来自神经分离的GAI"权力用户"的自动民族学见解
Kate Glazko1, JunHyeok Cha2, Aaleyah Lewis3
1University of Washington, Seattle, Washington, USA.
Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference
|November 27, 2025
概括
生成性人工智能 (GAI) 为神经分化个体提供了新的可访问性途径,有助于导航社会期望. 然而,在平衡真实表达与符合完全GAI潜力的过程中,仍然存在挑战.
科学领域:
- 人与计算机的交互
- 人工智能的人工智能
- 神经多样性研究 神经多样性研究
背景情况:
- 生成型人工智能 (GAI) 越来越多地融入日常生活,人们越来越认识到其可访问性潜力.
- 现有的可访问性技术往往无法满足神经分化个体的需求,这些个体开发出独特的应对机制.
- 了解神经分离的"权力用户"如何与GAI互动,对于最大限度地发挥其益处至关重要.
研究的目的:
- 探索神经分离个体如何利用GAI来导航规范性环境和期望.
- 确定神经分离用户在与GAI互动时所面临的挑战和机遇.
- 检查边缘化和交叉身份对神经分化群体中GAI使用的影响.
主要方法:
- 自动民族学作为主要研究方法被采用.
- 采用基于GAI的日记和采访来收集数据,以保护隐私.
- 定性分析侧重于用户体验和与GAI的互动.
主要成果:
- GAI可以帮助神经分离的用户完成诸如代码交换,情绪调节和信息获取等任务.
- 在可能限制自主权的系统中,GAI帮助神经分离的用户恢复主动性.
- 使用GAI带来了挑战,包括平衡自我表达与社会规范和其他潜在风险.
结论:
- 作为神经分离个体的可访问性工具,GAI具有显著的潜力.
- 解决与真实表达和社会压力相关的挑战是释放GAI充分可访问性潜力的关键.
- 需要进行进一步的研究,以了解和减轻神经分化社区公平采用GAI的障碍.
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