Call for Papers:
Special Issue of Human-Computer Interaction:
“Unifying Human Computer Interaction and Artificial Intelligence”
http://showhow.fxpal.com/hcij/publicInfo/cfp_hciai.pdf
Special Issue Editors:
Munmun De Choudhury, Min Kyung Lee, David A. Shamma, and Haiyi Zhu
Timeline:
March 20, 2019: Proposals Due (1,000 words)
June 15, 2019: Full Papers Due
MOTIVATION
Over the past decade, artificial intelligence (AI) has increasingly been deployed across many domains such as transportation, retail, criminal justice, finance and health. But these very domains that AI is aiming to revolutionize may also be where human implications are the most momentous. The potential negative effects of AI on society, whether amplifying human biases or the perils of automation, cannot be ignored and as a result such topics are increasingly discussed in scholarly and popular press contexts. As the New York Times notes: “[…] if we want [AI] to play a positive role in tomorrow’s world, it must be guided by human concerns.”
However, simply introducing human guidance or human sensitivity into AI is not going to be enough to realize AI’s full potential or to prevent its unintended consequences. AI is increasingly being incorporated into technology design, including technologies of deep interest to researchers and practitioners in human computer interaction (HCI). While most AI-based approaches offer promising methods for tackling real-world problems, many of the technologies they enable have been developed in isolation, without appropriate involvement of the human stakeholders who use these systems and who are the most affected by them. Human involvement in AI system design, development, and evaluation is critical to ensure that AI-based systems are practical, with their outputs being meaningful and relatable to those who use them. Moreover, human activities and behaviors are deeply contextual, complex, nuanced, and laden with subjectivity; aspects which may cause current AI-based approaches to fail as they cannot adequately be addressed by simply adding more data. As a result, to ensure the success of future AI approaches, we must incorporate new complementary human-centered insights. These include stakeholders’ demands, beliefs, values, expectations, and preferences-attributes that constitute a focal point of HCI research-and which need to be a part of the development of these AI-based technologies.
The same issues also give rise to important new methodological questions. For instance, how can existing HCI methodology incorporate AI methods and data to develop intelligent systems to improve the human condition? What are the best ways to bridge the gap between machines and humans while designing technologies? How can AI enhance the human experience in interactive technologies; and further could it help define new styles of interaction? How will conventional evaluation techniques in HCI need to be modified in contexts where AI is a core technology component? What existing research methods might be most compatible with AI approaches? And, what will be involved in training the next generation of HCI researchers who want to work at the intersection with AI? Of course the concepts of “design”, “interaction”, and “evaluation” continue to be interpreted by different HCI researchers and practitioners in many related but non-identical ways. Nonetheless, how the potential synergy between AI and HCI will influence these interpretations remains an open but pertinent question.
Naturally, conversations about the relationship between HCI and AI are not new. Shneiderman and Maes (1997) discussed if AI should be a primary metaphor in the human interface to computers. Similarly, Grudin (2009) described alternating cycles in which one approach flourished, while the other suffered a “winter”, characterized by a period of reduced funding, and academic and popular interest. And more than a decade ago, Winograd (2006) argued about the strengths and limitations, as well as the relevance of rationalistic and design approaches offered by AI and HCI respectively, when applied to “messy” human problems. While the landscape of both AI and HCI research has significantly evolved since these early conversations, and researchers have begun to be more vocal about the need for a stronger “marriage” between HCI and AI, nevertheless the competing philosophies and research styles of the two fields, the current context, both academic and societal, demands renewed attention to unifying HCI and AI.
This special issue aims to be a step forward in this regard. We hope to revive and extend prior attempts to bridge HCI and AI, given the burgeoning promise and traction AI has invited recently in tackling challenging human problems. In doing so, we seek to engage both HCI and AI researchers contributing theoretical, empirical, systems, or design papers that aim to unify these two perspectives. We want to bring together research that spans this wide set of issues to help integrate the different parts of this emerging space. By doing so, we aim to begin a constructive dialog to bridge the gap via original research.
TOPICS
Submissions should address key questions in unifying AI and HCI. The following questions are intended to be inspiring, not limiting:
- How can we bridge the fundamental mismatch between human-styles of interpretation, reasoning, and feedback and the machine’s statistical optimization for data with high-dimensionality?
- How can we incorporate human insights—including stakeholders’ demands, beliefs, values, expectations, and preferences—into the development of AI technologies?
- How can we predict the societal consequences of AI system deployment?
- How can we systematically evaluate the social, psychological, and economic impacts of AI technologies?
- How can we train our next-generation developers and designers to create AI system in a human-centered manner?
- How does AI change how we design and prototype new HCI systems and applications?
- How should AI interactions be designed to help end users understand AI and make better decisions?
- What HCI methods can we use to address AI’s limitations?
- What design methods and prototyping tools can help us create novel AI applications and services?
- How might existing human-centric methods help increase algorithmic transparency and explainability?
- Where can AI help HCI in testing, evaluation, and User Experience?
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