This paper explores the design challenges that arise in supporting online freelancers to navigate relationships with clients. Prior studies have shown that current platform designs can lead to worker precarity in freelancer-client relationships, such as power imbalances, information asymmetry, and labor abuse. To envision alternative designs that empower workers in managing their relationships with clients, we engaged 22 Upwork freelancers in participatory speculative design activities. Through this co-design process, we identified design tensions that constrain design options as a result of conflicting values and priorities that could only be balanced and compromised rather than completely resolved. Six design tensions were identified in the context of designing for four different phases of freelancing. We observed three patterns in these tensions: 1) the freelancers' need for client involvement in their tasks and career growth, which conflicted with their skepticism that clients had sufficient incentives to be involved; 2) that there was often no viable balancing option for some tensions, but they could be addressed through changes in the platform's incentive structure; and 3) some tensions occurred not only between freelancers and clients, but also within the freelancer community. We present three approaches for addressing these design tensions and discuss how this research can support more equitable and healthy freelancer-client relationships.

Digitally replicating the appearance and behaviour of individuals is becoming feasible with recent advancements in deep-learning technologies such as interactive deepfake applications, voice conversion, and virtual actors. Interactive applications of such agents, termed AI clones, pose risks related to impression management, identity abuse, and unhealthy dependencies. Identifying concerns AI clones will generate is a prerequisite to establishing the basis of discourse around how this technology will impact a source individual's selfhood and interpersonal relationships. We presented 20 participants of diverse ages and backgrounds with 8 speculative scenarios to explore their perception towards the concept of AI clones. We found that (1. doppelganger-phobia) the abusive potential of AI clones to exploit and displace the identity of an individual elicits negative emotional reactions; (2. identity fragmentation) creating replicas of a living individual threatens their cohesive self-perception and unique individuality; and (3. living memories) interacting with a clone of someone with whom the user has an existing relationship poses risks of misrepresenting the individual or developing over-attachment to the clone. These findings provide an avenue to discuss preliminary ethical implications, respect for identity and authenticity, and design recommendations for creating AI clones.

Modern large-scale language models approach the quality of human-level writing. This promises the advent of AI writing companions performing AI-led writing under human control, surpassing traditional writing tools limited to revision and ideation supports. However, human-AI co-writing may endanger writers’ control, autonomy, and ownership by overstepping co-creative boundaries. Our design workbook study with 7 hobbyists and 13 professional writers elicited three sets of primary barriers to the adoption of human-AI co-writing. Storywriters desire retaining control over writing rather than letting AI take the lead when they (1) prioritize emotional values in turning ideas into words over the productivity of AI-generated writing; (2) have high self-confidence and distrust AI in challenging sub-tasks (e.g., creating characters and dialogue); and (3) expect the AI control mechanism to mismatch their writing strategies. We lay the groundwork for AI companions that respect storywriters’ personal values and writing methods.

Women make up approximately half of the workforce in ride-hailing, food delivery, and home service platforms in North America. While studies have reported that gig workers face bias, harassment, and a gender pay gap, we have limited understanding of women’s perspectives of these issues and their coping mechanisms. We interviewed 20 women gig workers to hear their unique experiences with these challenges. We found that gig platforms are gender-agnostic, meaning they do not acknowledge women’s experiences and the value they bring. By not enforcing anti-harassment policies in design, gig platforms also leave women workers vulnerable to bias and harassment. Due to the lack of support for immediate actions and in fear of losing access to work, women workers “brush off” harassment. In addition, the platforms’ dispatching and recommendation mechanisms do not acknowledge women’s contributions in perceived safety for customers and social support for peer workers.

Crowdworkers depend on Amazon Mechanical Turk (AMT) as an important source of income and it is left to workers to determine which tasks on AMT are fair and worth completing. While there are existing tools that assist workers in making these decisions, workers still spend significant amounts of time finding fair labor. Difficulties in this process may be a contributing factor in the imbalance between the median hourly earnings ($2.00/hour) and what the average requester pays ($11.00/hour). In this paper, we study how novices and experts select what tasks are worth doing. We argue that differences between the two populations likely lead to the wage imbalances. For this purpose, we first look at workers' comments in TurkOpticon (a tool where workers share their experience with requesters on AMT). We use this study to start to unravel what fair labor means for workers. In particular, we identify the characteristics of labor that workers consider is of "good quality'' and labor that is of "poor quality'' (e.g., work that pays too little.) Armed with this knowledge, we then conduct an experiment to study how experts and novices rate tasks that are of both good and poor quality. Through our research we uncover that experts and novices both treat good quality labor in the same way. However, there are significant differences in how experts and novices rate poor quality labor, and whether they believe the poor quality labor is worth doing. This points to several future directions, including machine learning models that support workers in detecting poor quality labor, and paths for educating novice workers on how to make better labor decisions on AMT.

