Although we may complain about meetings, they are an essential part of an information worker's work life. Consequently, busy people spend a significant amount of time scheduling meetings. We present Calendar.help, a system that provides fast, efficient scheduling through structured workflows. Users interact with the system via email, delegating their scheduling needs to the system as if it were a human personal assistant. Common scheduling scenarios are broken down using well-defined workflows and completed as a series of microtasks that are automated when possible and executed by a human otherwise. Unusual scenarios fall back to a trained human assistant executing an unstructured macrotask. We describe the iterative approach we used to develop Calendar.help, and share the lessons learned from scheduling thousands of meetings during a year of real-world deployments. Our findings provide insight into how complex information tasks can be broken down into repeatable components that can be executed efficiently to improve productivity.
Microtask workflows are being increasingly used to accomplish complex, multi-step tasks @cite_6 , such as taxonomy-creation @cite_36 , itinerary-planning @cite_21 , writing @cite_25 , and even real-time conversations @cite_3 . Workflows have even been used to assemble flash teams of expert workers of different specialties @cite_14 . Prior work has demonstrated several advantages to breaking a monolithic task into microtasks, including making tasks easier for workers to complete @cite_34 , producing higher quality outcomes @cite_37 , reducing coordination and collaboration overheads @cite_25 , and facilitating partial automation of a larger task @cite_1 . Emerging services such as Facebook M @cite_31 , X.ai and Clara Labs @cite_22 @cite_8 use a combination of automation and human labor while providing users with a seamless experience. However, very little has been shared about how these systems actually work and are used.
@cite_37 @cite_14 @cite_22 @cite_8 @cite_36 @cite_21 @cite_1 @cite_3 @cite_6 @cite_31 @cite_34 @cite_25
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A large, seemingly overwhelming task can sometimes be transformed into a set of smaller, more manageable microtasks that can each be accomplished independently. For example, it may be hard to subjectively rank a large set of photographs, but easy to sort them in spare moments by making many pairwise comparisons. In crowdsourcing systems, microtasking enables unskilled workers with limited commitment to work together to complete tasks they would not be able to do individually. We explore the costs and benefits of decomposing macrotasks into microtasks for three task categories: arithmetic, sorting, and transcription. We find that breaking these tasks into microtasks results in longer overall task completion times, but higher quality outcomes and a better experience that may be more resilient to interruptions. These results suggest that microtasks can help people complete high quality work in interruption-driven environments. We introduce flash teams, a framework for dynamically assembling and managing paid experts from the crowd. Flash teams advance a vision of expert crowd work that accomplishes complex, interdependent goals such as engineering and design. These teams consist of sequences of linked modular tasks and handoffs that can be computationally managed. Interactive systems reason about and manipulate these teams' structures: for example, flash teams can be recombined to form larger organizations and authored automatically in response to a user's request. Flash teams can also hire more people elastically in reaction to task needs, and pipeline intermediate output to accelerate completion times. To enable flash teams, we present Foundry, an end-user authoring platform and runtime manager. Foundry allows users to author modular tasks, then manages teams through handoffs of intermediate work. We demonstrate that Foundry and flash teams enable crowdsourcing of a broad class of goals including design prototyping, course development, and film animation, in half the work time of traditional self-managed teams. Taxonomies are a useful and ubiquitous way of organizing information. However, creating organizational hierarchies is difficult because the process requires a global understanding of the objects to be categorized. Usually one is created by an individual or a small group of people working together for hours or even days. Unfortunately, this centralized approach does not work well for the large, quickly changing datasets found on the web. Cascade is an automated workflow that allows crowd workers to spend as little at 20 seconds each while collectively making a taxonomy. We evaluate Cascade and show that on three datasets its quality is 80-90 of that of experts. Cascade has a competitive cost to expert information architects, despite taking six times more human labor. Fortunately, this labor can be parallelized such that Cascade will run in as fast as four minutes instead of hours or days. An important class of tasks that are underexplored in current human