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math_rachel's profile
Rachel Thomas
Rachel Thomas
Rachel Thomas
@math_rachel

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Rachel Thomas

@math_rachel

co-founder http://fast.ai , professor of practice @QUTDataScience | machine learning, data, ethics, algorithmic harms, math PhD (she/her)

Brisbane, Australia
rachel.fast.ai
Joined May 2013

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    Rachel Thomas‏ @math_rachel 25 May 2019

    An audit of a resume screening algorithm found that the two factors most indicative of job performance were being named Jared and playing high school lacrosse https://qz.com/1427621/companies-are-on-the-hook-if-their-hiring-algorithms-are-biased/ …pic.twitter.com/2oZHYVAWSb

    Mark J. Girouard, an employment attorney at Nilan Johnson Lewis, says one of his clients was vetting a company selling a resume screening tool, but didn’t want to make the decision until they knew what the algorithm was prioritizing in a person’s CV.

After an audit of the algorithm, the resume screening company found that the algorithm found two factors to be most indicative of job performance: their name was Jared,
    9:42 AM - 25 May 2019
    • 1,331 Retweets
    • 3,174 Likes
    • zen frog Fernando Franco ifdurdad Devin Howard Sol Ozzan Martina 🐸 unbalancedparen | fat-tailed 🐸 Brad Spendlove 🤖🧠💡 Nick’s Tics
    67 replies 1,331 retweets 3,174 likes
      1. New conversation
      2. Rachel Thomas‏ @math_rachel 26 May 2019

        Rachel Thomas Retweeted Rachel Thomas

        For those interested in learning more about bias in machine learning:https://twitter.com/math_rachel/status/1006984583135428608 …

        Rachel Thomas added,

        Rachel Thomas @math_rachel
        My @QConAI keynote on "Analyzing and Addressing Bias in AI" is now online: https://www.infoq.com/presentations/unconscious-bias-machine-learning …
        3 replies 19 retweets 94 likes
        Show this thread
      3. Rachel Thomas‏ @math_rachel 26 May 2019

        Rachel Thomas Retweeted Rachel Thomas

        Some additional posts & talks on the topics of bias & ethics in machine learning:https://twitter.com/math_rachel/status/1112761223114711041 …

        Rachel Thomas added,

        Rachel Thomas @math_rachel
        Here's some of my writing/speaking on YouTube's runaway feedback loop & bias in ML: https://www.fast.ai/2019/01/29/five-scary-things/ … https://youtu.be/LqjP7O9SxOM  https://www.fast.ai/2017/11/02/ethics/ … https://www.fast.ai/2018/08/07/hbr-bias-algorithms/ … https://www.infoq.com/presentations/unconscious-bias-machine-learning … https://youtu.be/WC1kPtG8Iz8 
        Show this thread
        1 reply 10 retweets 49 likes
        Show this thread
      4. Rachel Thomas‏ @math_rachel 27 May 2019

        Rachel Thomas Retweeted Rachel Thomas

        16 things you can do to make tech more ethicalhttps://twitter.com/math_rachel/status/1124385374040514560 …

        Rachel Thomas added,

        Checklist for data projects
Conduct ethical risks sweeps
Resist the tyranny of metrics
Choose a revenue model other than advertising
Have product & engineering sit with trust & safety
Advocate within your company
Organize with other employees
Leave your company when internal advocacy no longer works
Avoid non-disparagement agreements
Support thoughtful regulations & legislation
Speak with reporters
Decide in advance
        Rachel Thomas @math_rachel
        16 Things You Can Do to Make Tech More Ethical (a 3-part series) https://www.fast.ai/2019/04/22/ethics-action-1/ … https://www.fast.ai/2019/04/25/ethics-action-2/ … https://www.fast.ai/2019/05/03/ethics-action-3/ … pic.twitter.com/EURTj0o8VX
        Show this thread
        3 replies 9 retweets 29 likes
        Show this thread
      5. Rachel Thomas‏ @math_rachel 27 May 2019

        Rachel Thomas Retweeted Rachel Thomas

        Lack of diversity has contributed to algorithmic bias & misuse:https://twitter.com/math_rachel/status/1124384072975863808 …

        Rachel Thomas added,

        Me standing on stage, in front of a slide that has the headlines: "'We're in a diversity crisis': cofounder of Black in AI on what's poisoning algorithms in our lives.", "Diversity crisis in AI" and "AI is the future- but where are the women?"
        Slide saying: Sometimes people ask me if it's dangerous to make AI accessible to more people.  What's dangerous is a homogeneous & exclusive group creating technology that impacts us all.
        Rachel Thomas @math_rachel
        At TEDx, I spoke about 3 aspects of AI that scare me: - Encodes & magnifies bias - Incentivizes conspiracy theories - Surveillance and predictive policing Lack of diversity in the field has contributed to these misuses. https://www.youtube.com/watch?v=LqjP7O9SxOM&list=PLtmWHNX-gukLQlMvtRJ19s7-8MrnRV6h6 … pic.twitter.com/hA8lugV6qo
        Show this thread
        0 replies 3 retweets 19 likes
        Show this thread
      6. End of conversation
      1. snoring url‏ @SnoringUrl 25 May 2019
        Replying to @math_rachel @triketora

        Imagine working with non-English names !!

        0 replies 0 retweets 3 likes
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      1. New conversation
      2. Mike‏ @mousedroppings 25 May 2019
        Replying to @math_rachel @Susan_Hennessey

        Predictive of good job performance? or bad?

        1 reply 0 retweets 0 likes
      3. MWR DBM‏ @mwr_dbm 25 May 2019
        Replying to @mousedroppings @math_rachel @Susan_Hennessey

        Both. Lack of being named Jared is clearly a drag on my performance.

        0 replies 0 retweets 0 likes
      4. End of conversation
      1. fiftyseven58mph‏ @fiftyseven58mph 25 May 2019
        Replying to @math_rachel @Susan_Hennessey

        Challenge....all rising stars named Jared and who played lacrosse stand!

        0 replies 0 retweets 2 likes
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      1. I Brake 4 Ants‏ @ibrake4ants 25 May 2019
        Replying to @math_rachel @Susan_Hennessey

        I thought this was going to be a Jared Kushner joke... 😂

        0 replies 0 retweets 3 likes
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      2. 司徒‏ @szetoinsitu 26 May 2019
        Replying to @rs505 @math_rachel @blakehounshell

        That’s something they’d rather not admit.. But it could be parsed by college grad date?

        0 replies 0 retweets 2 likes
      3. End of conversation

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