Fairness and Transparency in Crowdsourcing

International audienceDespite the success of crowdsourcing, the question of ethics has not yet been addressed in its entirety. Existing efforts have studied fairness in worker compensation and in helping requesters detect malevolent workers. In this paper, we propose fairness axioms that generalize existing work and pave the way to studying fairness for task assignment, task completion, and worker compensation. Transparency on the other hand, has been addressed with the development of plug-ins and forums to track workers' performance and rate requesters. Similarly to fairness, we define transparency axioms and advocate the need to address it in a holistic manner by providing declarative specifications. We also discuss how fairness and transparency could be enforced and evaluated in a crowdsourcing platform

Pay Fairness: Percepsi, Anteseden, dan Konsekuensi

Various factors have caused Pay Fairness to become a much more important and challenging issue for reward leaders in recent years. Rewards equity and constructs related to fairness and salary equity are closely related to employee attitudes and behavior. This paper is a review of articles from international journals on payment equity. Justice in this paper is taken from the word’s fairness, justice, and equity, while the opposite, injustice, is taken from unfairness, injustice, and inequity. The purpose of this paper is to obtain one of the variables, along with the indicators, which will be used in research on Pay Fairness. Apart from perceptions, antecedents, and consequences, there are also several theories and grand theories referred to in assessing the fairness of payments. The results show that the antecedents of fairness of payments are efforts to close gender gaps, change attitudes towards transparency, increase workforce diversity, increase access to information, and sensitivity to employee preferences. The consequences of pay transparency are divided into 1) positive, such as increasing motivation, involvement, loyalty, and understanding how payments are determined and knowing how to maximize payments; and 2) negative, such as creating jealousy and conflict, dishonesty, perceptions of injustice, and loss of privac

Reputation Agent: Prompting Fair Reviews in Gig Markets

Our study presents a new tool, Reputation Agent, to promote fairer reviews from requesters (employers or customers) on gig markets. Unfair reviews, created when requesters consider factors outside of a worker's control, are known to plague gig workers and can result in lost job opportunities and even termination from the marketplace. Our tool leverages machine learning to implement an intelligent interface that: (1) uses deep learning to automatically detect when an individual has included unfair factors into her review (factors outside the worker's control per the policies of the market); and (2) prompts the individual to reconsider her review if she has incorporated unfair factors. To study the effectiveness of Reputation Agent, we conducted a controlled experiment over different gig markets. Our experiment illustrates that across markets, Reputation Agent, in contrast with traditional approaches, motivates requesters to review gig workers' performance more fairly. We discuss how tools that bring more transparency to employers about the policies of a gig market can help build empathy thus resulting in reasoned discussions around potential injustices towards workers generated by these interfaces. Our vision is that with tools that promote truth and transparency we can bring fairer treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202

クラウドソーシングにおける参加率と活躍度を考慮したタスク割当て手法

筑波大学修士(情報学)学位論文・平成31年3月25日授与(41281号

The Dark Side of Recruitment in Crowdsourcing: Ethics and Transparency in Micro-Task Marketplaces

Micro-task crowdsourcing marketplaces like Figure Eight (F8) connect a large pool of workers to employers through a single online platform, by aggregating multiple crowdsourcing platforms (channels) under a unique system. This paper investigates the F8 channels’ demographic distribution and reward schemes by analysing more than 53k crowdsourcing tasks over four years, collecting survey data and scraping marketplace metadata. We reveal an heterogeneous per-channel demographic distribution, and an opaque channel commission scheme, that varies over time and is not communicated to the employer when launching a task: workers often will receive a smaller payment than expected by the employer. In addition, the impact of channel commission schemes on the relationship between requesters and crowdworkers is explored. These observations uncover important issues on ethics, reliability and transparency of crowdsourced experiment when using this kind of marketplaces, especially for academic research

The dark side of recruitment in crowdsourcing: ethics and transparency in micro-task marketplaces

Micro-task crowdsourcing marketplaces like Figure Eight (F8) connect a large pool of workers to employers through a single online platform, by aggregating multiple crowdsourcing platforms (channels) under a unique system. This paper investigates the F8 channels’ demographic distribution and reward schemes by analysing more than 53k crowdsourcing tasks over four years, collecting survey data and scraping marketplace metadata. We reveal an heterogeneous per-channel demographic distribution, and an opaque channel commission scheme, that varies over time and is not communicated to the employer when launching a task: workers often will receive a smaller payment than expected by the employer. In addition, the impact of channel commission schemes on the relationship between requesters and crowdworkers is explored. These observations uncover important issues on ethics, reliability and transparency of crowdsourced experiment when using this kind of marketplaces, especially for academic research

Effective Strategies Small Retail Leaders Use to Engage Employees

Research suggests that 70% of North American employees are disengaged in the workplace. Some small retail managers lack strategies for engaging employees. Using the employee engagement framework, the purpose of this descriptive case study was to explore successful strategies that small retail managers use to engage employees. The target population was small retail leaders, purposefully selected because of their success with engaging employees at an Orlando, Florida, company. Data collection was through face-to-face interviews with 5 leaders; and a review of archived organizational documents, including company memorandums, central email software, and online customer reviews through social media websites such as Google, Yelp, and Facebook posts. Data were analyzed using inductive coding of phrases and words from participant interviews, whereas secondary data were collected from participant memorandums, the company website, central email software, and online social media posts supporting the theme interpretation through methodological triangulation. The findings on these Orlando leaders revealed that supportive leaders improved employee engagement, direct communication improved employee engagement, and training improved employee performance. Improving employee engagement contributes to social change because small retail managers can use the findings to improve employee engagement through the implementation of effective strategies, direct communication, and training initiatives

Understanding the Use of HIT Catchers and Crowd Knowledge Sharing: A Case Study of Crowdworkers on Amazon Mechanical Turk

Crowdsourcing platforms have become a vital component of the modern digital economy, offering a wide range of HIT (Human Intelligence Task) opportunities to workers worldwide. Meanwhile, crowdworkers' use of scripting tools and their communication with each other are continuously shaping the entire crowdsourcing ecosystem. This thesis explores the use of HIT catchers by crowdworkers and their sharing of skill-based knowledge that facilitates the popularity of such scripting tools. It is revealed that the use of HIT catchers affects the completion speed and HIT-worker diversity for the whole HIT group, while depriving job opportunities from others. This potentially undermines the stability of the platform under the current reputation system relying on numbers of approvals and approval rates. Subsequently, another study explored how work strategies under the use of HIT catchers, including HIT acceptance, backlog, and completion, affect HIT availability, completion time, and result quality. The study also found differences in work behaviours between workers using and not using HIT catchers. Finally, this thesis investigates the skill-based knowledge sharing behaviour of crowdworkers, which promotes the blooming of scripting tools including HIT catchers, to improve the fairness of work opportunities and mitigate its negative impact on HIT completion. Using PLS-SEM, we assess the factors influencing knowledge sharing in the domain of skills. The study reveals the significance of high performance expectation, low effort expectation, and the joy and satisfaction in motivating the crowd skill-based knowledge sharing. Overall, this study provides an in-depth exploration around these two types of collective behaviour, highlighting the important role of tool use and knowledge sharing in shaping the crowdsourcing ecosystem