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The continuous growth and adoption of communication, mobility, and sensing technologies have spurred human data proliferation from a large number of diverse data sources, which has offered unprecedented opportunities to study human behaviors and their relationships to various types of systems and services. The analysis of human behaviors has impacted many domains, ranging from business, health, security, to education and disaster management. For example, marketers analyze website traffic data for behavioral insights to identify potential marketing targets, while healthcare providers augment clinical data with patients' social networking behavior to improve health plans and clinical outcomes. While many statistical techniques have been developed, people often find them insufficient to provide behavioral insights, especially into how individuals or crowds will behave and why, because behavioral context is missing from most statistical analysis. Interactive visual analytic systems that couple interaction design with data analysis, and show analytic results with behavioral context have provided new ways to overcome the challenge. This special issue aims at featuring recent progress and new results in this emerging area.
This special issue invites submissions featuring original research relating to interactive visual analysis of human and crowd behavior. A suitable submission must also demonstrate its relevance to the TiiS journal by exhibiting the two defining characteristics of an interactive intelligent system: intelligence and interactivity. The topics of interest include, but are not limited to:
Check out author information and submission details.
Recent advances in machine intelligence and robotics have enabled new forms of human-computer interaction characterized by greater adaptability, shared decision-making, and mixed initiative. These advances are leading towards machines that can operate with relative autonomy, but are designed to interact or engage with human counterparts in joint human-machine teams. The degree to which people trust machines is critical to the efficacy of these teams. People will cooperate with, and rely upon, intelligent agents they trust. Those they do not trust fall into disuse. As intelligent agents become more self-directed, learn from their experiences, and adapt behavior over time, the relationship between people and machines becomes more complex, and designing system behaviors to engender the proper level of trust becomes more challenging. Moreover, as intelligent systems become common in safety-critical domains, we must understand and assess the influence they might exert on human decision making to avoid unintended consequences, such as over-trust, compliance or undue influence. Online social environments further complicate human-machine relationships. In the social media ecosystem, intelligent agents (e.g., chat-bots) might act as aids or assistants, but also as competitors or adversaries. In this context, research challenges include understanding how human-machine relationships evolve in social media, and especially how humans develop trust and are susceptible to influence in social networks.
This special issue invites submissions featuring original research related to designing trustworthy intelligent agents, and trust and influence in the social media space. A suitable submission must also demonstrate its relevance to the TiiS journal by exhibiting the two defining characteristics of an interactive intelligent system: intelligence and interactivity. Interdisciplinary research is especially encouraged. Specific areas of interest include, but are not limited to:
Machine learning is one of the most important and successful techniques in contemporary computer science. It involves the statistical inference of models (such as classifiers) from data. It is often conceived in a very impersonal way, with algorithms working autonomously on passively collected data. However, this viewpoint hides considerable human work of tuning the algorithms, gathering the data, and even deciding what should be modeled in the first place. Examining machine learning from a human-centered perspective includes explicitly recognizing this human work, as well as reframing machine learning workflows based on situated human working practices, and exploring the co-adaptation of humans and intelligent systems. A human-centered understanding of machine learning in human context can lead not only to more usable machine learning tools, but to new ways of framing learning computationally. This journal issue will bring together research from different disciplines that aims to create a human-centered approach to machine learning.
We invite submissions presenting novel research concerning the role of humans in machine learning systems to a special issue on Human-Centered Machine Learning to be published in the ACM Transactions of Interactive Intelligent Systems (TiiS). These include both interactive machine learning systems and user studies that aim to understand the role of people in machine learning (or a combination of the two). The relevant topics are listed below. To be included in this special issue, each submission must also demonstrate its relevance to the two defining characteristics of an interactive intelligent system: intelligence and interactivity, as required by TiiS. Areas of interest include, but are not limited to:
Full paper submission deadline: December 2, 2016
First author notification: March 1, 2017
Revised paper due: June 1, 2017
Final author notification: July 1, 2017
Expected publication: Autumn 2017
Dr. Rebecca Fiebrink, Goldsmiths, University of London
Dr. Marco Gillies, Goldsmiths, University of London