ExperTwin: An Alter Ego in Cyberspace for Knowledge Workers

Toxtli, Carlos; Flores-Saviaga, Claudia; Maurier, M.; Ribot, A.; Bankole, Temitayo; Entrekin, A.; Cantley, Mark; Singh, Sobhit; Reddy, Sumitra; Reddy, R.

doi:10.1109/cybermatics_2018.2018.00169

Cited by 2 publications

(1 citation statement)

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“…Additionally given that Reputation Agent can be easily implemented as a validation module, Platform maintainers could change their front-end review interface without having to worry about Reputation Agent suddenly not working. Reputation AgentâĂŹs deep learning nature makes it so that if a gig market changes its policies, platform maintainers with minimum knowledge in artificial intelligence can easily re-train Reputation Agent to be updated with the changes [34,99]. In our website we have shared training examples for Reputation Agent's learning module so that website maintainers can easily start using our tool.…”

Section: Feasibility and Maintainabilitymentioning

confidence: 99%

Reputation Agent: Prompting Fair Reviews in Gig Markets

Toxtli,

Richmond-Fuller,

Savage

2020

Preprint

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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.

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Section: Feasibility and Maintainabilitymentioning

confidence: 99%