An Introduction to Human Computation and Games With A Purpose - Part I
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An Introduction to Human Computation and Games With A Purpose - Part I

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Crowdsourcing and human computation are novel disciplines that enable the design of computation processes that include humans as actors for task execution. In such a context, Games With a Purpose are ...

Crowdsourcing and human computation are novel disciplines that enable the design of computation processes that include humans as actors for task execution. In such a context, Games With a Purpose are an effective mean to channel, in a constructive manner, the human brainpower required to perform tasks that computers are unable to per- form, through computer games. This tutorial introduces the core research questions in human computation, with a specific focus on the techniques required to manage structured and unstructured data. The second half of the tutorial delves into the field of game design for serious task, with an emphasis on games for human computation purposes. Our goal is to provide participants with a wide, yet complete overview of the research landscape; we aim at giving practitioners a solid understanding of the best practices in designing and running human computation tasks, while providing academics with solid references and, possibly, promising ideas for their future research activities.

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  • Of course this “cheap labor” thing is not something to be proud about…. We will see it later
  • [Blinder 2006, Horton 2013] ???And in fact, Blinder argues that about 20% of current American jobs could be sent down a wire. These include tasks like programming, accounting, marketing, and even machine operators. Recent evidence for crowd work in particular suggests that its volume will be roughly 454 billion dollars per year. That’s 91 billion hours per year, employing about 45 million fulltime workers. What might this mean? Think of current workers having the ability to become fulltime contractors, able to control their jobs and their career as they desire. On the other end of the spectrum, we get far more flexibility in time, like a stay-at-home dad who uses his skills while the baby is sleeping.
  • Alexis Claude de Clairaut found a model that could be solved numerically He recruited two friends for 9 months, divide the calculations of the orbit, mathematically tracing the cometHuman computers soon discovered the benefits of dividing the task and specializing their skills. Adam Smith [1723–1790] the division of labor produce “greatest improvement in productive powers of labor.” Human computers could reduce the cost of computation by either increasing the speed of calculation or by reducing errors in calculationTraditionally, hierarchical control (good in military) =>More visionary: mechanical controlCharles Babbage [1792-1871]'s Difference EngineThe Difference Engine was invented because Babbage was frustrated by limitations of (human) computers.A machine combining the additions and subtractions in order to interpolate a functionFirst World War required large numbers of human computers (map grids, surveying aids, navigation tables and artillery tables)Most computers were women and many were college educatedGreat Depression and Second World War: WPA (Works Progress Administration)  Mathematical Tables Project Requirement: use labor-intensive methods in order to employ the greatest number of workersMost of the computers knew little about arithmeticDeveloped ways of organizing the group and devised mathematical methods that were self-checkingWorkers gladly did the hard labor of research calculation in the hope that they might be part of the scientific community. In the end, they were rewarded by a new electronic machine that took the place and the name of those who were, once, the computers.
  • http://en.wikipedia.org/wiki/Frederick_Winslow_Taylor#Managers_and_workersIt is only through enforced standardization of methods, enforced adoption of the best implements and working conditions, and enforced cooperation that this faster work can be assured. And the duty of enforcing the adoption of standards and enforcing this cooperation rests with management alone.[9]Workers were supposed to be incapable of understanding what they were doing. According to Taylor this was true even for rather simple tasks.
  • Thisis a famous game, called “Where’s Wally”. Identify Wally withinthis image, givenit’sdescriptionprovided by its image
  • It does not take into account the fact that workers can make random guesses or make mistakes and still agree by chance [13]. This is especially problematic if the majority of the workers are novices (who systematically make the same kinds of errors) or spammers (who generate answers at random). Additionally, many of the factors that influence the outcome of the computation are not captured by the simple model. First, each worker may have different biases. F
  • Initialize by aggregating labels for each object (e.g., use majority vote)Estimate error rates for workers (using aggregate labels)Estimate aggregate labels (using error rates, weight worker votes according to quality)Keep labels for “gold data” unchangedGo to Step 2 and iterate until convergence
  • Not that simple: how do you build the data?
  • Mention active learning
  • Writing a news story• Programming software • Composing a symphony
  • the first challenge is to decompose the task:if you were planning a conference, you might split up finding a venue from reviewing papersif you were google, you might split up different parts of the web for different machines to process need to assemble the right teams of people:if you were conference chair you need to find respectable academics or coercable friends to be on the committee if you were google: assign different machines play different roles, like a master node coordinating a mapreduce process finally, you have to execute workflows, which may have multiple stages and decision processesfor a conference we have multistage review processesin computing, we have algorithms: for example, the output of one mapreduce process may get passed to another
  • badges can influence and steer user behavior on a site—leading both to increased participation and to changes in the mix of activities a user pursues on the site.

An Introduction to Human Computation and Games With A Purpose - Part I An Introduction to Human Computation and Games With A Purpose - Part I Presentation Transcript

  • AN INTRODUCTION TO HUMAN COMPUTATION & GAMES WITH A PURPOSE ALESSANDRO BOZZON DELFT UNIVERSITY OF TECHNOLOGY LUCA GALLI POLITECNICO DI MILANO
  • ABOUT THE TUTORIAL • Crowdsourcing, Human Computation, and GWAPs are hot topics • “Human Computation” => more than 3000 papers • 400 in 2013 • “Crowd Sourcing” => more than 36000 papers • 4800 in 2013 • “Games With A Purpose” => more than 1400 papers • 162 in 2013 • This short tutorial is necessarily shallow, but • Concrete Examples • Lot of references and links • An outlook on the future • Slides and additional materials available • http://hcgwap.blogspot.com ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 2
  • ABOUT THE SPEAKERS ALESSANDRO BOZZON Assistant Professor - TU Delft http://www.alessandrobozzon.com a.bozzon@tudelft.nl LUCA GALLI Ph.D. Student - Politecnico di Milano http://www.lucagalli.me lgalli@elet.polimi.it ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 3 • RESEARCH BACKGROUND AND INTERESTS • Web Data Management • Crowdsourcing and Human Computation • Game Design • Web Engineering and Model Driven Development
  • AGENDA 4 ICWE 2013 - An Introduction To Human Computation and Games With a Purpose
  • AGENDA • PART 1 => CrowdSourcing and Human Computation • Introduction • Design of Human Computation Tasks • Frameworks And Applications • The Future of Human Computation • PART 2 => Games With a Purpose ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 5
  • PART 1 HUMAN COMPUTATION 6 ICWE 2013 - An Introduction To Human Computation and Games With a Purpose
  • INTRODUCTION PART 1 7 ICWE 2013 - An Introduction To Human Computation and Games With a Purpose
  • THE RISE OF CROWDSOURCING • The “….sourcing” trend, from a business perspective [B_Tibbets2011] • Outsourcing: Outsource the data center or outsource application development • Same or better quality, less effort, less money • Offshoring: Outsourcing to developing countries (e.g. India, China) • offshore outsourcing • The same quality software at a huge discount • CrowdSourcing: everyday people use their spare cycles to create content, solve problems, etc. ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 8 [1990’s] Outsourcing [2000’s] Offshore outsourcing [2010’s] CrowdSourcing • Human Computation S A V I N G S Jeff Howe [B_Wired2006]
  • THE AGE OF THE CROWD • Distributed computing projects: UC Berkeley’s SETI@home? • Tapping into the unused processing power of millions of individual computers • “Distributed labor networks” • Using the Internet (and Web 2.0) to exploit the spare processing power of millions of human brains • Successful examples? • Open source software: a network of passionate, geeky volunteers could write code just as well as highly paid developers at Microsoft or Sun Microsystems • often better • Wikipedia: creating a sprawling and surprisingly comprehensive online encyclopedia • Quora, StackExchange: can’t exist without the contributions of users ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 9
  • THE AGE OF THE CROWD • The productive potential of millions of plugged-in enthusiasts is attracting the attention of old-line business too • Cheap Labor => Overseas Vs. Connected work forces • Technological advances (from product design software to digital video cameras) are breaking down the cost barriers that once separated amateurs from professionals • Smart companies in industries tap the latent talent of the crowd ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 10 “The labor isn’t always free, but it costs a lot less than paying traditional employees. It’s not outsourcing: it’s crowdsourcing”
  • DEFINITION OF HUMAN COMPUTATION • “…the idea of using human effort to perform tasks that computers cannot yet perform, usually in an enjoyable manner.” [Law2009] • “…a new research area that studies the process of channeling the vast internet population to perform tasks or provide data towards solving difficult problems that no known efficient computer algorithms can yet solve” [Chandrasekar2010] • “…systems of computers and large numbers of humans that work together in order to solve problems that could not be solved by either computers or humans alone” (Quinn2009) ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 11 SUGGESTED VIEWS  http://www.youtube.com/watch?v=tx082gDwGcM http://www.youtube.com/watch?v=Aszl5avDtek
  • CAPTCHA ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 12 http://xkcd.com/233/ “Completely Automated Public Turing test to tell Computers and Humans Apart” Luis von Ahn et al. 2000
  • THE HUMAN CO-PROCESSING UNITS (HPU) [DAVIS2010] • Humans are a first class computational platform ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 13 Abstract Computer-mediated, human micro-labor markets have so far been treated as novelty services good for cheaply labeling training data and easy user studies. This paper’s primary contribution is conceptual, the claim that these markets can be characterized as Human co-Processing Units (HPU), and represent a first class computational platform. In the same way that Graphics Processing Units (GPU) represent a change in architecture from CPU based computation, HPU based computation is different, and deserves careful characterization and study. We demonstrate the value of this claim by showing that simplistic HPU computation can be more accurate than complex CPU based algorithms on some important computer vision tasks. We also argue that HPU computation can be cheaper than state-of-the-art CPU based computation. Finally we give some examples of characterizing the HPU as an architectural platform. 1. Introduction This paper explores the idea that humans can be used as a processor for certain tasks, in the same way that CPUs and GPUs are now used. Rather than thinking of humans as the primary director of computation, with computers as their subordinate tools, we explicitly advocate treating these computational platforms equally and characterizing the performance of Human Processing Units (HPUs). We from where. Nearly all current use of micro-outsourcing is similar to the traditional way we might use an employed assistant in our office, to outsource from human to human. This proposal explicitly suggests we should quantify performance, treat this as a new computational platform, and build real systems which make use of HPU co- processors for certain tasks which are too computationally expensive, or insufficiently robust when computed on CPUs. A survey of other papers using micro-labor for computer vision reveals two dominant frameworks in Figure 1: We usually think of machines as computational tools to help humans perform better. This paper argues that humans are also computational tools to help machines perform better.
