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samuel dooley
@SpamDooley
human centered machine learning PhD student working with 🌈
cs.umd.edu/~sdooley1Joined April 2022

samuel dooley’s Tweets

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visit my poster this afternoon!! ⌚️ 2-4pm 📌 hall j #1023 📰 arxiv.org/abs/2211.15937 💻 dooleys.github.io/robustness/ come talk to me about this work or... (short 🧵1/2)
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we test 3 academic and 3 commercial face detection models on 4 different datasets which we corrupt using the imagenet-c paradigm. (1) we see that all models on all datasets are very susceptible to these edge cases of noisy or corrupted images.
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we also provide some recommendations to industry and the public sector about how our findings and methodology can be used and adapted. you can also read more in this article by from a while back about an early release of this study
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and (3) both academic and commercial face detection systems exhibit a similar degree of their sociodemographic bias. which might be (not) surprising and disappointing given the resources and commitments made by companies to address performance disparities in face rec
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(2) in almost all cases, we find that images of individuals who are masculine-presenting, older, darker skinned, or dimly lit fare worse than their counterparts in face detection models
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we test 3 academic and 3 commercial face detection models on 4 different datasets which we corrupt using the imagenet-c paradigm. (1) we see that all models on all datasets are very susceptible to these edge cases of noisy or corrupted images.
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since facial detection is everywhere, it get used in a variety of edge cases — like settings where the image is noisy or corrupted we ask (1) how robust is face det to noise, (2) are there demographic disparities, (3) are commercial providers better than academics?
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a fantastic talk by at #chi2022 insightful and important findings that women gig workers are not getting necessary support by gig platforms which generally make one size fits all design decisions. gender agnostic choices cause specific harms for these users
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Great to see on Twitter! If you are in New Orleans for #chi2022 later this month, pop by his talk looking at privacy assurances and COVID app adoption, & say hello! Teaser: youtube.com/watch?v=6CmdBg His first-author work is joint with and
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do you think it is better to include reassurances about privacy when talking about apps which use sensitive data? in two weeks, i'll be at #chi2022 to talk about what we learned about human behavior from using different messages to advertise covid contact tracing apps twitter.com/eredmil1/statu…
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Woo is on twitter & headed to #chi2022 in person for his first-ever in-person conference. Check out the talk at CHI & meet Samuel :)
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do you think it is better to include reassurances about privacy when talking about apps which use sensitive data? in two weeks, i'll be at #chi2022 to talk about what we learned about human behavior from using different messages to advertise covid contact tracing apps twitter.com/eredmil1/statu…
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but, if you appeal to an audience's sense of being part of a group, then privacy statements can help your messaging be more effective 📈📈📈 so a collectivist's concerns about oneself (like privacy) are allayed by reassuring privacy messages, increasing engagement with the app
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turns out - it depends on how you frame the pitch if you appeal to individuality, then a privacy statement hurts the effectiveness of your message 📉📉📉 so if you're primed to be self-interested, hearing about privacy concerns makes the app less appealing
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do you think it is better to include reassurances about privacy when talking about apps which use sensitive data? in two weeks, i'll be at #chi2022 to talk about what we learned about human behavior from using different messages to advertise covid contact tracing apps
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Our field study on how to ethically advertise COVID-19 apps (collab with @LADeptHealth) has received a #chi2022 best paper honorable mention! coauthored with S. Dooley @DanaTurjeman @johnpdickerson
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and (3) documenting and contextualizing privacy views of various tech systems i've done work on perceptions of contact tracing apps and views of governmental and organizational surveillance
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(2) human-centered assistive technologies in morally laden decision spaces things like, helping mental healthcare providers prioritize their time to help more patients with higher quality
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