Crowdsourcing in Enterprise Environments

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1 Crowdsourcing in Enterprise Environments Dr. ir. Alessandro Bozzon Delft University of Technology Web Information Systems Delft Social Data Science Lab Kick-off KIVI Leerstoel Big Data Science Den Haag - June 10th 2015

2 My Why

3 Complex Systems Created By People Populated by people Kowloon Walled City, Hong Kong

4 Complex Systems Created By People Driven by people

5 Intelligent = IT Prescriptive, centralised design The Environment should fit the software Data should fit the software Users should fit the software Computing science => Efficiency Efficient Software => Efficient Systems Credits: Geert-Jan Houben, Dies Natalis 2015, Tu Delft

6 A Web-driven Cultural Shift Decentralisation Openness & Linking Personalisation Adaptation Credits:

7 Intelligent = Data Data Machines Scale Speed Sustainability

8 Intelligent = Data Semantics Data Machines Scale Speed Sustainability Semantics

9 Intelligent = Social Data Semantics People Understanding Create Analyse Interpret Engage & Retain Data Describe People Machines Semantics

10 Social Data Science From the people By the people For the people Creation Implicit Vs. Explicit Organically Vs. On Demand Sources Mobile Phones Social Media (Personal) Sensors Annotations To train machines Analysis When machine cannot Interpretation Culture, Context Multiple Domains Knowledge Generation Well-being City Life Enterprise In The Real World The World is My Lab HCI Network Analysis Sociology Cognitive Psychology Knowledge Discovery Data Mining Behavioural Economics Collective Intelligence Security & Privacy Software Engineering Domain Specific Expertise

11 Scientific Challenge How can humans and machines better collaborate in computation problems?

12 Takeaway Message More Machines - Scalability Big Data Big Computation Conventional Computation Social Computers Human Computation More People - Human Intelligence

13 Societal Challenges How will technology influence the creation and distribution of wealth and well-being? What will be the role of humans in the (near) future?

14 Crowdsourcing

15 The global opportunity in online outsourcing. June The World Bank The contracting of third-party workers and providers to supply services or perform tasks via Internet-based marketplaces or platforms.

16 Workers Infrastructure Payment Clients Online Outsourcing Firms

17 Crowdsourcing Types Microwork Online Freelancing Task of seconds/minutes Basic literacy and numeracy skills High Availability Fast Response Times Contract Services Task can take days/weeks Technical or Professional Skills More similar to traditional outsourcing

18 Crowdsourcing Market Million Workers 2B Gross Service Revenue B Gross Service Revenue B Gross Service Revenue Microwork => driven mainly by medium/large enterprises Online Freelancing => small/medium enterprises Workers are mainly millenials Educated (or being educated) Driven by income generation

19 Crowdsourcing and Data Science

20 Completely Automated Public Turing Test To Tell Computers and Humans Apart Luis von Ahn, Manuel Blum, Nicholas J. Hopper, and John Langford. Eurocrypt, 2000

21 100 million CAPTCHAs every day 100 million users typing words How can this insane amount of work be exploited?

22 Does it work? As of 2012, thirty years of The New York Times had been digitalised

23 Which data (creation / analysis / interpretation) tasks cannot satisfactorily performed by computers (yet)?

24 Object Detection, Recognition, Identification Ordering Image Annotation Clustering

25 Emulating Human Computers 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 Computer scientists (in the artificial intelligence field) have been trying to emulate human abilities Language Visual processing Reasoning Now we need humans again for AI-complete tasks

26 The Human Advantage Electronic Fast Determinist Arithmetic Human Slow Inconsistent/Noisy But better at Perception, Preference / Aesthetic Judgment,Creativity, Emotions

27 Algorithm INPUT OUTPUT Human Computation Computation performed by humans to help computers executed computational tasks they cannot efficiently and/or effectively solve yet

28 Applications in Data Science Information Extraction Schema Matching Entity Resolution Data spaces Building structured KBs Sorting Top-k Graph Search Mining and Classification Social Media Analysis NLP Text Summarisation Sentiment Analysis Search

29 A vision of Social Computers Humans as First Class Computational Units How to employ human computational resources efficiently and effectively to accommodate large volumes of heterogeneous data with variable quality?