  • A GROWING, MULTIDISCIPLINARY FIELD… ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 14
  • … WITH A BIG MARKET… • estimated future volume • $454,000,000,000 per year • 91,000,000,000 hours per year • 45,000,000 full-time workers ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 15
  • … WITH COMPLEX RELATIONS BETWEEN DISCIPLINES [QUINN2011] • Crowdsourcing • Social computing • Collective intelligence • Data mining • A lot of value here! ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 16
  • COLLECTIVE INTELLIGENCE Large groups of loosely organized people can accomplish great things working together • Traditional study focused on “decision making capabilities by a large group of people” • Taxonomical “genome” of collective intelligence • “… groups of individuals doing things collectively that seem intelligent” [Malone2009] • Collective intelligence generally encompasses human computation and social computing ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 17
  • CROWDSOURCING AND HUMAN COMPUTATION • “Crowdsourcing is the act of taking a job traditionally performed by a designated agent (usually an employee) and outsourcing it to an undefined, generally large group of people in the form of an open call.” (Jeff Howe) • Human computation replaces computers with humans • Crowdsourcing replaces traditional human workers with members of the public • Crowdsourcing facilitates human computation (but they are not equivalent) • Citizen journalism, sensing, ... ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 18
  • SOCIAL COMPUTING • Social computing is a general term for an area the intersection of social behavior and computational systems. • In the broad sense of the term, social computing has to do with supporting any sort of social behavior through computational systems. • any social software => blogs, email, instant messaging, social network services, wikis, … • In the narrow sense of the term, social computing has to do with supporting computations that are carried out by groups of people • collaborative filtering, online auctions, prediction markets, reputation systems, tagging, and verification games ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 19
  • DISTINGUISHING FEATURES OF HUMAN COMPUTATION • Conscious Effort • Humans are actively computing something, not merely carrier of sensors and computational devices. • Explicit Control • The outcome of the computation is determined by an algorithm, and not the natural dynamics of the crowd. Although sometimes those constraints can be relaxed • e.g. human computation on social networks, GWAPS ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 20
  • HISTORY OF HUMAN COMPUTATION The term “computer” used to refer to humans who did computation ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 21 [Grier2005] 1700’s Alexis Claude de Clairaut 1800’s Charles Babbage 1900’s World Wars 1940’s ENIAC Division of labor -- Mass production -- Professional managers
  • HISTORY OF HUMAN COMPUTATION Alan Turing wrote in 1950: “The idea behind digital computers may be explained by saying that these machines are intended to carry out any operations which could be done by a human computer” ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 22 [Turing1950]
  • ELECTRONIC VS. HUMAN COMPUTERS Electronic • Fast • Deterministic • Arithmetic Human • Slow • Inconsistent & Noisy • But… still better at some things ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 23
  • EMULATING HUMAN COMPUTERS • Computer scientists (in the artificial intelligence field) have been trying to emulate human abilities • Language • Visual processing • Reasoning • … • Can you think about some other hard-to-imitate human abilities? • Now we need humans again for the “AI-complete” tasks ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 24 of accomplishing? Or are we really targeting the $10/day global middle class? Characterizing the HPU is addressing exactly this question. Unthinkable new opportunities: There are many products and services which could exist now, but don‟t exist, because some critical computational component is not yet robustly and efficiently computable. If HPUs are shown to provide a solution for some of these components, new companies will arise, offering products we would currently believe to be impossible (or at least too costly to implement robustly). Completely new applications are possible which would be impossible to consider using current CPU based algorithms, because they simply could not possibly be made sufficiently robust. Consider a diet aid application running on a smartphone. Every time you eat something, you take a picture of it, and the application computes the calories and other nutritional information, keeping statistics for the user. Would current CPU based object recognition applications be able to tell which food I‟m eating? Doubtful. Would HPU based algorithms do better? Probably not perfect, but perhaps well enough we can at least imagine the service. 4. Experiments – HPU vs CPU The experiments in this section are meant to establish that it is meaningful to directly compare CPU and HPU performance on standard computer vision tasks and that in some instances HPU algorithms will outperform CPU. Comparisons of HPU/CPU accuracy include bar code reading, color labeling, text summarization, and gender classification. In all cases we find that simple HPU algorithms are competitive with published CPU based algorithms. All HPU experiments in this paper were performed using Amazon‟s Mechanical Turk. 4.1. Accuracy HPU vs CPU: Barcodes Since their first commercial use in 1966 barcodes have become the de facto standard for processing and handling goods. Their design was optimized to maximize the accuracy of reading with laser scanners. The introduction return_value = HPU(image, “For the image below, please type in the numbers you see below the barcode”); HPU computation is not deterministic. Thus, in contrast to CPU computation, it is frequently easy to improve performance simply by making multiple calls to the same function and aggregating the results. In this example, we iterate 6 times over the call to the HPU. This aggregation can be done on the CPU, leading to HPU/CPU hybrid algorithms. We implement a simple aggregator that rejects answers with the wrong number of digits or which fail barcode checksum, and then uses voting to determine which of several answers is correct. Figure 4 gives a comparison of the percentage of barcodes detected accurately by each method. Note that the HPU method is comparable to the best CPU method we tested, and that the HPU/CPU joint method Easy Hard Figure 3: Examples of barcode images found in our Easy and Hard datasets. Note that the Hard image has significant blurring effects. Barcode Recognition Accuracy: HPU and CPU methods Method Easy (%) Hard (%) HPU/CPU 100% 83% HPU 92% 60% CPU [Gallo09] 98% 54% CPU [Tekin09] 95% 6% CPU DataSymbol 0% 0% CPU DTK 98% 3% CPU OCR 59% 0% Figure 4: A comparison of a variety of CPU and HPU based methods for determining barcode values on both Easy and Hard datasets. Note that the joint HPU/CPU method outperforms either HPU or CPU based computation alone. (Many comparison numbers from [Gallo09])
  • EXAMPLE OF “DIFFICULT” COMPUTATIONAL PROBLEMS ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 25 Sorting Medical Diagnosis Object recognition Translation Editing Planning set of objects set of objects sorted x-ray, lab tests diagnosis Image Tag Source sentence  sentence corrected Text  Text Goal, Constraints  sequence of actions
  • THE HUMAN ADVANTAGE • Perception • Perception/comprehension: reconstructing information that wasn't captured at capture-time (as in a photo or surface scan) • Constructing/inferring information that was never recorded using knowledge humans naturally possess • Sketch • Recognizing emotions • Labeling images • Preference/aesthetic judgments • evaluate goodness ("beauty") for sorting or optimization • Sims, Electric Sheep, Interactive Genetic Algorithm/Human- Based Genetic Algorithms, [Little 2009]/[Bernstein 2011] ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 26
  • THE HUMAN ADVANTAGE • Creativity • search: finding images that go well together • art projects like The Sheep Market [Koblin 2006] • [Little 2009/10] for expanding text/jokes/shirt design • [Yu and Nickerson 2011] for sketching chair designs (“Cooks or Cobblers”) • [Bernstein 2011] for posing humans • [Kittur 2011] for wikipedia... or wikipedia ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 27
  • MODERN HUMAN COMPUTATION The Open Mind Initiative (1999) • “… a web-based collaborative framework for collecting large knowledge bases from non-expert contributors.” • “an attempt to ... harness some of the distributed human computing power of the Internet, an idea which was then only in its early stages.” • It accumulated more than a million English facts from 15.000 contributors ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 28
  • MODERN HUMAN COMPUTATION Luis Von Ahn’s Phd Thesis • [VonAhn2005] “A paradigm for utilizing human processing power to solve problems that computers cannot yet solve” • “We treat human brains as processors in a distributed system, each performing a small part of a massive computation.” • “We argue that humans provide a viable, under- tapped resource that can aid in the solution of several important problems in practice.” ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 29
  • AREAS OF APPLICATIONS • Data management • Data analytics • Training • Collaboration and knowledge sharing • Customer loyalty programs • Ad network optimization • Virtual goods and currencies. ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 30
  • CROWD-ENHANCED DATA MANAGEMENT Relational • Information Extraction • Schema Matching • Entity Resolution • Data spaces • Building structured KBs • Sorting • Top-k • … Beyond Relational • Graph Search • Mining and Classification • Social Media Analysis • NLP • Text Summarization • Sentiment Analysis • Search • … ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 31
