论文标题
迈向混合量化视觉分析的偏见未来
Toward a Bias-Aware Future for Mixed-Initiative Visual Analytics
论文作者
论文摘要
混合初始性视觉分析系统结合了建立良好的设计原则，以提高用户解决问题的能力。由于这些系统考虑是否要实现用户目标，因此许多当前系统依靠他们可以采取的固定计划，而不是识别，交流和解决发生时的偏见。我们认为，混合初始设计原则可以并且应该通过开发嵌入系统中的缓解技术来直接纳入消除认知偏差的缓解策略，以解决原位的认知偏见。我们确定了机器学习领域的领域专家，采用了视觉分析技术和系统，这些技术和系统结合了现有的混合初始性原理，并研究了它们支持偏见缓解策略的潜力。该考试认为这些专家将视觉分析带来了独特的观点，并位于现有的以用户为中心的系统中，这些系统使认知理论告知的设计原理示例性地使用了。然后，我们建议为领域专家提供明智的机会，以根据其现有对该领域的贡献来解决认知偏见。最后，我们为寻求采用视觉分析技术的设计师贡献了开放的问题和研究方向，这些技术将偏见的计划纳入了未来的系统。
Mixed-initiative visual analytics systems incorporate well-established design principles that improve users' abilities to solve problems. As these systems consider whether to take initiative towards achieving user goals, many current systems address the potential for cognitive bias in human initiatives statically, relying on fixed initiatives they can take instead of identifying, communicating and addressing the bias as it occurs. We argue that mixed-initiative design principles can and should incorporate cognitive bias mitigation strategies directly through development of mitigation techniques embedded in the system to address cognitive biases in situ. We identify domain experts in machine learning adopting visual analytics techniques and systems that incorporate existing mixed-initiative principles and examine their potential to support bias mitigation strategies. This examination considers the unique perspective these experts bring to visual analytics and is situated in existing user-centered systems that make exemplary use of design principles informed by cognitive theory. We then suggest informed opportunities for domain experts to take initiative toward addressing cognitive biases in light of their existing contributions to the field. Finally, we contribute open questions and research directions for designers seeking to adopt visual analytics techniques that incorporate bias-aware initiatives in future systems.
