Crowdsourcing for Reminiscence Chatbot Design

In this work-in-progress paper we discuss the challenges in identifying effective and scalable crowd-based strategies for designing content, conversation logic, and meaningful metrics for a reminiscence chatbot targeted at older adults. We formalize the problem and outline the main research questions that drive the research agenda in chatbot design for reminiscence and for relational agents for older adults in general

Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time

Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs. A promising direction is to combine the two approaches for high quality, low latency, and low cost solutions. In this paper, we introduce Evorus, a crowd-powered conversational assistant built to automate itself over time by (i) allowing new chatbots to be easily integrated to automate more scenarios, (ii) reusing prior crowd answers, and (iii) learning to automatically approve response candidates. Our 5-month-long deployment with 80 participants and 281 conversations shows that Evorus can automate itself without compromising conversation quality. Crowd-AI architectures have long been proposed as a way to reduce cost and latency for crowd-powered systems; Evorus demonstrates how automation can be introduced successfully in a deployed system. Its architecture allows future researchers to make further innovation on the underlying automated components in the context of a deployed open domain dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human Factors in Computing Systems 2018 (CHI'18

Avances en Interacción Humano-Computadora

Crowdsourced content creation like articles or slogans can be powered by crowds of volunteers or workers from paid task markets. Volunteers often have expertise and are intrinsically motivated, but are a limited resource, and are not always reliably available. On the other hand, paid crowd workers are reliably available, can be guided to produce high-quality content, but cost money. How can these different populations of crowd workers be leveraged together to power cost-effective yet high-quality crowdpowered content-creation systems? To answer this question, we need to understand the strengths and weaknesses of each. We conducted an online study where we hired paid crowd workers and recruited volunteers from social media to complete three content creation tasks for three real-world non-profit organizations that focus on empowering women. These tasks ranged in complexity from simply generating keywords or slogans to creating a draft biographical article. Our results show that paid crowds completed work and structured content following editorial guidelines more effectively. However, volunteer crowds provide content that is more original. Based on the findings, we suggest that crowdpowered content-creation systems could gain the best of both worlds by leveraging volunteers to scaffold the direction that original content should take; while having paid crowd workers structure content and prepare it for real world use.Avances en Interacción Humano computadorafile:///C:/Users/Propietario/Downloads/65-13-135-3-10-20210201.pd

Interactional Slingshots: Providing Support Structure to User Interactions in Hybrid Intelligence Systems

The proliferation of artificial intelligence (AI) systems has enabled us to engage more deeply and powerfully with our digital and physical environments, from chatbots to autonomous vehicles to robotic assistive technology. Unfortunately, these state-of-the-art systems often fail in contexts that require human understanding, are never-before-seen, or complex. In such cases, though the AI-only approaches cannot solve the full task, their ability to solve a piece of the task can be combined with human effort to become more robust to handling complexity and uncertainty. A hybrid intelligence system—one that combines human and machine skill sets—can make intelligent systems more operable in real-world settings. In this dissertation, we propose the idea of using interactional slingshots as a means of providing support structure to user interactions in hybrid intelligence systems. Much like how gravitational slingshots provide boosts to spacecraft en route to their final destinations, so do interactional slingshots provide boosts to user interactions en route to solving tasks. Several challenges arise: What does this support structure look like? How much freedom does the user have in their interactions? How is user expertise paired with that of the machine’s? To do this as a tractable socio-technical problem, we explore this idea in the context of data annotation problems, especially in those domains where AI methods fail to solve the overall task. Getting annotated (labeled) data is crucial for successful AI methods, and becomes especially more difficult in domains where AI fails, since problems in such domains require human understanding to fully solve, but also present challenges related to annotator expertise, annotation freedom, and context curation from the data. To explore data annotation problems in this space, we develop techniques and workflows whose interactional slingshot support structure harnesses the user’s interaction with data. First, we explore providing support in the form of nudging non-expert users’ interactions as they annotate text data for the task of creating conversational memory. Second, we add support structure in the form of assisting non-expert users during the annotation process itself for the task of grounding natural language references to objects in 3D point clouds. Finally, we supply support in the form of guiding expert and non-expert users both before and during their annotations for the task of conversational disentanglement across multiple domains. We demonstrate that building hybrid intelligence systems with each of these interactional slingshot support mechanisms—nudging, assisting, and guiding a user’s interaction with data—improves annotation outcomes, such as annotation speed, accuracy, effort level, even when annotators’ expertise and skill levels vary. Thesis Statement: By providing support structure that nudges, assists, and guides user interactions, it is possible to create hybrid intelligence systems that enable more efficient (faster and/or more accurate) data annotation.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163138/1/sairohit_1.pd

"Is There Anything Else I Can Help You With?" Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent

Intelligent conversational assistants, such as Apple's Siri, Microsoft's Cortana, and Amazon's Echo, have quickly become a part of our digital life. However, these assistants have major limitations, which prevents users from conversing with them as they would with human dialog partners. This limits our ability to observe how users really want to interact with the underlying system. To address this problem, we developed a crowd-powered conversational assistant, Chorus, and deployed it to see how users and workers would interact together when mediated by the system. Chorus sophisticatedly converses with end users over time by recruiting workers on demand, which in turn decide what might be the best response for each user sentence. Up to the first month of our deployment, 59 users have held conversations with Chorus during 320 conversational sessions. In this paper, we present an account of Chorus' deployment, with a focus on four challenges: (i) identifying when conversations are over, (ii) malicious users and workers, (iii) on-demand recruiting, and (iv) settings in which consensus is not enough. Our observations could assist the deployment of crowd-powered conversation systems and crowd-powered systems in general