Learning from Learning Buddies: Opportunities for Tech to Connect across Generations

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(Cross-posted from Irene’s Medium)

As a child, I spent a lot of time with my parents’ retired colleagues in our community who often helped take care of children like me when our working parents were occupied. Yet to say, not all children have such caring older adults when they grow up and many older adults don’t have younger generations in their community as they age. Today’s communities have programs that specifically aim to connect the two generations. These programs often seek older adults’ experience and expertise to support children’s growth. However, there are challenges that prevent older adults from benefiting in these programs and we see opportunities for technologies to address these challenges.

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Value Sensitive Algorithm Design: Method, Case Study and Lessons

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Intelligent algorithmic systems are assisting humans to make important decisions in a wide variety of critical domains. Examples include: helping judges decide whether defendants should be detained or released while awaiting trial; assisting child protection agencies in screening referral calls; and helping employers to filter job resumes.

However, technically sound algorithms might fail in multiple ways. First, automation may worsen engagement with key users and stakeholders. For instance, a series of studies have shown that even when algorithmic predictions are proved to be more accurate than human predictions, domain experts and laypeople remain resistant to using the algorithms. Second, an approach that largely relies on automated processing of historical data might repeat and amplify historical stereotypes, discriminations, and prejudices. For instance, African-American defendants were substantially more likely than Caucasian defendants to be incorrectly classified as high-risk offenders by recidivism algorithms.

In this CSCW paper, we propose a novel approach to the design of algorithms, which we call Value-Sensitive Algorithm Design. Our approach is inspired by and draws on Value Sensitive Design and the participatory design approach. We propose that the Value Sensitive Algorithm Design method should incorporate stakeholders’ tacit knowledge and insights into the abstract and analytical process of creating an algorithm. This helps to avoid biases in the design choices and compromises of important stakeholder values. Generally, we believe that algorithms should be designed to balance multiple stakeholders’ values, motivations and interests, and help achieve important collective goals. (more…)

Simulation Experiments on (the Absence of) Ratings Bias in Reputation Systems

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Screenshots from Upwork, Uber, Rover, Instacart, each showing 5-star rating interfaces.
Rating systems for building reputation are used everywhere in the gig economy (left to right: Upwork, Uber, Rover, Instacart), and lots of prior research suggests they will show race- and gender-based biases. Our research tells a more complex story.

It seems like every day there is a new gig work platform (e.g. UpWork, Uber, Airbnb, or Rover) that uses a 5-star scale to rate workers. This helps workers build reputation and develop the trust necessary for gig work interactions, but there is a big concern: lots of prior work finds that race and gender biases occur when people evaluate each other. In an upcoming paper at the 2018 ACM CSCW conference, we describe what we thought would be a straightforward study of race and gender biases in 5-star reputation systems. However, it turned into an exercise in repeated experimentation to verify surprising results and careful statistical analysis to better understand our findings. Ultimately, we ended up with a future research agenda composed of compelling new hypotheses about race, gender and five-star rating scales. (more…)

Order From Chaos: Where Is Sharing Economy Literature Going Next?

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As people flock to services like Airbnb, Uber, and TaskRabbit, the sharing economy has become a prominent research topic in Computer Science, especially in Human-Computer Interaction (HCI). As shown in the figure below, research on the sharing economy has almost doubled year by year, and seemed to start declining after 2015. Our study reviews the existing computing literature in this space and suggests where future efforts can go. [Link] (more…)

What Users of Couchsurfing and Airbnb Can Tell Us about Online Room Sharing

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Couchsurfing and Airbnb are websites that connect people with an extra guest room or couch with random strangers on the Internet who are looking for a place to stay. Although Couchsurfing predates Airbnb by about five years, the two sites are designed to help people do the same basic thing and they work in extremely similar ways. They differ, however, in one crucial respect. On Couchsurfing, the exchange of money in return for hosting is explicitly banned. In other words, couchsurfing only supports the social exchange of hospitality. On Airbnb, users must use money: the website is a market on which people can buy and sell hospitality. (more…)