From ”Explainable AI” to ”Graspable AI”
From ”Explainable AI” to ”Graspable AI”
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从“可解释的人工智能”到“可掌握的人工智能”
DOI：
10.1145/3430524.3442704
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Wiberg, Mikael
中科院分区：
文献类型：
--
作者：
Ghajargar, Maliheh;Bardzell, Jeffrey;Renner, Alison Smith;Krogh, Peter Gall;Höök, Kristina;Cuartielles, David;Boer, Laurens;Wiberg, Mikael
自从人工智能(AI)和机器学习(ML)问世以来，研究人员一直在问智能计算系统如何与他们的用户和环境交互并与之相关，这导致了围绕人工智能系统偏见、ML黑盒、用户信任、用户对系统控制的感知以及系统的透明度等问题的辩论。所有这些问题都与人类如何通过使用不同交互模式的界面与AI或ML系统交互有关。以前的研究从不同的角度解决这些问题，从通过伦理学和科学技术研究(STS)的角度来理解和框定问题，到找到有效的技术解决方案。但几乎所有这些努力都有一个共同的假设，即如果系统能够解释他们预测的方式和原因，人们就会对控制有更好的感知，因此会更信任这样的系统，甚至可以纠正自己的缺点。这一研究领域被称为可解释人工智能(XAI)。在本工作室中，我们总结了这一领域以前的工作；然而，我们重点使用有形和具体化交互(TEI)作为理解ML的交互方式。我们注意到，物理形式及其行为的承受能力不仅有助于ML系统的可解释性，也有助于为批评提供一个开放的环境。该工作室寻求对可解释的ML术语进行批评，并绘制出TEI可以为人机界面提供的机会，以设计更可持续、更易掌握和更公正的智能系统。
Since the advent of Artificial Intelligence (AI) and Machine Learning (ML), researchers have asked how intelligent computing systems could interact with and relate to their users and their surroundings, leading to debates around issues of biased AI systems, ML black-box, user trust, user’s perception of control over the system, and system’s transparency, to name a few. All of these issues are related to how humans interact with AI or ML systems, through an interface which uses different interaction modalities. Prior studies address these issues from a variety of perspectives, spanning from understanding and framing the problems through ethics and Science and Technology Studies (STS) perspectives to finding effective technical solutions to the problems. But what is shared among almost all those efforts is an assumption that if systems can explain the how and why of their predictions, people will have a better perception of control and therefore will trust such systems more, and even can correct their shortcomings. This research field has been called Explainable AI (XAI). In this studio, we take stock on prior efforts in this area; however, we focus on using Tangible and Embodied Interaction (TEI) as an interaction modality for understanding ML. We note that the affordances of physical forms and their behaviors potentially can not only contribute to the explainability of ML systems, but also can contribute to an open environment for criticism. This studio seeks to both critique explainable ML terminology and to map the opportunities that TEI can offer to the HCI for designing more sustainable, graspable and just intelligent systems.
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DOI：
10.1145/3308557.3313112
发表时间：
2019
期刊：
Companion Proceedings of the 24th International Conference on Intelligent User Interfaces
影响因子：
--
作者：
Brian Y. Lim;Advait Sarkar;Alison Smith;S. Stumpf
通讯作者：
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影响因子：
1.3
作者：
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通讯作者：
Jeffrey Bardzell
DOI：
--
发表时间：
2018
期刊：
影响因子：
--
作者：
Mikael Wiberg
通讯作者：
Mikael Wiberg
影响因子：
0.9
作者：
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通讯作者：
Kulesza, Todd
赋予智能对象形式，探索智能作为交互设计材料
DOI：
10.1145/1709886.1709905
发表时间：
2018
期刊：
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
影响因子：
--
作者：
M. Rozendaal;Maliheh Ghajargar;Gert Pasman;Mikael Wiberg
通讯作者：
Mikael Wiberg