Solidarity and A.I. for Transitioning to Crowd Work during COVID-19 from Saiph Savage on Vimeo.
Below I present a brief summary of some of the sessions I attended in the symposium:
🌟 Highlighted Talks: Gig Work, VR, & Disabilities.
My research was featured in this set of highlighted talks. Sean Rintel from Microsoft was our chair. Some of the talks included:
Joshua McVeigh-Schultz & Katherine Isbister, SFSU and UC Santa Cruz, VR in Workplace Meetings: Learning from Social VR in ‘The Wild’
This talk discussed the ways in which VR could be used to improve work environments. I found particularly interesting the notion of how surreal animals (such as gigantic cats, similar to the ones in Alice in Wonderland) could be integrated into the work place to provoke conversations and interactions that might not be possible with traditional labor. This research in general is exploring how VR can create labor interactions we might not be able to have under traditional settings. However, here it is also important to think about how do we avoid turning the workspace into an amusement park . How might we still maintain the seriousness and professionalism that some employers and workers might value? Other related questions that emerged for this work, was about how integrating VR might create a large cognitive load on workers, especially when they had to switch contexts (e.g., they are working from home and engaging in a virtual environment and meeting with their boss, but now they have to switch contexts and check on their toddler that exists just in the real world.) It is important to also consider how these technologies might make it easier for workers to switch between the different activities they do in their labor, but also in their personal lives.
This research in general falls under the areas of serious games. My research lab has also been exploring storytelling and games in crowd work. Especially how we could integrate play as a tangent activity into online labor. Below some examples of our play in crowd work research:
John Tang, Microsoft, Early Indicators of the Effect of the Global Shift to Remote Work on People with Disabilities.
This research from Microsoft was exploring the ways that the pandemic was affecting the work of people with disabilities. One of the things I found interesting from this talk was that the work found that for people with disabilities one of the biggest challenges they faced was having to put on their cameras during meetings. Having to turn on their cameras was hard because many times this meant that the person with disabilities would have to "perform" for the people on the call (e.g., ensure they keep a certain posture in front of the camera, look a certain way), in addition to paying attention to what each person said and contributed (which was more difficult in a mediated communication settings, because the audio could be more noisy than meeting in person, or they might not have as clear an image of the people speaking to make out what they were saying) Additionally, many times people with disabilities had interpreters who were the ones who ended up interacting with their co-workers for them. Given that algorithms sometimes highlight the people who participate and interact with others, the people with disabilities felt left out of the conversation, erased (as the algorithms would never showcase them within the chat and video interfaces). I found this talk thought provoking and it helped me to start to think about how the technology we have and norms we establish (such as forcing everyone in a meeting to turn on their cameras), can create power struggles, especially put people with disabilities in a position where they are erased from conversations, and also are given additional cognitive loads.
This research from Microsoft was exploring the ways that the pandemic was affecting the work of people with disabilities. One of the things I found interesting from this talk was that the work found that for people with disabilities one of the biggest challenges they faced was having to put on their cameras during meetings. Having to turn on their cameras was hard because many times this meant that the person with disabilities would have to "perform" for the people on the call (e.g., ensure they keep a certain posture in front of the camera, look a certain way), in addition to paying attention to what each person said and contributed (which was more difficult in a mediated communication settings, because the audio could be more noisy than meeting in person, or they might not have as clear an image of the people speaking to make out what they were saying) Additionally, many times people with disabilities had interpreters who were the ones who ended up interacting with their co-workers for them. Given that algorithms sometimes highlight the people who participate and interact with others, the people with disabilities felt left out of the conversation, erased (as the algorithms would never showcase them within the chat and video interfaces). I found this talk thought provoking and it helped me to start to think about how the technology we have and norms we establish (such as forcing everyone in a meeting to turn on their cameras), can create power struggles, especially put people with disabilities in a position where they are erased from conversations, and also are given additional cognitive loads.
Saiph Savage & Mohammad Jarrahi, Microsoft and UNC Chapel Hill. Solidarity and A.I. for Transitioning to Crowd Work during COVID-19
Our research focused on combining worker solidarity and A.I. to design the future of work after COVID-19. Our video isshown on top of this post:
Due to the COVID-19 pandemic, a number of gig workers who engaged in location-based gig work (e.g., Taskrabbit, Care.com, or Wag) have had to transition to new jobs that are independent of location (e.g., online freelancing or crowd work). However, this has been a difficult transition. Especially because in this new environment, gig workers now have to compete globally for work, and they also have to focus on work interactions that are primarily online (instead of gig work that takes place within specific physical locations or within in-person meetings). In this paper, we build on our extensive research on gig work, gig literacy and the design of crowdsourcing systems, to present an intelligent architecture for helping workers transition to new gig jobs in times of global crisis. Our intelligent architecture uses machine learning and draws on collective action theory to introduce ``Solidarity Brokers.'' Our Solidarity Brokers are computational mechanisms that identify the best ways to build solidarity between workers with the purpose of mobilizing workers to help each other transition to new jobs. We finish by presenting a brief research agenda for intelligent tools that facilitate work transitions during the global pandemic and beyond.