Researchers in various fields have been discussing the ethics of field research, particularly their responsibility to provide concrete benefits to participants. For example, when designing technology for their participants the discussion has centered around how and whether the technology benefits participants. We argue, that design projects can be reoriented towards benefiting participants from the outset, by slightly changing the process, activities, and initial object of design. Focusing instead on how to immediately impact the goals of the participants, designers can then progressively introduce technology to scale their impact. In this paper we present an ongoing instantiation of this process in rural West Virginia, where we have been teaching computer classes at a local library. We have found that our participants and partners are more appreciative of our efforts, and that the data we are gathering is just as, if not more, rich than previous design methodologies that we have used.

In recent years, the peer-to-peer economy has grown exponentially, particularly in the ride-sharing sector. This growth has been accompanied by a muddying between the sharing and gig economy, and it has become unclear when an activity is sharing a resource vs. providing a service. To unpack this difference, we studied two successful carpooling groups (university students traveling home and commuting among professionals), which we contrast with previous literature on ride-hailing apps (e.g., Uber). The two communities that we studied differ in that: professionals, had more routine ride-sharing needs based on their commute; and students, arranged rides to return home for school breaks or long weekends. We detail how common needs and backgrounds impacted how carpoolers treated each other. Leveraging these findings, we outline design paths for both the sharing and gig economies to better realize the ideas of the sharing economy.

Ride-sharing platforms have rapidly spread and disrupted ride hailing markets, resulting in conflicts between ride-sharing and taxi drivers. Taxi drivers claim that their counterparts have unfair advantages in terms of lower prices and a more stable customer base, making it difficult to earn a living. Local government entities have dealt with this disruption and conflict in different ways, often looking towards some form of regulation. While there have been discussions about what the regulation should be, there has been less work looking at what impacts regulations have on ride-sharing drivers and their usage of the platforms. In this paper we present our interview study of ride-sharing drivers in Taiwan, who have gone through three distinct phases of regulation. Drivers felt that regulations legitimized their work, while having to navigate consequences related to regulated access to platforms and fundamental changes to the "gig'' of ride-sharing.

Ride-sharing companies have been reshaping the structure and practice of ride-hailing work. At the same time, studies have been showing mixed driver experiences on the platform while many of the drivers are working part-time. In this research, we seek to understand why drivers on this platform are working part-time, how this impacts their view of the platform, and what this means for more accurately evaluating the design of these platforms. To investigate this question, we focused on situating ride-sharing in the lives and constellation of gigs that drivers maintain. We collected 53 survey responses and conducted 10 semi-structured interviews with drivers to probe these questions. We found that the extent that drivers categorize themselves as part-time is less about the number of hours worked and more about how dependent they are on ride-sharing income. The level of this dependency seemed to heavily influence how they interacted with the platform and their attitudes towards difficulties faced. It seemed to us that in some ways that the design or functioning of the platform almost pushed users towards working part-time. We discuss the importance of taking these different types of workers and their situations into consideration when evaluating the design and usability of these platforms.

Uber is a ride-sharing platform that is part of the 'gig-economy,' where the platform supports and coordinates a labor market in which there are a large number of ephemeral, piecemeal jobs. Despite numerous efforts to understand the impacts of these platforms and their algorithms on Uber drivers, how to better serve and support drivers with these platforms remains an open challenge. In this paper, we frame Uber through the lens of Stakeholder Theory to highlight drivers' position in the workplace, which helps inform the design of a more ethical and effective platform. To this end, we analyzed Uber drivers' forum discussions about their lived experiences of working with the Uber platform. We identify and discuss the impact of the stakes that drivers have in relation to both the Uber corporation and their passengers, and look at how these stakes impact both the platform and drivers' practices.