84,850 research outputs found

    Return of the man-machine interface: violent interactions

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    This paper presents the design and evaluation of “the man-machine interface” a punchable interface designed to criticise and react against the values inherent in modern systems that tacitly favour one type of user (linguistically and technically gifted) and alienate another (physically gifted). We report a user study, where participants used the device to express their opinions before engaging in a group discussion about the implications of strength-based interactions. We draw connections between our own work and that of evolutionary biologists whose recent findings indicate the shape of the human hand is likely to have been partly evolved for the purpose of punching, and conclude by examining violent force as an appropriate means for expressing thoughts and feelings

    The design with intent method: A design tool for influencing user behaviour

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    The official published version can be found at the link below.Using product and system design to influence user behaviour offers potential for improving performance and reducing user error, yet little guidance is available at the concept generation stage for design teams briefed with influencing user behaviour. This article presents the Design with Intent Method, an innovation tool for designers working in this area, illustrated via application to an everyday human–technology interaction problem: reducing the likelihood of a customer leaving his or her card in an automatic teller machine. The example application results in a range of feasible design concepts which are comparable to existing developments in ATM design, demonstrating that the method has potential for development and application as part of a user-centred design process

    Reputation Agent: Prompting Fair Reviews in Gig Markets

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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.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202
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