  • AMAZON MTURK • Artificial Artificial Intelligence • Provide a UI and Web Services API to allow developers to easily integrate human intelligence directly into their processing ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 32 www.mturk.com
  • TYPE OF TASKS IN M-TURK ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 33
  • CROWDFLOWER • Labor on-demand • Less problems with • Worker engagement (see later) • 27 Channels • Quality control features ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 34
  • SOME OTHER HUMAN COMPUTATION PLATFORMS • CloudCrowd • DoMyStuff • Livework • Clickworker • SmartSheet • uTest • Elance • oDesk • vWorker (was rent-a-coder) ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 35
  • BUT ALSO SEVERAL OTHERS… • Social Networks • Q&A Systems • Ad-hoc crowds • … ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 36
  • ETHICS AND OPPORTUNITIES • Developer Outsourced His Job To China To surf Reddit • [B_NextWeb2013] • New “Taylorization” era? • More later • Duke professor uses crowdsourcing to grade • [B_Chronicle2009] • We can make some cool science! ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 37 http://www.robcottingham.ca/cartoo n/archive/2007-08-07- crowdsourced/
  • DESIGN PART 1 38 ICWE 2013 - An Introduction To Human Computation and Games With a Purpose
  • COMPUTATION The process of mapping an input to an output • Algorithm: An algorithm is a finite set of rules which gives a sequence of operations for solving a specific type of problem, with five important properties • Input: quantities that are given to it initially before the algorithm begins, or dynamically as the algorithm runs. • Output: quantities that have a specified relation to the inputs. • Finiteness: An algorithm must always terminate after a finite number of steps • Definiteness: Each step of an algorithm must be precisely defined • Effectiveness: its operations must all be sufficiently basic that they can in principle be done exactly and in a finite length of time by someone using pencil and paper ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 39 OUTPUTINPUT
  • TASK A crowdsourced data creation/manipulation/analysis activity typically focused on a single action (although several concurrent actions are allowed) performed on coherent set of Objects Also known as HIT (Human Intelligence Task) ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 40
  • EXAMPLES OF TASKS • Recognize and identify the people contained in a set of image • Input Objects: images • Output Objects: images + bounding boxes + names • Annotate the named entities contained in a book • Input Objects: text organized in pages • Output Objects: set of named entities • Crop the silhouette of the models in a set of images • Input Objects: images • Output Objects: images + polylines • Create a complete list of the restaurants nearby PoliMI • Input Objects: none • Output Objects: set of restaurant names • Evaluate the courses offered at TUDelft • Input Objects: set of course names • Output Objects: course names + vote ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 41
  • PERFORMER A human being involved in the execution of a Task • A.k.a. workers, turkers, etc. • The workforce • Examples • Amazon Mechanical Turk Workers • Students of the IR course • ICWE Attendees • My Facebook friends • Javascript Experts on Stack Overflow ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 42
  • MICROTASK An instance of a Task, operating on a subset of its input objects, and assigned to one or more performers for execution • The simplest unit of execution • Typically rewarded • Examples • Locate and identify the faces of the people appearing in the following 5 images • Order the following papers according to your preference • Find me the email address of the following companies ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 43
  • HUMAN COMPUTATION DESIGN • How hard is the problem? Is it efficiently solvable? • Trade-off between human versus machine? • Is the human computation algorithm correct and efficient? • How do we aggregate the outputs of many human computers? • To whom do we route each task, and how? • How to motivate participation, and incentivize truthful outputs? ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 44 What WhoHow GOAL => Given a computational problem, design a solution using human computers and automated computers
  • TRADE-OFF • There is always a tradeoff between how much work the human does and how much work the computer does. ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 45
  • EXAMPLE: SORT ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 46 Human Computation Algorithms human-driven operation function quicksort(A) initialize empty lists L and G if (length(A) ≤ 1) returnA pivot = A.remove(find_pivot(A)); for x inA if compare(x,pivot) L.add(x) else G.add(x) return concatenate(quicksor t(L),pivot,quicksort(G)) function pivot(A) return randomIndex(A); function compare(x,pivot) return human_compare(x,pivot) Mechanical TurkTask
  • XKCD CROWD-SORT ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 47 http://xkcd.com/1185/
  • PROBLEM TYPES • Simple Problems • Computational problems solved by using a single human computation task ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 48 • Complex Problems • Computational problems solved by using a set of tasks organized according to a given workflow • Hybrid Problems • Computational problems solved by organizing human and automatic computation in one or more workflows • Human Orchestration • Tasks are coordinated by humans • Automatic Orchestration • Task are automatically coordinated by machines • Hybrid Orchestration • Humans and machines coordinate tasks
  • TYPICAL WORKFLOW ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 49 Experiment Design Task Design Task Design Task Design Task Design Task Design Task Execution Task Control Output Aggregation and Analysis Iterate and Improve
  • ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 50 SIMPLE PROBLEMS
  • TASK TASK DESIGN ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 51 μTaskTask UX Input Objects Output Objects Design Interface Operations Output Aggregation And Quality Control Task Routing Incentives Advertisement (Requester) Reputation Management
  • OPERATION TYPES • A possible (non-exhaustive) list of human computation tasks may include: • Data creation/modification • Object Recognition/Identification/Detection • Sorting (Clustering/Ordering) • Natural Language Processing • State Space Exploration • Content Generation/Submission • User preference/opinion elicitation ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 52
  • OBJECT RECOGNITION Recognize one or several pre-specified or learned objects together with their 2D positions in the image or 3D poses in the scene. ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 53 Qiang Hao, Rui Cai, Zhiwei Li, Lei Zhang, Yanwei Pang, Feng Wu, and Yong Rui. "Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition"
  • IDENTIFICATION • Recognize an individual instance of an object • Identification of a specific person's face or fingerprint • Identification of a specific vehicle ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 54
  • DETECTION An image/text is analyzed to recognize a specific condition or anomalies. ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 55
  • CLUSTERING Task of grouping a set of objects in such a way that objects in the same group (called cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). Task for humans: define a (subjective) similarity measure to compare the input data with and group objects into clusters based on it. ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 56
  • ORDERING • Arranging items of the same kind, class, nature, etc. in some ordered sequence, based on a particular criteria. • define a (subjective) evaluation criteria to compare the input data and order the objects based on the chosen criteria. ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 57 Altwaijry,H.;Belongie,S.,"Relativerankingoffacial attractiveness,"ApplicationsofComputerVision (WACV),2013IEEEWorkshopon
  • TASK UI AND INTERACTION • Workers want to maximize their income and their reputation • The UI is one of the most important aspect of the relationship with workers • Prepare to iterate • Ask the right questions • Keep it short and simple. Brief and concise. • Workers may not be experts: don’t assume the same understanding in terms of terminology • Show examples • Engage with the worker • Attractiveness (worker’s attention & enjoyment) • Workers also have intrinsic motivations => Avoid boring stuff ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 58
  • OTHER DESIGN PRINCIPLES • Text alignment • Legibility • Reading level: complexity of words and sentences • Multicultural / multilingual • Who is the audience (e.g. target worker community) ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 59
  • AGGREGATION ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 60 • Challenges: • Output are noisy (lack of expertise) • Humans are not always reliable (cheating) • Cultural context may bias the answers • Goal: Automatic procedure to merge Micro-task results • Assumptions: • There exists a “True” answer • Redundancy helps • What to look for? • Agreement, reliability, validity
  • WHAT IS TRUTH? Objective truth Exists freely or independently from a mind (E.g. ideas, feelings) • Medical diagnosis, protein structure, number of birds... Cultural truth Shared beliefs of a group of people, often involving perceptual judgments. • Is the music sad? Is this image pornographic? Is this text offending? ... ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 61
  • LATENT CLASS MODEL • Observed : HIT outputs • Latent (hidden) : Truth, user experience, task difficulty • Often, the matrix is incomplete • Ground truth may never been known ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 62 O11 O12 ... O1N O21 O22 ... O2N ... ... ... ... OM1 OM 2 ... OMN æ è ç ç ç ç ç ö ø ÷ ÷ ÷ ÷ ÷ Y1 Y2 ... YM æ è ç ç ç ç ç ö ø ÷ ÷ ÷ ÷ ÷ SolutionTasks Workers
