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Learning and Incentives in Crowd-Powered Systems Research Collection Doctoral Thesis Learning and Incentives in Crowd-Powered Systems Author(s): Singla, Adish Publication Date: 2016 Permanent Link: https://doi.org/10.3929/ethz-a-010867044 Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library DISS. ETH N◦ 24034 Learning and Incentives in Crowd-Powered Systems A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich) presented by ADISH KUMAR SINGLA M. Sc. in Computer Science, EPFL, Switzerland born on 12.03.1984 citizen of India accepted on the recommendation of Prof. Dr. Andreas Krause (ETH Zurich), examiner Prof. Dr. Donald Kossmann (ETH Zurich), co-examiner Prof. Dr. Carla Gomes (Cornell University), co-examiner Dr. Eric Horvitz (Microsoft Research), co-examiner Prof. Dr. David Parkes (Harvard University), co-examiner 2016 Abstract The ongoing technological revolution—fueled by the Internet, mobile computing, and advances in AI—is leading to deep-seated economic and societal changes, and funda- mentally altering our lives in unprecedented ways. An underlying theme of this rev- olution is that the users are increasingly becoming an integral part of computational systems; prominent examples include community-driven services like Airbnb, shared mobility based bike sharing systems, citizen science projects like eBird, community- sensing applications like Waze, online tutoring systems like Coursera, and many more. These emerging crowd-powered systems are opening up exciting new opportunities for industry and science by providing the ability to harness the knowledge/expertise of people at scale and by collecting/analyzing the unprecedented volume of users’ data. However, the machine learning algorithms and AI techniques behind these crowd- powered systems are increasingly interacting with and seeking information from peo- ple. This raises fundamentally new issues and technical challenges including con- cerns about users’ privacy, incentivizing users, and the robustness of the deployed algorithms against the intricacies of human behavior (e.g., users acting strategically). Furthermore, these systems typically face various operational challenges on a daily basis—for instance, imbalance of the bikes across stations in bike sharing systems and the huge disproportion of the revenue across hosts in services like Airbnb—as their users act primarily in their own self-interest. In this dissertation, we develop novel techniques at the interplay of learning and incen- tives to tackle some of the above-mentioned challenges with the goal of improving the overall effectiveness of crowd-powered systems. Part II of this dissertation explores the first facet of this interplay—Learning about Incentives—where we develop new online learning mechanisms using the approach of regret minimization for learning about users’ preferences and offering them optimized incentives. In Part III, we study the i aspect of Incentives for Learning, in particular for data collection, exploring the role of designing/engineering the incentives in crowd-powered systems for incentivizing users to solving complex sensing and computation tasks. We design a new class of privacy-aware truthful mechanisms suitable for a wide range of sensing applications dealing with information acquisition from strategic agents under uncertainty and en- joying provable guarantees on the acquired utility. Finally, Part IV of this dissertation tightly couples this interplay and explores Learning as an Incentive, motivated by ap- plications to educating volunteers in citizen science. We introduce a new stochastic model of human learners and then develop a novel teaching algorithm that selects a sequence of training examples to the human learners in order to steer them towards the true hypothesis. We theoretically analyze our approach, proving strong approxima- tion guarantees, and thereby providing—one of the first—teaching complexity results for a practically applicable approach towards teaching the crowd. As part of the research carried out in this dissertation, we collaborate with various industrial/academic partners and interact directly with their end users (via conduct- ing surveys, running online experiments, or deploying smartphone apps). Through this process, we gain deep insights into real-world problems, a key towards develop- ing practically applicable techniques. In this dissertation, we apply our techniques across three important application domains presented in three different parts of this dissertation. Part II tackles the imbalance problem in bike sharing systems by de- veloping a new crowdsourcing scheme to incentivize users in the bike repositioning process (in collaboration with a