30 What How hard is the problem? Is it efficiently solvable? Trade-off between human versus machine? How Who Is the human computation algorithm correct and efficient? How do to coordinate the work of many human computers? More machines Machines using people e.g., human computation People using machines e.g., collective action To whom do we route each task, and how? How to motivate participation, and incentive truthful outputs? More people

31 Infrastructures for efficient and effective hybrid data management systems Challenge Crowdsearcher Framework (with API) Query Answer Specification paradigm SE Access Interface Query Interface Search Execution Engine Local Source Access Interface Human Access Interface Human Interaction Management Social Networks Q&A Crowdsource platforms Reactive execution and control environment Hybrid computation flows Crowds from heterogeneous systems US PATENT US B2 - Method and system of management of queries for crowd searching

32 Pervasive Crowdsourcing Vision Perform tasks every time everywhere

33 Experts retrieval for knowledge-intensive data creation, analysis and interpretation tasks Challenge Novel metrics and strategies for expertise retrieval, assessment, creation

34 Bias / Veracity Challenge Social Data is nuanced by culture, context, background uncertain in expression and content inconsistent, Ambiguous, Deceptive Lack of Veracity is a challenge Hampers reliability of analysis Supports wrong interpretations But often it is an opportunity Reality can be perceived different ways Bias and diversity can be desirable data properties

35 Systematically create capacity for long-running human data management activities Challenge Workers Work Novel metrics and strategies for (crowd) engagement and retainment Sourcing

36 Veracity By Design Social'Data'Source Vision Crowd Creation Analysis Interpretation Task Modeling Crowd Modeling Skills Expertise Availability Sense5 making Personality Knowledge need Routing The$right crowd Workflow Modeling Control3& Optimization Money Fun Glory Duty Motivations Knowledge

37 Crowdsourcing in the Enterprise

38

39 How Can Crowdsourcing and Human Computation be Employed in your Company?

40 Examples Launching innovation activities Developing / Testing Software IT Inventory Management and Support Develop Business Strategies Assessing resumes of Job Candidates Support document and translation management E-commerce and the Internet Advertising and PR

41 Cultural Heritage Annotation Identification and engagement with niches of people with the right expertise for content annotation

42 Intelligent Cities www://social-glass.org Provide actionable insights about people in urban environments Offer extensible social sensing and social interaction tools Targets Urban phenomena Mobility Environment Social City Life

43 Enterprise (Social) Networks Professional Social Networks Enterprise Directory Enterprise Social Networks Personal Social Networks Enterprise Blogs Personal Blogs Communications Enterprise Q&A Examples of Applications Our study Expertise Elicitation and Retrieval Discovery of (latent) Relationship Networks Topical Bus Factor Environmental Sensing Source: Bozzon, Efstathiades, Houben, Sips. A study of the online profile of enterprise users in professional social networks. WWW 2014.

44 Employee Engagement Only 30% of US workforce is engaged in their work Learning Goal Foster positive behaviour in employees to achieve important business needs Social online interaction Spread awareness outside the company Sense of Belonging

45 Next?

46 Research Agenda Goal Employ human computational resources efficiently and effectively to accommodate large volumes of heterogeneous data with variable quality What? Novel methods and tools for Social Data Science User Modelling (expertise, hard-skills, soft-skills) Hybrid Computation Systems (combine machines and humans) User Engagement (motivation and incentives) Crowd Sensing How? Empirical, data-driven research Strong focus on value creation (e.g. prototypes)

47 Help us improving the state-of-the-art Real-world uses cases: Difficult data analysis problems (at scale) Data sense-making problems (at scale) Domain Expertise, to Develop new models Create optimised methods

48 Together Advance the state-of-the-art Create societal and business impact Educate the next generation of engineers

49

50 Contact Dr. ir. Alessandro Bozzon Web : Mail :

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