computation systems are complex tasks with global constraints. One example of such a task is itinerary planning, where solutions consist of a sequence of activities that meet requirements specified by the requester. In this paper, we focus on the crowdsourcing of such plans as a case study of constraint-based human computation tasks and introduce a collaborative planning system called Mobi that illustrates a novel crowdware paradigm. Mobi presents a single interface that enables crowd participants to view the current solution context and make appropriate contributions based on current needs. We conduct experiments that explain how Mobi enables a crowd to effectively and collaboratively resolve global constraints, and discuss how the design principles behind Mobi can more generally facilitate a crowd to tackle problems involving global constraints. We show how machine learning and inference can be harnessed to leverage the complementary strengths of humans and computational agents to solve crowdsourcing tasks. We construct a set of Bayesian predictive models from data and describe how the models operate within an overall crowd-sourcing architecture that combines the efforts of people and machine vision on the task of classifying celestial bodies defined within a citizens' science project named Galaxy Zoo. We show how learned probabilistic models can be used to fuse human and machine contributions and to predict the behaviors of workers. We employ multiple inferences in concert to guide decisions on hiring and routing workers to tasks so as to maximize the efficiency of large-scale crowdsourcing processes based on expected utility. Despite decades of research attempting to establish conversational interaction between humans and computers, the capabilities of automated conversational systems are still limited. In this paper, we introduce Chorus, a crowd-powered conversational assistant. When using Chorus, end users converse continuously with what appears to be a single conversational partner. Behind the scenes, Chorus leverages multiple crowd workers to propose and vote on responses. A shared memory space helps the dynamic crowd workforce maintain consistency, and a game-theoretic incentive mechanism helps to balance their efforts between proposing and voting. Studies with 12 end users and 100 crowd workers demonstrate that Chorus can provide accurate, topical responses, answering nearly 93 of user queries appropriately, and staying on-topic in over 95 of responses. We also observed that Chorus has advantages over pairing an end user with a single crowd worker and end users completing their own tasks in terms of speed, quality, and breadth of assistance. Chorus demonstrates a new future in which conversational assistants are made usable in the real world by combining human and machine intelligence, and may enable a useful new way of interacting with the crowds powering other systems. Paid crowd work offers remarkable opportunities for improving productivity, social mobility, and the global economy by engaging a geographically distributed workforce to complete complex tasks on demand and at scale. But it is also possible that crowd work will fail to achieve its potential, focusing on assembly-line piecework. Can we foresee a future crowd workplace in which we would want our children to participate? This paper frames the major challenges that stand in the way of this goal. Drawing on theory from organizational behavior and distributed computing, as well as direct feedback from workers, we outline a framework that will enable crowd work that is complex, collaborative, and sustainable. The framework lays out research challenges in twelve major areas: workflow, task assignment, hierarchy, real-time response, synchronous collaboration, quality control, crowds guiding AIs, AIs guiding crowds, platforms, job design, reputation, and motivation. What happens when we algorithmically break complex productivity tasks down into microtasks? At Microsoft Research, the author and her team are accelerating a shift toward microproductivity to make it easy for people to get big things done one small step at a time. This paper presents the MicroWriter, a system that decomposes the task of writing into three types of microtasks to produce a single report: 1) generating ideas, 2) labeling ideas to organize them, and 3) writing paragraphs given a few related ideas. Because each microtask can be completed individually with limited awareness of what has been already done and what others are doing, this decomposition can change the experience of collaborative writing. Prior work has used microtasking to support collaborative writing with unaffiliated crowd workers. To instead study its impact on collaboration among writers with context and investment in the writing project, we asked six groups of co-workers (or 19 people in total) to use the MicroWriter in a synchronous, collocated setting to write a report about a shared work goal. Our observations suggest ways that recent advances in microtasking and crowd work can be used to support collaborative writing within preexisting groups.