考虑如何具体实施以过程为导向的预期和包容的 RI 原则。
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Making Co-Design More Responsible: Case Study on the Development of an AI-Based Decision Support System in Dementia Care.

Background: Emerging technologies such as artificial intelligence (AI) require an early-stage assessment of potential societal and ethical implications to increase their acceptability, desirability, and sustainability. This paper explores and compares 2 of these assessment approaches: the responsible innovation (RI) framework originating from technology studies and the co-design approach originating from design studies. While the RI framework has been introduced to guide early-stage technology assessment through anticipation, inclusion, reflexivity, and responsiveness, co-design is a commonly accepted approach in the development of technologies to support the care for older adults with frailty. However, there is limited understanding about how co-design contributes to the anticipation of implications.

Objective: This paper empirically explores how the co-design process of an AI-based decision support system (DSS) for dementia caregivers is complemented by explicit anticipation of implications.

Methods: This case study investigated an international collaborative project that focused on the co-design, development, testing, and commercialization of a DSS that is intended to provide actionable information to formal caregivers of people with dementia. In parallel to the co-design process, an RI exploration took place, which involved examining project members' viewpoints on both positive and negative implications of using the DSS, along with strategies to address these implications. Results from the co-design process and RI exploration were analyzed and compared. In addition, retrospective interviews were held with project members to reflect on the co-design process and RI exploration.

Results: Our results indicate that, when involved in exploring requirements for the DSS, co-design participants naturally raised various implications and conditions for responsible design and deployment: protecting privacy, preventing cognitive overload, providing transparency, empowering caregivers to be in control, safeguarding accuracy, and training users. However, when comparing the co-design results with insights from the RI exploration, we found limitations to the co-design results, for instance, regarding the specification, interrelatedness, and context dependency of implications and strategies to address implications.

Conclusions: This case study shows that a co-design process that focuses on opportunities for innovation rather than balancing attention for both positive and negative implications may result in knowledge gaps related to social and ethical implications and how they can be addressed. In the pursuit of responsible outcomes, co-design facilitators could broaden their scope and reconsider the specific implementation of the process-oriented RI principles of anticipation and inclusion.

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来源期刊
JMIR Human Factors
JMIR Human Factors Medicine-Health Informatics
CiteScore
3.40
自引率
3.70%
发文量
123
审稿时长
12 weeks
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