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  1. Prikvačeni tweet
    10. srp 2018.

    Thanks to everyone who attended our tutorial on Machine Learning for Personalised Health given by , Lamiae Azizi and myself. A copy of our slides can be found on the tutorial website here:

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  2. proslijedio/la je Tweet
    30. sij

    We are planning to hold events, big and small, for in Ethiopia, in Austria, in Boston, in Vancouver, and more! Apply to be an organizer if you'd like to be more involved.

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  3. proslijedio/la je Tweet
    30. sij

    Are you excited for an opportunity to organize related events in 2020? It is a great opportunity to get involved and support the community of women in machine learning! We are opening applications on January 31st, please look out for the announcement on our social media.

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  4. proslijedio/la je Tweet
    25. sij

    Watch my awesome mentor talk about why it’s important to include people in the ML lifecycle. Her views on human-centered approaches and evaluation of interpretability are exactly how this field should move forward!

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  5. proslijedio/la je Tweet
    24. sij

    Want to sponsor AISTATS 2020? Check our sponsorhsip details. This year, funding will go towards supporting diversity and inclusion efforts and the families attending!

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  6. proslijedio/la je Tweet
    21. sij

    Save the date for our 4th annual Advances in Data Science conference on June 22nd and 23rd, co-organised with and . Great speaker line-up to follow soon, keep an eye on the website for more news

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  7. proslijedio/la je Tweet
    16. sij

    We are keen to provide participants with children with personalized solutions to be able to comfortably attend AISTATS. Please fill out We ❤️ family-friendly ML conferences! 👪👨‍👩‍👦‍👦👪

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  8. proslijedio/la je Tweet
    3. sij

    This is your 10 day reminder! Send your work at the interface of machine learning and healthcare to CHIL 2020 by January 13th. We have four tracks spanning machine learning, health policy, and practice. For more details, check out:

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  9. proslijedio/la je Tweet
    30. pro 2019.

    Are you a late-stage PhD student or a brand new postdoc? Consider submitting your best work to the Doctoral Consortium of ACM CHIL 2020 and get valuable feedback or collaborations going! Submission Deadline - February 3, 2020! Check out details here -

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  10. 29. pro 2019.

    The Confidence Code. So much love for this book. So much resonates. So much truth. A must read for all women. Thank you for introducing me to this book.

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  11. proslijedio/la je Tweet
    27. pro 2019.

    Earlier this month, I went to at NeurIPS. The talks were recorded, which is amazing. But I know you aren't going to watch 6 hours of video. Maybe you'll read 40 minutes of summary, though!

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  12. proslijedio/la je Tweet
    15. pro 2019.
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  13. proslijedio/la je Tweet
    14. pro 2019.

    I’m honored to have won the best paper award for the public policy track of the AI for Social Good workshop! Thanks to all the organizers and reviewers for putting this great day together!

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  14. 14. pro 2019.

    Nyalleng wrap up panel. She says that we need to take account of skills of local communities in solving local problems, even if those solutions are not AI. Also problems in local communities are not "their" problems, but these are "our" problems

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  15. proslijedio/la je Tweet
    14. pro 2019.

    "Our field has hubris, we think because we can recommend kitten videos that we can save the world." - paraphrasing

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  16. 14. pro 2019.

    Ben Green at the workshop on . We need to be as rigorous about the question of what is good as we are with regards to what is AI

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  17. proslijedio/la je Tweet
    13. pro 2019.

    Quite a somber discussion at ML4D today. My most important take away is: in development work, often that data point is a person and that person is probably not one of the powerful stakeholders. Handle with care.

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  18. 14. pro 2019.

    Excellent work by Ying-Qi Zhao on estimating uncertainty measurements and causal effects in the context of observational data

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  19. 14. pro 2019.

    A powerhouse of speakers at the workshop on “Do the right thing”: machine learning and causal inference for improved decision making Listening to Susan Murphy Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time

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  20. 14. pro 2019.

    The story has a happy ending where the authors of the paper worked with the manufacturers of the algorithm who retrained their models to reduce bias.

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  21. 14. pro 2019.

    Possible mechanisms for why this bias arises: disparity in access to treatment, but also disparity in how people are treated based on ethnographic studies, leading to a lack of trust in the healthcare system

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