This project was partially supported by the UNO Office of Research and Creative Activity through a grant for Graduate Research and Creative Activity (GRACA).
In the early days of the Internet, companies provided content, while users were only able to consume content. With the emergence of Web 2.0, users became capable of generating content on the web. User-generated content enables crowdsourcing, which has changed how individuals, groups, or organizations complete tasks and solve problems; this phenomenon is changing how some companies and industries do business. These changes have raised new ethical, social, and political issues. Previous studies have highlighted ethical challenges that organizations and individuals face in crowdsourcing, such as undercutting prices, low pay, privacy, and deception, to name a few. This research aims to address two problems with this. First, previous research identifies ethical considerations, but there is a lack of research exploring the ethical considerations of crowdsourcing participants. Second, organizations that make money through crowdsourcing may be unmotivated to consider ethical values. Using research on transparency in organizations and Value Sensitive Design (VSD), this study explores these problems through two distinct contributions.
First, this research assembles a taxonomy to classify ethical concerns and considerations in crowdsourcing. When a phenomenon is largely unknown, such as ethical issues in crowdsourcing, a taxonomy provides a theory for analyzing the concepts and relationships among concepts.
Second, this research uses the design science research methodology to create a model for designers of crowdsourcing systems to use when creating crowdsourcing applications. The model includes design principles in the form of meta-requirements identified from transparency, VSD, and other ethics literature. This model and taxonomy provide benefits for crowdsourcing organizations as well as participants. Organizations and individuals can understand the trade-offs that occur among ethical principles as well as trade-offs of principles with crowdsourcing outcomes.
For example, an organization may pay more money if they are able to retain participants for future crowdsourcing endeavors. On the individual side, participants may divulge more private information if they are able to earn more money for completing a task.
Future work for this project includes refining the ethical principles and identifying other salient principles. Following this, I will instantiate the model empirically by performing an experiment. This experiment will use the Amazon Mechanical Turk platform to instantiate the model, by embedding meta-requirements into the system.
To test the model empirically, I intend to measure the participants’ attitudes toward the system, ethical perceptions of the crowdsourcing organization, and behavioral intention to return to the system, as well as measure the interplay of ethical principles and crowdsourcing outcomes. This experiment will evaluate if the ethical design of the crowdsourcing system can protect crowdsourcing stakeholders as well as if the design affects predictors of organizational and market performance (such as retaining participants).