  • MAJORITY VOTE • Ask multiple labelers, keep majority label as “true” label • Assumptions • The output that each worker independently generates depends on the true answer • There is no prior information about which categories are more or less likely to be the true classification • Quality is probability of being correct ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 63 Onm Yn True Answer Observed Output M N
  • MAJORITY VOTE ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 64 Yn = argmax j P(Yn = j |O) Yn = argmax j P(On,m = on,m |Yn = j)P(Yn = j) m=1 M Õ P(O) Yn = argmax j P(On,m = on,m |Yn = j)+ j) m=1 M Õ Yn µargmax j (1-e) 1(On,m=j) ×em=1 M å 1(On,m¹ j)m=1 M å n  computation Task 1-ε probability of the correct answer j  answer m  performer
  • MAJORITY VOTING AND LABEL QUALITY • Quality is probability of being correct ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 65 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1 3 5 7 9 11 13 QualityforMajorityVote Number of labelers
  • MEASURING MAJORITY • Some statistics • Percentage agreement • Cohen’s kappa (2 raters) • Fleiss’ kappa (any number of raters) • But what if 2 say relevant, 3 say not? • Use expert to break ties • Collect more judgments as needed to reduce uncertainty • Can we try and estimate quality? ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 66
  • HIDDEN FACTORS • Majority vote works best when workers have similar quality • But workers can make random guesses or make mistakes and still agree by chance • Worker Characteristics • Expertise (e.g., bird identification) • Bias (e.g., mother vs college students) • Physical Conditions (e.g., fatigue) • Task Characteristics • Quality (e.g., blurry pictures) • Difficulty (e.g., transcription of non-native speech) ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 67
  • INCORPORATING WORKER QUALITY Objective: Medical diagnosis by doctors Model: Doctors have different rates and types of errors. • πjl (k) defines the probability of doctor k to declare a patient in state l when the true state is j, • ηil (k) is the number of time the clinician k gets responses I from patient i. ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 68 Onm Yn True Answer Observed Output M N πm Worker Characteristics
  • INCORPORATING WORKER QUALITY • Solution: Expectation-Maximization (EM) Algorithm (Dawid & Skene, 1979) • Estimate the confusion matrix AND the true state of an object simultaneously, using the Expectation-Maximization (EM) algorithm, which iteratively • estimates the true states of each object by weighing the votes of the performers according to our current estimates of their quality (as given by the confusion matrix) • re-estimates the confusion matrices based on the current beliefs about the true states of each patient. ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 69
  • INCORPORATING TASK DIFFICULTY • EXAMPLE [Welinder 2010] • HIT: Select images containing at least one “duck” • Competence varies with bird image • Worker’s bias toward various mistakes • Difficulty of the image ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 70 Onm Yn True Answer Observed Output M N πm Worker Characteristics βn Task Difficulty
  • QUALITY CONTROL • An holistic problem • It is not only about the workers performance • Is the question well expressed? • Is the UI understandable? • You may think the worker is doing a bad job, but the same worker may think you are a lousy requester (see reputation) • Strategies • Beforehand => Qualification test, Screening (by quality/competence), recruiting, training • During => Assess worker quality “as you go” • After : Accuracy metric, Filter, weight • Still no success guaranteed! ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 71
  • QUALITY CONTROL SCREENING • Approval rate • Typically built-in in human computation platforms • Mechanical Turk recently introduced a Master qualification for workers => few, and very “picky”, only for specific tasks • Crowdflower Programmatic Gold • It can be defeated • Geographic restrictions / Workers community • Also built-in • Mechanical Turk: US / India • Crowdflower: Mechanical Turk / Others • White list / black list of workers • For known superstars/spammers • To be manually maintained ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 72
  • QUALITY CONTROL QUALIFICATION TEST • Prescreen workers ability to do the task (accurately) • AMT: assign qualification to workers • Advantages • Great tool for controlling quality • Disadvantages • Extra cost to design and implement the test • May turn off workers, hurt completion time • Refresh the test on a regular basis • Hard to verify subjective tasks like judging relevance • Try creating task-related questions to get worker familiar with task before starting task in earnest ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 73
  • QUALITY CONTROL GOLD TESTING • Two strategies • Trap questions with known answers (“honey pots”) • Measure inner-annotator agreement between workers • An exploration-exploitation scheme: • Explore: Learn about the quality of the workers • Exploit: Label new examples using the quality • Assign gold labels when benefit in learning better quality of worker outweighs the loss for labeling a gold (known label) example [Wang et al, WCBI 2011] • Assign an already labeled example (by other workers) and see if it agrees with majority [Donmez et al., KDD 2009] ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 74
  • GOLD TESTING No significant advantage under “good conditions” (balanced datasets, good worker quality) ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 75 10 labels per example 2 categories, 50/50 Quality range: 0.55:1.0 200 labelers
  • GOLD TESTING Advantage under imbalanced datasets ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 76 10 labels per example 2 categories, 90/10 Quality range: 0.55:0.1.0 200 labelers
  • GOLD TESTING Advantage with bad worker quality ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 77 5 labels per example 2 categories, 50/50 Quality range: 0.55:0.65 200 labelers
  • GOLD TESTING ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 78 10 labels per example 2 categories, 90/10 Quality range: 0.55:0.65 200 labelers Significant advantage under “bad conditions” (imbalanced datasets, bad worker quality)
  • QUALITY CONTROL ADDITIONAL HEURISTICS • Ask workers to rate the difficulty of a task • Let workers justify answers • Justification/feedback as quasi-captcha • Should be optional • Automatically verifying feedback was written by a person may be difficult (classic spam detection task) • Broken URL/incorrect object • Leave an outlier in the data set • Workers will tell you • If somebody answers “excellent” for a broken URL => probably spammer • Create cross-validating questions • E.g. workers says that picture does not contain people but tags somebody ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 79
  • QUALITY CONTROL DEALING WITH BAD WORKERS • Pay for “bad” work instead of rejecting it? • Pro: preserve reputation, admit if poor design at fault • Con: promote fraud, undermine approval rating system • Use bonus as incentive • Pay the minimum $0.01 and $0.01 for bonus • Better than rejecting a $0.02 task • If spammer “caught”, block from future tasks • May be easier to always pay, then block as needed ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 80
  • • Work of many non-experts can be aggregated to approximate the answer of an expert • However the competence and expertise of the workers do matter • E.g. knowledge intensive, domain specific tasks • Experts are better at [Chi2006] • generating better, faster and more accurate solutions • detecting features and deeper structures in problems • adding domain-specific and general constraints to problems • self monitoring and judging the difficulty of the task • choosing effective strategies • actively seeking information and resources to solve problems • retrieving domain knowledge with little cognitive effort. ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 81 TASK ROUTING
  • EXPERTISE DIMENSIONS • Knowledge • Implicit Knowledge (e.g. language, location) • Topical Knowledge (e.g. flowers, fashion) • Availability Reliability and Trustworthiness • Response time • Percentage of accepted microtask executions • Masters in AMT • Soft Skills • E.g. Attitude ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 82
  • STRATEGIES • Push : System controls the distribution of tasks • The worker is passive • Workers have strict preferences • Allocation • Worker’s expertise is known (or estimated). • A coalition is a group of agents which cooperate in order to achieve a common task. • (Coalition Problem). Given ⟨A, H, T ⟩, the coalition problem is to assign tasks t ∈ T to coalitions of agents C ⊆ A such that the total utility is maximized and the precedence order is respected. • Pull : Workers can browse, visualize & search data • The workers are active, and tend to choose tasks in which they have the most expertise, interest & understanding • Advantage • More effective on platform with high turn-over • Disadvantage • Coverage & completion time • More in “Advertisement” ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 83
  • FINDING THE RIGHT CROWD • Crowd selection by ranking the members of a social group according to the level of knowledge that they have about a given topic ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 84 [Bozzon2013b]
  • MAIN RESULTS • Profiles are less effective than level-1 resources • Resources produced by others help in describing each individual’s expertise • Twitter is the most effective social network for expertise matching – sometimes it outperforms the other social networks • Twitter most effective in Computer Engineering, Science, Technology & Games, Sport • Facebook effective in Locations, Sport, Movies & TV, Music • Linked-in never very helpful in locating expertise ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 85 Groundtruth created trough self-assessment. For expertise need, vote on 7 Likert Scale. EXPERTS  expertise above average
  • PICK-A-CROWD ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 86 PickVAVCrowd:#Tell#Me#What#You#Like,#and#I'll#Tell#You#What#to#Do Djellel Eddine Difallah, Gianluca Demartini, and Philippe Cudré-Mauroux. Pick-A-Crowd: Tell Me What You Like, and I'll Tell You What to Do. In: 22nd International Conference on World Wide Web (WWW 2013)
