DjangoCon 2015

Consecuencias sociales y responsabilidad ética de nuestro código

Carina C. Zona  · 




Extracto de la transcripción automática del vídeo realizada por YouTube.

we didn't do a mic level check it does work yay okay alright so yeah this talk is meant to be a tool kit for empathetic coding and we're gonna be delving into some pretty specific examples of uncritical programming so the first thing I want to do because

we're gonna be talking about painful results of doing things in ways that are perfectly benignly intended and then sides white people is I'm not gonna do that to you so start with a Content warning here we're gonna be delving into examples that

include a bunch of things including grief PTSD depression miscarriage infertility racial profiling concentration camps surveillance sexual history consent and assault and while we won't be dwelling on those that is topics that are going to come up all

right so algorithms impose consequences on people all the time we're able to extract such remarkably precise insights about any individual but we haven't really asked ourselves enough do we have a right to know what they didn't consent to share

with us even when they willingly share other data that ends up leading us there and we have to be asking ourselves more about how do we mitigate against unintended consequences of that stuff so it's helpful to step back a little and start with asking just

a really basic question what is an algorithm and generically speaking it's just a step by step set of operations for predictively arriving at a conclusion and predictably is a really operative word in that so obviously when we talk about algorithms usually

we meet in the computer science sense in the mathematics sense patterns of instructions articulated in code or in formulas but you can also think of algorithms as being part of everyday life all the time they're just patterns of instructions articulated

in other ways such as for instance a recipe or a lovely shawl you know if your worst code nightmare looks like this you're doing well okay so deep learning is a particular new actually new old style of algorithms so the technology goes back to in theory

at least the 1950s but there's been some recent breakthroughs since 2012 and 2013 that have really reshaped the landscape of what's possible with machine learning it's really just the new hotness because of this oversimplified you can think of

machine deep learning as algorithms for fast trainable artificial neural networks it's a technology that you know like I said it's been around in academia for instance for a long time but really mostly a theoretical scale breakthroughs in parallelization

in availability of processors at massive scale these things are making it much more viable to actually take this now into production use so because of that deep learning has become realistically able to extract insights out of vast big data in ways that we've

never really even been able to imagine about before deep learning is a particular approach to building and training artificial neural networks you could think of them as decision-making black boxes so what does that mean okay so we have some inputs you're

just giving an array of numbers representing words concepts objects in some sense and then executing it by running a series of functions that you write and executing against the array and the outputs you get are the machine learning predicting properties that

it thinks will be useful in the future for drawing intuitions about similar data sets so you give it this training data set it says hmm I noticed some patterns here this is probably how you can figure out similar results next time and then you can throw much

larger j-just sets at it and it's able to come up with similar predictions so what would that look like for instance okay so there's a few problems here black box we'll get back to that in a second so because deep learning relies on an artificial

neural networks automated discovery of patterns within that training data set it gets to apply those discoveries to draw intuitions about the future inputs however that means that every flaw or a so in the training data set or in those original functions that

you've written are going to have unrecognized influence on the algorithms and on the outcomes that they generate so that's something that we're gonna revisit but let's look at a really just very sick practical example of deep learning ma Rio

it's an artificial neural network that teaches itself how to play Super Mario World it starts with absolutely no clue whatsoever there's a great YouTube video of this by the way in action it spends 24 hours simply manipulating numbers and seeing what

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