Our research focused on combining worker solidarity and A.I. to design the future of work after COVID-19. Our video isshown on top of this post:
Due to the COVID-19 pandemic, a number of gig workers who engaged in location-based gig work (e.g., Taskrabbit, Care.com, or Wag) have had to transition to new jobs that are independent of location (e.g., online freelancing or crowd work). However, this has been a difficult transition. Especially because in this new environment, gig workers now have to compete globally for work, and they also have to focus on work interactions that are primarily online (instead of gig work that takes place within specific physical locations or within in-person meetings). In this paper, we build on our extensive research on gig work, gig literacy and the design of crowdsourcing systems, to present an intelligent architecture for helping workers transition to new gig jobs in times of global crisis. Our intelligent architecture uses machine learning and draws on collective action theory to introduce ``Solidarity Brokers.'' Our Solidarity Brokers are computational mechanisms that identify the best ways to build solidarity between workers with the purpose of mobilizing workers to help each other transition to new jobs. We finish by presenting a brief research agenda for intelligent tools that facilitate work transitions during the global pandemic and beyond.
Next, I co-chaired with Professor Steven B Sawyer the break out session of:
✨"Employment: Hiring, onboarding, management, freelancing, on-demand, crowdwork, gig work"
Some interesting things that were discussed here was that:
- Instcart during the pandemic has been pushing a narrative of "The gig workers on this platform are heroes!" However, not all Instacart workers are buying this narrative of seeing themselves as heroes. They are bothered that the company is not doing more to keep workers safe. The narrative feels manipulative, especially when the workers are out risking their health just so somebody else can eat an ice-cream. Workers feel they are not getting essential food, but rather "buying the indulgences of an elite".
- Interesting to learn how gig workers are identifying what tasks are "essential" and which ones are more "indulgence".
- It is important to consider how these hero narratives will change over time and whether they will have any long-lasting effects on how we view these gig workers.
- Instacart workers tend to be more white women. I found this interesting given that a large portion of gig workers are usually people of color. However, on Instacart white women workers might be more present because they need a car to be able to buy groceries. White women usually are better off economically than women of color and hence they can more likely better afford to have their own car to work on the platform and buy groceries. Notice that Uber/Lyft also requires that you have a car. But, markets have emerged where people can rent cars to work on Uber/Lyft. Instacart is still not as popular so these markets are not yet available.
- There were also discussions around how do we design worker centric platforms during the pandemic, especially for location based gig work and location independent gig work. Example of Location Independent include: Upwork, Amazon Mechanical Turk; while Location-Dependent includes Instacart, Uber/Lyft, Task Rabbit.
- We also discussed how the learning curves that gig platforms have can also act as a barrier for helping workers grow and transition to different jobs across gig markets. People who are established on certain platforms like Upwork might have a harder time moving to other platforms like Fiverr, etc where they have to rebuild their reputation. These type of dynamics affect workers' empowerment and where they can move to.
- There was also discussions on whether the process of designing for the worker can align incentives for all parties (platform, workers, customers) or whether it would entail concentrating power between two stakeholders (e.g., customers and workers) to "overthrow" the unfair practices of the platform. It is not clear which approach is more promising, or what are the alternatives?
- There were a lot of reference's to Michael Dunn's research where he studied the characteristics of the different gig markets. We discussed his characterization to start to think about how these properties might be best leveraged to create worker centric labor platforms during the pandemic and beyond. Below some nice images of the work Michael Dunn has done in this space to characterize different gig markets.
Finally, I also co-chaired with Frank Morgan the session of:
✨"Society: Public policy, business, economics, health, societal implications and confounding factors"
Here we had discussions around:
- How might we be able to measure competency and available potential of a workforce in the wild? An issue that can emerge is that it is not straightforward to know what are the best ways to visualize competency and potential. It can depend on the strategy and the type of work a company wants to get done.
- Social Glue. Having workers keep social interactions with each other can help them to get work done (they need social glue.) However, we discussed whether this social glue was also needed within crowd work. The group argued that since crowd work had primarily micro labor, social glue was not needed. Workers were transient. However, based on my lab's research around crowd work, I would argue that social glue is very necessary in crowd work as well. Primarily because: crowd work also has a steep learning curve. Therefore, the workers that stay on the platform are the ones who have invested significant time in learning how to find good work, and learning best practices for getting that work done to ensure quality and high wages for themselves. Crowd workers are not transient. Considering that these workers will spend significant time on Amazon Mechanical Turk, and other crowd platforms, it can be important to think about how might we facilitate empowering workers within this space. I would argue that it is through the social glue. Through creating solidarity between workers. My lab's CSCW paper explored this angle of helping workers keep social glue through peer coaching.
- How might we design technology to make workers more productive and flexible, what kinds of policies should we create to ensure that a diverse set of workers benefit from productivity and flexibility? For example, could we use breaks and work disruptions as mechanisms to help workers relaxed or further connected with their children? What kind of social divisions are new technologies creating in workers? (Some workers can access highly specialized tools because they can afford them, while other cannot. What type of divisions and gaps does this cause? ) Alex Williams has an interesting paper looking at tooling and how it can further create social divisions in crowd workers.Overall, I greatly enjoyed having discussions around the Future of Work with Microsoft, other academics and different folks for industry. It was motivating to brainstorm together to start defining an empowering future of work 👊
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