Designing Technology for Children: Review of Last Decade’s IDC Research

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With the rapid changes of technologies and the increasing number of technology toys in the market (e.g., Osmo, Luka), one may wonder: what are the popular choices of technology platforms when researchers are designing for children? What has changed in these design choices as children become familiar with a variety of technologies at much younger ages recently, as opposed to 10 years ago?

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Get More Citations by Keeping Graduate Students Off Reddit

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My first day as a PhD student in HCI is coming up pretty soon, so I’m being a good doobie and reading a bunch of papers in the field. A quick bit of background: I spent the first year of my CS PhD doing computational biology research in a microbiome informatics lab. I got tired of all the “Mo is a poop scientist” jokes (most of our data come from stool samples), so I decided to pursue a respectable career as an HCI researcher instead. TBH though, working with poop data was pretty cool, and I’ve had the wonderful opportunity of being exposed to papers in a field with entirely different standards and conventions, at least one of which could (and should) be adopted by papers in HCI.

I don’t always read papers super carefully. I also don’t finish every paper I start reading. Neither do you; be honest. I’m an exhausted grad student with limited time (and even more limited brain power). If your paper is easy to read, I’m a lot more likely to keep reading it. And if I finish reading it, I’m a lot more likely to share it. More shares = more people citing your work. So really, one of your goals when writing a paper should be to keep me off Reddit for long enough to fully engage with the wonderful new research you worked so hard to produce. But how could you possibly compete with this adorable baby skunk I found on r/aww? I mean look at it.

A cat lying on a bed

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As they say, “if you can’t beat ‘em, join ‘em.” Take a page out of Reddit’s book: more pictures! To see why, here’s a quick analysis I had fun running last weekend on a web scrape of CHI 2019 papers (@sig_chi, please unblock my IP address). TL;DR: CHI 2019 papers with more figures tend to be downloaded more.

Did you actually bother reading the table, or was it TL? Okay, now here’s the same data presented in a box plot instead:

Oooooh, pretty! 

When I presented my data in a table, I forced you to do some extra work to identify the important trend for yourself (if you even bothered looking at the table, that is). I made you perform tedious column-by-column comparisons of numbers. With your actual eyeballs. The horror!

But it doesn’t have to be this way. Using a figure instead—in this case a box plot—communicates the same essential piece of information in a fraction of the time, for a fraction of the effort on your part. A quick visual scan is all it takes for you to get the all-important message: more figures leads to more downloads. Got it.

From what I’ve noticed so far, HCI papers use figures a lot less effectively than papers in computational biology. But citations/downloads aside, we want our research to be as clear and accessible as possible, especially when trying to communicate our work to a broader audience, or to relevant stakeholders in the general public. Our field is human-centered, after all.

More great tips on disseminating your research in this CHI 2020 paper by my colleagues, BTW. See you next time.

What does it mean to “keep community in the loop” when building algorithms for Wikipedia?

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Original Artwork contributed by: Laura Clapper.

[Cross-posted from Wikimedia Foundation Technical Blog]

Imagine you’ve just created a profile on Wikipedia and spent 27 minutes working on what you earnestly thought would be a helpful edit to your favorite article. You click that bright blue “Publish changes” button for the very first time, and you see your edit go live! Weeee! But 52 seconds later, you refresh the page and discover that your edit has been wiped off the planet. How would you feel if you knew that an algorithm had contributed to this rapid reversion of all your hard work?

For the sake of illustration, let’s say you were editing a “stub” article about a woman scientist you admire. You can’t remember where you read it, but there’s this great story about how she got interested in computing. So, you spend some time writing up the story to improve her mostly empty bio. Clearly, you’re trying to be helpful. But unfortunately, you didn’t cite your source…and boom!—your work gets blown away. Without any way to understand what happened, you now feel snubbed and unwanted. Will you ever edit again?! :scream_emoji:

Many edits (like yours) are damaging to Wikipedia, even if they were completed in good faith—e.g. missing citations [ ], bad grammars, mis-speled werds, and incorrect {syntax. And then there are plenty of edits that are malicious—e.g. the addition of offensive, racist, sexist, homophobic, or otherwise unacceptable content. All of these examples make it necessary for human moderators (a.k.a. “patrollers”) to review edits and revert (or fix) the bad ones. However, given the massive volume of edits to Wikipedia each day, it’s impossible for humans to review every edit, or even to identify which edits should be reviewed. 