  • LIKE VS ACCURACY ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 87 Like%vs%Accuracy%
  • INCENTIVES ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 88 “money, love, or glory” T. W. Malone, R. Laubacher, and C. Dellarocas. Harnessing Crowds: Mapping the Genome of Collective Intelligence. Working paper no. 2009-001, MIT Center for Collective Intelligence, Feb. 2009. Sourcing
  • INCENTIVES INTRINSIC VS. EXTRINSIC People would prefer activities where they can pursue three things. • Autonomy: People want to have control over their work. • Mastery: People want to get better at what they do. • Purpose: People want to be part of something that is bigger than they are Intrinsic Motivations • Enjoyment, desire to help out, Extrinsic Motivations • Money, praise, promotion, preferment, the admiration of peers (social rewards), etc. Intrinsic motivations are typically more powerful than extrinsic ones, but the two classes have a strong interplay ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 89 SUGGESTED VIEW  http://www.ted.com/talks/dan_pink_on_motivation.html
  • MONETARY INCENTIVE VS. PERFORMANCE • “Rational choice” in economic theory: Rational workers will choose to improve their performance in response to a scheme that rewards such improvements with financial gain • Chocking effect • [Herzberg1987] financial incentives undermine actual performance e.g., hampering innovations • [Horton2010; Farber2008; Fehr2007] Workers may ignore rational incentives to work longer when they have accomplished pre-set targets • [Lazear200] Autoglass factory, install windshields • Switched from time-rate wage (pay per hour) to piece-rate wage (pay per unit) brought a 20% increase in productivity • Performance based pay scheme is a powerful tool for eliciting improved performance => but at what risk? • [Gneezy2000] [Heyman2004] Under certain circumstance the provision of financial incentives can undermine “intrinsic motivation” (e.g., enjoyment, altruism), possibly leading to poorer outcome ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 90
  • MONEY AND TROUBLE • No expectation of financial reward • effort motivated by other kinds of rewards • e.g. • social • non-profit SamaSource contracts workers refugee • Monetary compensation expected • the anticipated financial value of the effort will be the driving mechanism • Careful: Paying a little often worse than paying nothing! • Price commensurate with task effort • Ex: $0.02 for yes/no answer • Small pay now locks future pay • $0.02 bonus for optional feedback ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 91
  • MONEY AND TROUBLE • Payment replaces internal motivation (paying kids to collect donations decreased enthusiasm) • Lesson: Be the Tom Sawyer (“how I like painting the fence”), not the scrooge-y boss… • Paying a little: • No interest or slow response • Paying a lot: • People focus on the reward and not on the task • On MTurk spammers routinely attack highly-paying tasks ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 92
  • EXPERIMENT: WORD PUZZLE [MASON2005] • Want to further investigate payment incentives ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 93 * Shown a list of 15 possible words (not all of the words listed are in the puzzle) * Select a word: click the first and last letter (if correct, it will turn red) * Two wage models: quota vs. piece rate * Quota: every puzzle successfully completed * Piece: every word they found * Pay levels: low, medium, high, (no pay) -- Puzzle: $0.01, $0.05, $0.10 -- Word: $0.01, $0.02, $0.03
  • EXPERIMENT: WORD PUZZLE [MASON2005] • Payment incentives increase speed ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 94
  • EXPERIMENT: WORD PUZZLE [MASON2005] ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 95 Accuracy (fractionofworldsfoundperpuzzle) CostperWord No Contingent Pay Pay per Puzzle Pay per Word Accuracy Cost per word High accuracy per puzzle means low cost per word Low accuracy per puzzle, but workers find as many words as they can Intrinsic motivation (enjoyment)
  • INCENTIVES SOCIALIZATION AND PRESTIGE • Public credit contributes to sense of participation • Credit also a form of reputation • e.g. Leaderboards (“top participants”) frequent motivator [Farmer 2010] • Newcomers should have hope of reaching top • Should motivate correct behavior, not just measurable behavior • Whatever is measured, workers will optimize for this • Pro: • “free” • enjoyable for connecting with one another – can share infrastructure across tasks • Cons: • need infrastructure beyond simple micro-task – need critical mass (for uptake and reward • social engineering more complex than monetary incentive • Anonymity of MTurk-like settings discourage this factor ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 96
  • INCENTIVES ALTRUISM • Contributing back (tit for tat): Early reviewers writing reviews because read other useful review • Effect amplified in social networks: “If all my friends do it…” or “Since all my friends will see this…” • Contributing to shared goal • Help Others Who need knowledge (e.g. Freebase http://www.freebase.com/) • Help workers (e.g. http://samasource.org/) • Charity (e.g. http://freerice.com/) • Pro • “Free” • Can motivate workers for a cause • Cons • Small workforce ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 97
  • INCENTIVES PURPOSE OF WORK • Contrafreeloading: Rats and animals prefer to “earn” their food • Destroying work after production demotivates workers. [Ariely2008] • Showing result of “completed task” improves satisfaction • Workers enjoy learning new skills (often cited reason for Mturk participation) • Design tasks to be educational • DuoLingo: Translate while learning new language [vonAhn, duolingo.com] • Galaxy Zoo, Clickworkers: Classify astronomical objects [Raddick2010; http://en.wikipedia.org/wiki/Clickworkers] • On MTurk [Chandler2010] • Americans [older, more leisure-driven] work harder for “meaningful work” • Indians [more income-driven] were not affected • Quality unchanged for both groups ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 98
  • INCENTIVES FUN • Gamify the task (design details later) • Examples • ESP Game: Given an image, type the same word (generated image descriptions) • Phylo: aligned color blocks (used for genome alignment) • FoldIt: fold structures to optimize energy (protein folding) • Fun factors [Malone, 1982, 1980]: • timed response, • score keeping, • player skill level, • highscore lists, • and randomness ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 99
  • ADVERTISEMENT • Your task needs to be found! • Mechanical Turk UI is very primitive • Users constantly refresh the web page to find most recent HITs • Quality of description is paramount! • Clear title, useful keywords • Tricks needed in order to promote tasks • Workers pick tasks that have large number of HITs or are recent [Chilton2010] • VizWiz optimizations [Bingham2011] : • Posts HITs continuously (to be recent) • Makes big HIT groups (to be large) ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 100
  • EFFECT OF #HITS: MONOTONIC, BUT SUBLINEAR ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 101 • 10 HITs  2% slower than 1 HIT • 100 HITs  19% slower than 1 HIT • 1000 HITs  87% slower than 1 HIT or, 1 group of 1000  7 times faster than 1000 sequential groups of 1
  • REPUTATION MANAGEMENT • Word of mouth effect • Forums, alert systems • Trust • Pay on time? • Fair rejections? • Clear explanation if there is a rejection • Opportunity • Workers looks for good tasks (time vs. reward) • – Experiments tend to go faster – Announce forthcoming tasks (e.g. tweet) ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 102
  • OCCUPATIONAL HAZARDS • Costs of requesters and admin errors are often borne by workers • Defective HITs, too short time to finish, etc. • Worker’s rating can be affected due to such errors • Staying safe online: phishing, scamming • Some reports from Turker Nation: “Do not do any HITs that involve: secret shopping, ….; they are scams” • How to moderate such instances? (in a scalable way?) • Employers who don’t pay ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 103
  • HELPING WORKERS? • Augmenting M-Turk from the outside • Few external Turking tools • Building alternative human computation platforms? • Offering workers legal protections (human rights)? • Humans or machines? • Legal responsibilities? • Intellectual properties? • Offering fair wage? • Minimum wage? (or fair wage?) ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 104
  • ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 105 COMPLEX PROBLEMS
  • COMPLEX PROBLEMS • Sometimes the problem at hand is too complex to be managed by a single task • Examples: • Text transcription / summarization • Open descriptions • Dynamic planning • Need for orchestration of several tasks • Possibly performed with the help of humans ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 106
  • CONTROLS ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 107 True False
  • LOGICAL UNITS ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 108 Generate / Create Find Improve / Edit / Fix Vote for accept-reject Vote up, vote down, to generate rank Vote for best / select top-k Split task Aggregate Creation Quality Control Flow Control
  • EXAMPLE: FREE-FORM ANSWERS • Create-Vote pattern. Break task into two HITs • “Create” HIT • “Vote” HIT • Vote HIT controls quality of Creation HIT • Redundancy controls quality of Voting HIT • Note: If “creation” very good, workers just vote “yes” • Solution: Add some random noise (e.g. add typos) ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 109 Creation HIT (e.g. find a URL about a topic) Voting HIT: Correct or not? TurkIttoolkit[Littleetal.,UIST2010]: http://groups.csail.mit.edu/uid/turkit/
  • EXAMPLE: FREE-FORM ANSWERS • Create-Improve-Compare pattern. Break task into three HITs • “Create” HIT • “Improve” HIT • “Compare” HIT ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 110 Creation HIT (e.g. describe the image) Improve HIT (e.g. improve description) Compare HIT (voting) Which is better?
  • ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 111 version 1: A parial view of a pocket calculator together with some coins and a pen. version 2: A view of personal items a calculator, and some gold and copper coins, and a round tip pen, these are all pocket and wallet sized item used for business, writting, calculating prices or solving math problems and purchasing items. version 3: A close-up photograph of the following items: A CASIO multi-function calculator. A ball point pen, uncapped. Various coins, apparently European, both copper and gold. Seems to be a theme illustration for a brochure or document cover treating finance, probably personal finance. version 4: …Various British coins; two of £1 value, three of 20p value and one of 1p value. … version 8: “A close-up photograph of the following items: A CASIO multi-function, solar powered scientific calculator. A blue ball point pen with a blue rubber grip and the tip extended. Six British coins; two of £1 value, three of 20p value and one of 1p value. Seems to be a theme illustration for a brochure or document cover treating finance - probably personal finance."
  • EXAMPLE: SOYLENT • Word processor with crowd embedded [Bernstein2010] • “Proofread paper”: Ask workers to proofread each paragraph • Lazy Turker: Fixes the minimum possible (e.g., single typo) • Eager Beaver: Fixes way beyond the necessary but adds extra errors (e.g., inline suggestions on writing style) • Find-Fix-Verify pattern • Separate Find and Fix, does not allow Lazy Turker • Separate Fix-Verify ensured quality ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 112 http://www.youtube.com/watch?v=n_miZqsPwsc
  • ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 113 Soylent, a prototype... Independent agreement to identify patches Randomize order of suggestions
  • ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 114 HYBRID PROBLEMS
  • HYBRID PROBLEMS ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 115 More people Moremachines THE BIGGER PICTURE Machines using people e.g., human computation People using machines e.g., collective action Dave%de%Roure% David De Roure http://www.slideshare.net/davidderoure/social-machinesgss
  • KEY ISSUES • The role of machine (i.e., algorithm) and humans • use only humans? • both? • Who’s doing what? • Quality control • Optimization: What to crowdsource • Scalability: How much to crowdsource ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 116
  • EXAMPLE INTEGRATION WITH MACHINE LEARNING • Crowdsourcing is cheap but not free • Cannot scale to web without help • We need to know when and how to use machines along with humans • Solution • Build automatic classification models using crowdsourced data • Humans label training data • Use training data to build model ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 117
  • TRADE-OFF FOR MACHINE LEARNING MODELS • Get more data • Active Learning, select which unlabeled example to label [Settles, http://active-learning.net/] • Improve data quality • Repeated Labeling, label again an already labeled example [Sheng et al. 2008, Ipeirotis et al, 2010] ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 118
  • ITERATIVE TRAINING • Use model when confident, humans otherwise • Retrain with new human input => improve model => reduce need for humans ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 119
  • HOW OFTEN WE CAN REFER TO THE CROWD? • Interaction protocol • Upfront: Ask all the B queries at once • Iterative: Ask K queries to the crowd and use them to improve the system. Repeat this B/K times All Human Intelligent Tasks (HIT) are NOT equally difficult for the machine • Measures used for selection • Uncertainty: Asking hardest (most ambiguous) questions • Explorer: Ask questions with potential to have largest impact on the system ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 120
  • EXAMPLE HYBRID IMAGE SEARCH ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 121 Yan, Kumar, Ganesan, CrowdSearch: Exploi?ng Crowds for Accurate Real-?me Image Search on Mobile Phones, Mobisys 2010.
  • EXAMPLE HYBRID DATA INTEGRATION Generate Plausible Matches • Paper = title, paper = author, paper = email, paper = venue • Conf = title, conf = author, conf = email, conf = venue Ask Users to Verify ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 122 Not sure Schema Matching paper conf Data integration VLDB-01 Data mining SIGMOD-02 title author email OLAP Mike mike@a Social media Jane jane@b Generate plausible matches & ask users to verify – paper = title, paper = author, paper = email, paper = venue – conf = title, conf = author, conf = email, conf = venue paper conf Data integration VLDB-01 Data mining SIGMOD-02 title author email venue OLAP Mike mike@a ICDE-02 Social media Jane jane@b PODS-05 Does attribute paper match attribute author? NoYes McCann,Shen,Doan:MatchingSchemasin OnlineCommunities.ICDE,2008
  • EXAMPLE CROWDQ: CROWDSOURCED QUERY UNDERSTANDING • Understand the meaning of a keyword query • Build a structured (SPARQL) query template • Answer the query over Linked Open Data ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 123 Gianluca Demartini, Beth Trushkowsky, Tim Kraska, and Michael Franklin. CrowdQ: Crowdsourced Query Understanding. In: 6th Biennial Conference on Innovative Data Systems Research (CIDR 2013)` Indiana#Jones#–#Harrison#Ford# Back#to#the#Future#–#Michael#J.#Fox# Forrest#Gump#V#actors#
  • FRAMEWORKS PART 1 124 ICWE 2013 - An Introduction To Human Computation and Games With a Purpose
  • CROWD-SOURCING DB SYSTEMS How can crowds help databases? • Fix broken data • Entity Resolution, inconsistencies • Add missing data • Subjective comparison How can databases help crowd apps? • Lazy data acquisition • Game the workers market • Semi-automatically create user interfaces • Manage the data sourced from the crowd Existing systems  mainly academic • CrowdDB (Berkley, ETH) • Qurk (MIT) • Scoop (Stanford) ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 125
  • GENERIC ARCHITECTURE ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 126
  • CROWDDB GOAL: crowd-source comparisons, missing data • SQL with extensions to the DML and the Query Language ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 127
  • CROWDSQL SEMANTICS ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 128
  • ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 129
  • UI EXAMPLES ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 130 ll out the missing company data! Submit IBMName Headquarter address lloutthemissing professordata Submit Carey E-Mail Name Department ndaprofessor llinherdata Submit E-Mail Name Department ll out the missing professor data Submit CS Carey Department Name Email lloutthemissing companydata! Submit IBMName Headquarter address LargeSize lloutthemissing professordata Submit CS Carey Department Name Email lloutthemissing departmentdata Name Phone Submit add
  • CROWDDB USER INTERFACE VS. QUALITY ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 131 Professor Department name="Carey" p.dep=d.name lloutthemissing professordata Submit Carey E-Mail Name lloutthemissing departmentdata Submit CS Phone Department Name MTJoin (Dep) p.dep=d.name MTProbe (Professor) name=Carey Department lloutthemissing professordata Submit CS Carey Department name Name MTJoin (Professor) p.name="carey" MTProbe(Dep) E-Mail lloutthemissing professordata Submit Carey E-Mail Name MTProbe (Professor,Dep) name=Carey Department Department Phone lloutthemissing departmentdata Submit Phone Department Name
