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o_guest's profile
Olivia Guest | Ολίβια Γκεστ
Olivia Guest | Ολίβια Γκεστ
Olivia Guest | Ολίβια Γκεστ
@o_guest

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Olivia Guest | Ολίβια Γκεστ

@o_guest

• goth gremlin • computational cognitive/neuroscience modeling • geek & techish Cypriot • plant aficionada • came up with #bropenscience • http://neuroplausible.com  •

Τότεναμ, Λονδίνο & Cyprus
olivia.science
Joined October 2015

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    1. Catherine D'Ignazio‏ @kanarinka 22 May 2018
      • Report Tweet

      @schock says: algorithms should not be “color blind” (equality model that works best for ppl in power). Algorithms should be just (equity model that takes history & intersectionality into account). #datajustice18pic.twitter.com/JweD79JhNi

      1 reply 24 retweets 55 likes
    2. Ryan Persson‏ @persson_ryan 22 May 2018
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      Replying to @kanarinka @o_guest @schock

      This will be harder to do, as just blindly training algorithms on various data sets won't be enough. But I think it is necessary and worth doing. Otherwise stereotypes could become entrenched in decision making and analytics algorithms and create negative feedback loops.

      1 reply 2 retweets 3 likes
    3. Ryan Persson‏ @persson_ryan 23 May 2018
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      Replying to @persson_ryan @kanarinka and

      Good example is: Let's say a machine learning algorithm is fed data and predicts possibility of defaulting on a loan. If this algorithm is just blindly fed data, it could inadvertently develop a stereotype against African Americans as a larger percentage are -

      1 reply 3 retweets 3 likes
    4. Ryan Persson‏ @persson_ryan 23 May 2018
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      Replying to @persson_ryan @kanarinka and

      likely to be lower socioeconomic status than the general population. Such a stereotype buried in a machine learning algorithm would then be self perpetuating if it were used in banks decision making processes. We need to be vigilant against such biases showing up in our software

      1 reply 1 retweet 2 likes
    5. Olivia Guest | Ολίβια Γκεστ‏ @o_guest 23 May 2018
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      Replying to @persson_ryan @kanarinka @schock

      You don't even need an example that brings SES into it (not in any direct way at least), as demonstrated by this huge fail: https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773 …

      2 replies 4 retweets 6 likes
    6. Dr. Kate Shaw MA., MS., PsyD.‏ @katelovesneuro 23 May 2018
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      Replying to @o_guest @persson_ryan and

      Prob is algorithmsR mathematical opinions. Its not only about whose writing them but whose using them, 4what purpose& w/what inputs. Just like AI&ML learns 4m us via multiplicity of megadata sets(God help us) algorithms, once in t/users hands canB used2 exponentially turn up UGLY

      1 reply 0 retweets 3 likes
    7. Dr. Kate Shaw MA., MS., PsyD.‏ @katelovesneuro 23 May 2018
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      Replying to @katelovesneuro @o_guest and

      I may be wrong. But thats what I worry about most. Its already happening on lower scales but its about to explode (see story a few days back about LAPD using predictive tech for policing of crimes)

      1 reply 0 retweets 3 likes
    8. Olivia Guest | Ολίβια Γκεστ‏ @o_guest 23 May 2018
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      Replying to @katelovesneuro @persson_ryan and

      Yeah, predictive stuff in and of itself is obviously something as a society we have to agree on, which I feel we haven't yet had that mature argument/discussion, let alone ushering in predictive racist stuff.

      1 reply 0 retweets 3 likes
    9. Olivia Guest | Ολίβια Γκεστ‏ @o_guest 23 May 2018
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      Replying to @o_guest @katelovesneuro and

      What I mean by mature is: we already have laws that say "if you threaten to kill somebody and it's a real threat" it's a crime (at least here in the UK) and that is obviously on the spectrum of "predictions" and "punishing before the act has taken place".

      1 reply 0 retweets 2 likes
    10. Olivia Guest | Ολίβια Γκεστ‏ @o_guest 23 May 2018
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      Replying to @o_guest @katelovesneuro and

      Same with terrorism. People in the UK get caught before they have done anything , when they are in the planning stage. This is on spectrum of predictions.

      1 reply 0 retweets 2 likes
      Olivia Guest | Ολίβια Γκεστ‏ @o_guest 23 May 2018
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      Replying to @o_guest @katelovesneuro and

      We need to have a non-hyperbolic and mature discussion about how richer data than just "we saw him buying acid" is used to stop crime and/or define a new category of crime. And yes, of course, like with all laws/everything everywhere we need to be less racist about it.

      9:18 AM - 23 May 2018
      • 4 Likes
      • Catherine D'Ignazio Ryan Persson Melissa Kline Dr. Kate Shaw MA., MS., PsyD.
      1 reply 0 retweets 4 likes
        1. New conversation
        2. Dr. Kate Shaw MA., MS., PsyD.‏ @katelovesneuro 23 May 2018
          • Report Tweet
          Replying to @o_guest @persson_ryan and

          Many of the most telling predictive crime variables R specific 2t/community in which crime occur &some times that difference is manifestly evident on block by block basis. What about inputs based on what people in that community know?

          1 reply 0 retweets 2 likes
        3. Dr. Kate Shaw MA., MS., PsyD.‏ @katelovesneuro 23 May 2018
          • Report Tweet
          Replying to @katelovesneuro @o_guest and

          I guarantee they know more than even local police about whats really a predictive cue/variable in that context. To the larger issues, science/scientists have for so long been enamoured with creating stuff that we gave little if any thought to ethical issues that would arise

          1 reply 1 retweet 1 like
        4. Dr. Kate Shaw MA., MS., PsyD.‏ @katelovesneuro 23 May 2018
          • Report Tweet
          Replying to @katelovesneuro @o_guest and

          once our "whatever it is" was gifted to civil society. Issues related to policy and use of that "gift" didnt occupy our thinking much. & civil society/government/business seeing a capitalist market, medical or military use for said gift, rushed in. By the time we realize

          1 reply 1 retweet 2 likes
        5. Dr. Kate Shaw MA., MS., PsyD.‏ @katelovesneuro 23 May 2018
          • Report Tweet
          Replying to @katelovesneuro @o_guest and

          that discussion is needed, its all over e.g. wired medical implants issues: easily hackable, the grid, vulnerable to the max so far etc...Just wondering if there's a more temperate way to develop what society then uses. We could start by presenting ethics and policy

          1 reply 1 retweet 1 like
        6. Dr. Kate Shaw MA., MS., PsyD.‏ @katelovesneuro 23 May 2018
          • Report Tweet
          Replying to @katelovesneuro @o_guest and

          recommendations with each developed product b/c lets face it, no one better knows the product and the potential for abuses than the scientist developer. The reach and impact of ML, AI, predictive modeling is global and the impact depending on whose got it and how used is too.

          1 reply 1 retweet 2 likes
        7. Dr. Kate Shaw MA., MS., PsyD.‏ @katelovesneuro 23 May 2018
          • Report Tweet
          Replying to @katelovesneuro @o_guest and

          Somewhere in this chain some group has to take the responsibility to engage these Qs b4 use. Why not us researcher/scientist/clinician/developers? Just a thought

          0 replies 1 retweet 2 likes
        8. End of conversation

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