An Affordance-Based Framework for Human Computation and Human-Computer Collaboration

Abstract

Abstract-VisualAnalytics is "the science of analytical reasoning facilitated by visual interactive interfaces" [70]. The goal of this field is to develop tools and methodologies for approaching problems whose size and complexity render them intractable without the close coupling of both human and machine analysis. Researchers have explored this coupling in many venues: VAST, Vis, InfoVis, CHI, KDD, IUI, and more. While there have been myriad promising examples of human-computer collaboration, there exists no common language for comparing systems or describing the benefits afforded by designing for such collaboration. We argue that this area would benefit significantly from consensus about the design attributes that define and distinguish existing techniques. In this work, we have reviewed 1,271 papers from many of the top-ranking conferences in visual analytics, human-computer interaction, and visualization. From these, we have identified 49 papers that are representative of the study of human-computer collaborative problem-solving, and provide a thorough overview of the current state-of-the-art. Our analysis has uncovered key patterns of design hinging on humanand machine-intelligence affordances, and also indicates unexplored avenues in the study of this area. The results of this analysis provide a common framework for understanding these seemingly disparate branches of inquiry, which we hope will motivate future work in the field.

A Review of Guidance Approaches in Visual Data Analysis: A Multifocal Perspective

“…Since a collaboration consists of two parties working together, in our categorization we do not consider approaches in which only one agent is responsible for completing the whole task. Hence, we do not consider those approaches that are usually referred to as human‐computation systems [CC12, QB11, YCK09] in this report. Conversely to guidance, where human and system efforts are intertwined, human‐computation systems consider just one side of the collaboration.…”

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

“…Because our study is based on a systematic review, coding, and analysis of previous work in the visualization and VA community, it is also related to previous work on self-reflection of empirical studies in information visualization [35], evaluation in visualization research in general [28], or affordance in human computation and human-computer interaction [14]. While we adopt the methodology of systematic analysis of previous work, our paper has a very different focus.…”

Toward Complexity Measures for Systems Involving Human Computation

“…One recent framework (Crouser and Chang, 2012) illustrates this complementarity, and attempts to organize existing literature on human-machine collaborative systems according to which skills, or affordances, the system leverages. Other taxonomies (Bertini and Lalanne, 2010;Quinn and Bederson, 2011) propose additional classification dimensions such as human-machine balance, motivation, aggregation, quality control, process order, and task-request cardinality.…”