  • CROWDSEARCHER • Given that crowds spend times on social networks… • Why don’t use social networks and Q&A websites as additional human computation platforms? • Example: search task ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 132 [Bozzon2012][Bozzon2013b] Search Execution Engine HumanInteraction Management SE Access Interface Human Access Interface Query Interface Local Source Access Interface Social Networks Q&A Crowd- source platforms Query Answer http://crowdsearcher.search-computing.org
  • SEARCH ON SOCIAL NETWORKS ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 133 Embedded application Social/ Crowd platform Native behaviours External application Standalone application API Embedding Community / Crowd Generated query template Native
  • MULTI-PLATFORM DEPLOYMENT ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 134
  • MULTI-PLATFORM DEPLOYMENT ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 135
  • MULTI-PLATFORM DEPLOYMENT ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 136
  • MULTI-PLATFORM DEPLOYMENT ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 137
  • MODEL • Support for several types of task operations • Like, Comment, Tag, Classify, Add, Modify, Order, etc. • Several strategies for • Task splitting: the input data collection is too complex relative to the cognitive capabilities of users. • Task structuring: the query is too complex or too critical to be executed in one shot. • Task routing: a query can be distributed according to the values of some attribute of the collection • Output aggregation • Platform/community assignment • a task can be assigned to different communities or social platforms based on its focus ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 138
  • REACTIVE CONTROL • Controlling crowdsourcing tasks is a fundamental issue • Cost • Time • Quality • A conceptual framework for modeling crowdsourcing computations and control requirements • Reactive Control Design • Active Rule programming framework • Declarative rule language • A reactive execution environment for requirement enforcement and reactive execution ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 139
  • RULE EXAMPLE ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 140
  • RULE EXAMPLE ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 141
  • RULE EXAMPLE ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 142
  • RULE EXAMPLE ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 143
  • RULE EXAMPLE ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 144
  • RULE EXAMPLE ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 145
  • WORKFLOWS WITH MECHANICAL TURK ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 146 HIT HIT HIT HIT HIT HIT Data Collected in CSV File Requester posts HIT Groups to Mechanical Turk Data Exported for Use
  • CROWDFORGE Map-Reduce framework for crowds [Kittur et al, CHI 2011] ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 147 My Boss is a Robot (mybossisarobot.com), Nikki Kittur (CMU) + Jim Giles (New Scientist) • Easy to run simple, parallelized tasks. • Not so easy to run tasks in which turkers improve on or validate each others’ work.
  • CROWDWEAVER [Kittur et al. 2012] ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 148 CrowdWeaver: Visually Managing Complex Crowd Wor Aniket Kittur, Susheel Khamkar, Paul André, Robert E. Kraut Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA, 15213 {nkittur, pandre, kraut}@cs.cmu.edu, susheelkhamkar2@gmail.com gure 1. The CrowdWeaver workflow management interface. (A) The workflow consisting of human tasks ( ), e.g., “create (n eads)”, and machine tasks (e.g., divide, permute). (B) The Task Summary pane details the selected task, with the “news lead” fiel
  • TURKOMATIC • Crowd creates workflows [Kalkani et al, CHI 2011]: • Turkomatic interface accepts task requests written in natural language • Ask workers to decompose task into steps (Map) • Can step be completed within 10 minutes? • Yes: solve it. • No: decompose further (recursion) • Given all partial solutions, solve big problem (Reduce) ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 149
  • DECOMPOSITION ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 150
  • EVALUATION • Tasks: • Producing a written essay in response to a prompt: “please write a five-paragraph essay on the topic of your choice” • Solving an example SAT test “Please solve the 16- question SAT located at http://bit.ly/SATexam” • Payment: $0.10 to $0.40 per HIT • Each “subdivide” or “merge” HIT received answers within 4 hours; solutions to the initial task were completed within 72 hours • Essay: the final essay (about “university legacy admissions”) displayed a reasonably good understanding of a topic; yet the writing quality is often mixed • SAT: the task was divided into 12 subtasks (containing 1-3 questions); the score was 12/17 ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 151
  • TURKIT Human Computation Algorithms on Mechanical Turk [Little2010] • Arrows indicate the flow of information. • Programmer writes 2 sets of source code: • HTML files for web servers • JavaScript executed by TurKit • Output is retrieved via a JavaScript database. • TurKit: Java using Rhino to interpret JavaScript code, and E4X2 to handle XML results from MTurk • IDE: Google App Engine3 (GAE) ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 152
  • CRASH-AND-RERUN PROGRAMMING MODEL • Observation: local computation is cheap, but the external class cost money • Managing states over a long running program is challenging • Examples: Computer restarts? Errors? • Solution: store states in the database (in case) • If an error happens, just crash the program and re-run by following the history in DB • Throw a “crash” exception; the script is automatically re-run. • New keyword “once”: • Remove non-determinism • Don’t need to re-execute an expensive operation (when re- run) ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 153
  • EXAMPLE: QUICK SORT ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 154
  • CROWD-POWERED SEARCH • Users ask questions on Twitter • An hybrid system provide answers • Workers used for • label tweets as “rhetorical question” or not • Median 3.02 mins • produce responses to question • Median 77.4 mins • Voting responses • Median 82.1 mis ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 155 A Crowd-Powered Socially Embedded Search Engine. Jin-Woo Jeong, Meredith Ringel Morris, Jaime Teevan, Daniel Liebling. ICWSM 2013 Median time =>162.5 minutes Cost => $0.95 per tweet
  • FUTURE PART 1 156 ICWE 2013 - An Introduction To Human Computation and Games With a Purpose
  • WHAT LIES AHEAD OF US? What would it take for us to be proud of our children growing up to be crowd workers*? *any work that could be sent down a wire • Ethics, entangled with methods and tools • Should workers be treated as undifferentiated and discardable? • Should requesters be viewed as distant and wielding incredible power to deny payment or harm reputations? • Work is complex, creative, and interdependent • Could a crowd compose a symphony? ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 157 A.Kittur et al. The future of crowd work. CSCW '13. ACM, New York, NY, USA, 1301-1318.