bike sharing company in Zurich, Switzerland and a public transport company in Mainz, Germany). In Part III, we develop practical so- lutions for community-based air-quality sensing (in the context of OpenSense, a Swiss nationwide project). In Part IV, we present our results on teaching participants on a crowdsourcing platform for different tasks of classifying animal species with applica- tions to biodiversity monitoring via citizen science. By grounding our work in three prototypical real-world applications, this dissertation explores several fundamental challenges that are prevalent in the ongoing technolog- ical revolution: incentivizing users in the era of the sharing economy, ensuring the privacy of users in the digital world, and designing personalized teaching policies for e-education. A central theme of our research is empowering users and actively en- gaging them to contribute to the system, with a goal towards building self-sustainable and intelligent crowd-powered systems. ii Zusammenfassung Die digitale Revolution, basierend auf dem Internet, Smartphones, und Fortschritten in der kunstlichen¨ Intelligenz, fuhrt¨ zu tiefgreifenden wirtschaftlichen und soziolo- gischen Anderungen¨ im Lebensalltag. Ein zugrundeliegendes Leitmotiv dieser Revo- lution ist, dass Endbenutzer verstarkt¨ in die digitalen Plattformen eingebunden wer- den; wichtige Beispiele sind gemeinschaftsbasierte Plattformen wie Airbnb, geteilte Mobilitatssysteme¨ wie Fahrradverleih-Dienste, wissenschaftliche Burgerbeteiligungen¨ wie eBird, gemeinschaftliche Messanwendungen wie Waze, online Ausbildungsplat- tformen wie Coursera, und viele weitere. Diese neu entstehenden crowd-basierten Systeme eroffnen¨ neue Moglichkeiten¨ fur¨ Industrie und Forschung, indem sie Zugang zum Wissen von einer Vielzahl an Menschen schaffen und es ermoglichen,¨ noch nie dagewesene Mengen an Nutzerdaten zu sammeln und zu analysieren. Die zugrundeliegenden Lernalgorithmen und Methoden fur¨ kunstliche¨ Intelligenz in- teragieren zunehmend mit Menschen, um Daten und Informationen zu sammeln. Dies fuhrt¨ zu neuen, fundamentalen ethischen Fragen und technischen Problemen, beispiel- sweise betreffend der Privatsphare¨ der NutzerInnen, Anreizsystemen fur¨ NutzerIn- nen, und die Robustheit der Algorithmen in Bezug auf die Besonderheiten des men- schlichen Verhaltens (z.B. strategisches Verhalten). Daruber¨ hinaus entstehen viele betriebsbedingte Herausforderungen wenn NutzerInnen nur in ihrem eigenen Inter- esse handeln. Beispiele sind eine unausgeglichene Verteilung von Fahrradern¨ uber¨ die einzelnen Stationen von Fahrradverleih-Diensten und die unverhaltnism¨ assige¨ Verteilung von Gewinnen fur¨ Gastgeber auf Plattformen wie Airbnb. In dieser Dissertation entwickeln wir neue Methoden am Schnittpunkt zwischen dem Lernen und Anreizen, um die zuvor beschriebenen Herausforderungen zu losen¨ und die Effektivitat¨ von crowd-basierten Systemen zu verbessern. Teil II dieser Disser- tation beschaftigt¨ sich mit dem ersten Aspekt dieser Interaktion, dem Lernen von iii Anreizen. Dazu entwickeln wir neue online Lernmethoden, basierend auf Methoden fur¨ ‘regret minimization‘, um Nutzerpraferenzen¨ zu lernen und den NutzerInnen op- timierte Anreize anzubieten. In Teil III beschaftigen¨ wir uns mit Anreizen fur¨ das Lernen, vor allem fur¨ das Sammeln von Daten. Wir erforschen die Rolle von An- reizen in crowd-Systemen um NutzerInnen zur Losung¨ von komplexen Mess- und Rechenaufgaben zu motivieren. Wir entwickeln eine neue Klasse von Algorithmen, die auf der einen Seite die Privatsphare¨ von NutzernInnen schutzen¨ und anderer- seits Garantien fur¨ die Qualitat¨ der bestimmten Losung¨ besitzen, und fur¨ eine grosse Spanne von Messapplikationen mit strategisch ausgewahlten¨ Nutzern und Modellun- sicherheiten angewendet werden konnen.¨ Teil IV dieser Arbeit koppelt die beiden Anwendungen und beschaftigt¨ sich mit dem Thema Lernen als Anreiz, motiviert durch Anwendungen zur Ausbildung von Nutzern in wissenschaftlichen Burgerbeteiligun-¨ gen. Wir entwickeln ein neues stochastisches Modell fur¨ menschliche Lerner und en- twickeln einen Lehralgorithmus, der eine Reihe von Lehrbeispielen auswahlt¨ um den Nutzer effektiv zur richtigen Losung¨ zu fuhren.¨ Wir analysieren diese Methode und beweisen starke Garantien fur¨ die Qualitat¨ der Naherungsl¨ osungen.¨ Dadurch erhalten wir eines der ersten Ergebnisse bezuglich¨ der Komplexitat¨ des Lehrens fur¨ praktisch anwendbare Ansatze¨ von Lehralgorithmen. Als Teil der Forschung in dieser Dissertation kollaborieren wir mit verschiedenen in- dustriellen und akademischen Partnern und interagieren direkt mit Endbenutzern durch Umfragen, online Experimente, oder Applikationen fur¨
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