In order to make it possible(-ish) to build and maintain Wikipedia, the community absolutely requires the help of algorithmic systems. But we need these algorithmic systems to be effective community partners (think R2-D2, cheerfully supporting the Rebel Alliance!) rather than AI overlords (think Terminator…being Terminator). How can we possibly design these systems in a way that supports all of its well-intentioned community stakeholders…including patrollers, newcomers, and everyone in between?

Our team of researchers from the University of Minnesota, Carnegie Mellon University, and the Wikimedia Foundation explored this question in our new open access research paper. We used a method called Value-Sensitive Algorithm Design which has three steps: 

(1) Understand community stakeholders’ values related to algorithms.
(2) Incorporate and balance these values across the full span of the ML development pipeline.
(3) Evaluate algorithms based not only on accuracy, but also on their acceptability and broader impacts.

We argue that if you follow these three steps, you can “keep community in the loop” as you build algorithmic systems, making you more likely to avoid catastrophic and community-damaging consequences. Our paper completes the first step of Value-Sensitive Algorithm Design with respect to a prominent machine learning system on Wikipedia called ORES (Objective Revision Evaluation Service).

ORES is a collection of machine learning algorithms which look at textual changes made by humans, and then produce statistical guesses of how likely the edits are to be damaging. These guesses are continuously fed via API in real-time all across Wikipedia, as editors and patrollers complete their work in parallel. 

For example, one prominent place where ORES’ guesses affect user experience is in the “Recent Changes” feed, which looks like a list that shows every new edit to the encyclopedia chronologically. Patrollers often spend time looking through the Recent Changes list, using a highlighting tool built into the interface. 

If we fed an edit like yours into ORES, it might output guesses like “82% likely to be damaging” and “79% likely to be done in good faith.” The Recent Changes list could use these scores to highlight your edit in red to show that it is “moderately likely to be problematic.” Or, if the patroller wanted, it could highlight your edit in green to show that you likely meant well. 

In either case, both the underlying algorithms of ORES and the highlights they generate majorly impact: (1) how the patroller interacts with your edit, and (2) whether or not you will continue editing in the future. That’s why, in our study, we wanted to understand what values should guide our design decisions with regard to systems like ORES, and how we can balance these values to lead to the best outcomes for the whole community.

We spoke to dozens of ORES stakeholders, including editors, patrollers, tool developers, Wikimedia Foundation employees, and even researchers, in order to systematically identify which values matter to the community. The following infographic summarizes the results.

For example, one critical value is “Human Authority.” On Wikipedia, the community believes it is vitally important to avoid giving final decision-making authority to the algorithmic system itself. In other words, please, nobody build Terminator! There should never be an algorithm that gets to call the shots and make the final decision about which edits stay, and which edits go. But we do need community partners like R2-D2 to assist with “Effort Reduction” by pointing us in the right direction.

At the same time, the example of your edit shows that along with “Effort Reduction,” we also need to build systems that foster “Positive Engagement.” In other words, ORES should reduce how much work it takes for patrollers to find bad edits, and it also needs to make sure that well-intentioned community members are having positive experiences, even when their edits aren’t up to snuff. 

So, maybe when ORES detects damaging (but good faith) edits in Recent Changes, those edits could receive special treatment. For example, rather than wiping out your red-highlighted edit without explanation, perhaps your edit could be allowed to stay online for a just a few extra minutes. Recent Changes could take a hint from Snuggle and direct a patroller to first reach out to you before reverting, provide some scaffolded text like, “Hi @yourhandle! Thanks for making your first edit to Wikipedia! Unfortunately, our algorithm detected an issue… It seems like you meant well, so I wanted to see if you could fix this by adding a citation so that I don’t have to revert it?” 

(Yes, this is challenging the BOLD, Revert, Discuss (B-R-D) paradigm, and suggesting that in some cases, B-D-R may be a more appropriate way to balance community values. Please discuss!)

In the full paper, we share our journey of applying VSAD to understand the Wikipedia community’s values, along with 25 concrete recommendations for developers interested in building ML-driven systems in complex socio-technical contexts. As you navigate community-based moderation, we hope our experiences may shed light on approaches to problems you may be experiencing in your community, as well.

Thanks for reading! Please share your thoughts in the comments, or get in touch with me @fauxneme on Wikipedia.

Special thanks to co-authors, colleagues, and friends who contributed feedback on this blog post, including Aaron Halfaker, Loren Terveen, Haiyi Zhu, Anjali Srivastava, Zachary Levonian, Mo Houtti, Sabirat Rubya, Charles Chuankai Zhang, and Laura Clapper.

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…)