  • A FRAMEWORK FOR IMPROVEMENT ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 158
  • IMPROVE WORKER EXPERIENCE • Reputation system for workers • More than financial incentives • Education? Recognition? Status? • Recognize worker potential (badges) • Paid for their expertise • Steering User Behavior with Badges [WWW2013] • Train less skilled workers (tutoring system) • Can we facilitate this process and deliver work suited to the person’s expertise, all the way along that process? ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 159
  • IMPROVE WORK • Promote workers to management roles • Create gold labels • Manage other workers • Make task design suggestions (first-pass validation) • Career trajectory (based on reputation): 1. Untrusted worker 2. Trusted worker 3. Hourly contractor 4. Employee ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 160
  • IMPROVE WORK TASK RECOMMENDATION • Content-based recommendation • Find similarities between worker profile and task characteristics • Collaborative Filtering • Make use of preference information about tasks (e.g. ratings) to infer similarities between workers • Hybrid • A mix of both ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 161
  • IMPROVE PLATFORMS • What is a platform? • Know your crowd: Model workers • Work-flows • Enforce Quality • Ubiquitous crowdsourcing ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 162
  • HOW TO BUILD SOCIAL SYSTEMS AT SCALE? ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 163 More people Moremachines OPEN QUESTION: HOW TO BUILD SOCIAL SYSTEMS AT SCALE? Big Data Big Compute Conventional Computation The Future! Social Networking e-infrastructure online R&D Dave%de%Roure% Social Machines!! David Roure http://www.slideshare.net/davidderoure/social-machinesgss
  • REFERENCES 164 ICWE 2013 - An Introduction To Human Computation and Games With a Purpose
  • PAPERS • [Grier2005] When Computers Were Human” • [Turing1950] http://www.loebner.net/Prizef/TuringArticle.html • [VonAhn2005] A.M. Turing. Computing Machinery and Intelligence. http://reports- archive.adm.cs.cmu.edu/anon/2005/abstracts/05-193.html • [Mason2009] Winter Mason and Duncan J. Watts. 2009. Financial incentives and the "performance of crowds". In Proceedings of the ACM SIGKDD Workshop on Human Computation (HCOMP '09), ACM, New York, NY, USA, 77-85. • [Lazear200] Lazear, E. P. Performance pay and productivity. American Economic Review, 90, 5 (Dec 2000), 1346-1361. • [Gneezy2000]Gneezy, U. and Rustichini, A. Pay enough or don't pay at all. Q. J. Econ., 115, 3 2000), 791-810. [Heyman2004] Heyman, J. and Ariely, D. Effort for Payment: A Tale of Two Markets. Psychological Science, 15, 11 2004), 787-793. • [Herzberg1987] Herzberg, F. One More Time: How do You Motivate Employees? Harvard Business ReviewSeptember-October, 1987), 5-16. • [Kittur2013] Aniket Kittur, Jeffrey V. Nickerson, Michael Bernstein, Elizabeth Gerber, Aaron Shaw, John Zimmerman, Matt Lease, and John Horton. 2013. The future of crowd work. In Proceedings of the 2013 conference on Computer supported cooperative work (CSCW '13). ACM, New York, NY, USA, 1301-1318. ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 165
  • PAPERS • [Farber2008] Farber. Reference-dependent preferences and labor supply: The case of New York City taxi drivers. American Economic Review, 2008. • [Fehr2007] Fehr and Goette. Do workers work more if wages are high?: Evidence from a randomized field experiment. American Economic Review, 2007 • [Chandler2010] Chandler and Kepelner, Breaking Monotony with Meaning: Motivation in Crowdsourcing Markets , 2010 • [Farmer2010] Farmer and Glass, Building Web Reputation Systems, O’Reilly 2010 • [Horton2010] Horton and Chilton: The labor economics of paid crowdsourcing. EC 2010 • [Quinn2011] Alexander J. Quinn and Benjamin B. Bederson. 2011. Human computation: a survey and taxonomy of a growing field. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '11). ACM, New York, NY, USA, 1403-1412. • [Snow2008] Snow, Rion and O'Connor, Brendan and Jurafsky, Daniel and Ng, Andrew. Cheap and Fast -- But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks, Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, October 2008, Honolulu, Hawaii. • [Chi2006] M.Chi. Two approaches to the study of experts’ characteristics. In K.A.Ericsson,N.Charness, P. J. Feltovich, and R. R. Hoffman, editors, The Cambridge handbook of expertise and expert performance, pages 21–30. Cambridge University Press, 2006. Cited on page(s) 35 ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 166
  • PAPERS • [Kittur2012] Aniket Kittur, Susheel Khamkar, Paul André, and Robert Kraut. 2012. CrowdWeaver: visually managing complex crowd work. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work (CSCW '12). ACM, New York, NY, USA, 1033-1036. • [ImageNet] http://www.image-net.org/about-publication • Mason and Watts, Financial Incentives and the “Performance of Crowds”, HCOMP 2009 • Yan, Kumar, Ganesan, “CrowdSearch: Exploiting Crowds for Accurate Real-time Image Search on Mobile Phones”, MobiSys 2010 • Ipeirotis, Analyzing the Mechanical Turk Marketplace, XRDS 2010 • Wang, Faridani, Ipeirotis, Estimating Completion Time for Crowdsourced Tasks Using Survival Analysis Models. CSDM 2010 • Chilton et al, Task search in a human computation market, HCOMP 2010 • Bingham et al, VizWiz: nearly real-time answers to visual questions, UIST 2011 • Horton and Chilton: The labor economics of paid crowdsourcing. EC 2010 ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 167
  • PAPERS • Huang et al., Toward Automatic Task Design: A Progress Report, HCOMP 2010 • Quinn, Bederson, Yeh, Lin.: CrowdFlow: Integrating Machine Learning with Mechanical Turk for Speed-Cost-Quality Flexibility • Parameswaran et al.: Human-assisted Graph Search: It's Okay to Ask Questions, VLDB 2011 • Mitzenmacher, An introduction to human-guided search, XRDS 2010 • Marcus et al, Crowdsourced Databases: Query Processing with People, CIDR 2011 • Raykar, Yu, Zhao, Valadez, Florin, Bogoni, and Moy. Learning from crowds. JMLR 2010. • Mason and Watts, Collective problem solving in networks, 2011 • Dellarocas, Dini and Spagnolo. Designing Reputation Mechanisms. Chapter 18 in Handbook of Procurement, Cambridge University Press, 2007 ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 168
  • TUTORIALS • Ipeirotis (WWW2011) • http://www.slideshare.net/ipeirotis/managing-crowdsourced-human-computation • Omar Alonso, Matthew Lease (SIGIR 2011) • http://www.slideshare.net/mattlease/crowdsourcing-for-information-retrieval- principles-methods-and-application • Omar Alonso, Matthew Lease (WSDM 2011) • http://ir.ischool.utexas.edu/wsdm2011_tutorial.pdf • Gianluca Demartini, Elena Simperl, Maribel Acosta (ESWC2013) • https://sites.google.com/site/crowdsourcingtutorial/ • Bob Carpenter and Massimo Poesio • http://lingpipe-blog.com/2010/05/17/lrec-2010-tutorial-modeling-data-annotation/ ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 169
  • TUTORIALS • Bob Carpenter and Massimo Poesio • http://lingpipe-blog.com/2010/05/17/lrec-2010-tutorial-modeling-data-annotation/ • Omar Alonso • http://wwwcsif.cs.ucdavis.edu/~alonsoom/crowdsourcing.html • Alex Sorokin and Fei-Fei Li • http://sites.google.com/site/turkforvision/ • Daniel Rose • http://videolectures.net/cikm08_rose_cfre/ • A. Doan, M. J. Franklin, D. Kossmann, T. Kraska (VLDB 2011) • Crowdsourcing Applications and Platforms: A Data Management Perspective. • List by Matt Lease http://ir.ischool.utexas.edu/crowd/ ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 170
  • BLOGS AND ONLINE RESOURCES • [B_Tibbets2011] http://soa.sys-con.com/node/1996041 • [B_Wired2006] http://www.wired.com/wired/archive/14.06/crowds.html • [B_NextWeb2013] http://thenextweb.com/shareables/2013/01/16/verizon-finds- developer-outsourced-his-work-to-china-so-he-could-surf- reddit-and-watch-cat-videos/ • [B_Chronicle2009] http://chronicle.com/blogs/wiredcampus/duke-professor-uses- crowdsourcing-to-grade/7538 • [B_DemoTurk] http://behind-the-enemy- lines.blogspot.com/2010/03/new-demographics-of-mechanical- turk.html ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 171
  • BOOKS, COURSES, AND SURVEYS • E. Law and L. von Ahn. Human Computation. Morgan & Claypool Synthesis Lectures on Artificial Intelligence and Machine Learning, 2011 • http://www.morganclaypool.com/toc/aim/1/1 • S.Ceri, A.Bozzon, M.Brambilla, P.Fraternali, S.Quarteroni. Web Information Retrieval. Springer. • Omar Alonso, Gabriella Kazai, and Stefano Mizzaro. Crowdsourcing for Search Engine Evaluation: Why and How. • To be published by Springer, 2011. • Deepak Ganesan. CS691CS: Crowdsourcing - Opportunities & Challenges (Fall 2010). UMass Amherst • http://www.cs.umass.edu/~dganesan/courses/fall10 ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 172 Credits: Matt Lease http://ir.ischool.utexas.edu/crowd/
  • BOOKS, COURSES, AND SURVEYS • Matt Lease. CS395T/INF385T: Crowdsourcing: Theory, Methods, and Applications (Spring 2011). UT Austin. • http://courses.ischool.utexas.edu/Lease_Matt/2011/Spring/IN F385T • Yuen, Chen, King: A Survey of Human Computation Systems, SCA 2009 • Quinn, Bederson: A Taxonomy of Distributed Human Computation, CHI 2011 • Doan, Ramakrishnan, Halevy: Crowdsourcing Systems on the World-Wide Web, CACM 2011 • Uichin Lee • http://mslab.kaist.ac.kr/twiki/bin/view/CrowdSourcing/ • ̂me Waldispühl, McGill University • http://www.cs.mcgill.ca/~jeromew/comp766/ ICWE 2013 - An Introduction To Human Computation and Games With a Purpose 173