RubyConf 2015

[Keynote] Las responsabilidades éticas de diseñar un algoritmo

Carina C. Zona  · 


Extracto de la transcripción automática del vídeo realizada por YouTube.

alright good morning 800 people who did that all right so this is called consequences of an insightful algorithm and this talk is a tool kit for empathetic coding and we're going to be delving into some really specific issues and examples of uncritical

programming and the painful results from doing things in ways that were benignly intended and so I want to start off with a content warning because I'm going to be delving into examples that deal with a number of very sensitive topics grief PTSD depression

miscarriage infertility sexual history consent stalking racial profiling and Holocaust and while none of those are the main topic they are examples that are going to come up so anyone who feels the sudden need for coffee please do I won't feel it all offended

that's about 10 minutes in so you've got some time to think about that algorithms impose consequences on people all the time and we're able to extract remarkably precise insights about an individual but the question is do we have a right to know

what they didn't consent to share even when they willingly share the data that leads us there and how do we mitigate against unintended consequences from doing that so let's start by asking a very basic question what is an algorithm it's a step-by-step

set of operations for predictably arriving at an outcome this is a very generic definition and of course usually when we talk about algorithms we're talking about those of computer science or mathematics the patterns of instructions articulated in code

or in mathematical formulas but you can also think of algorithms in everyday life their patterns of instructions in all sorts of different ways articulated for example as recipes or directions or even a crochet pattern deep learning is the new hotness right

now for data mining essentially it's algorithms for fast trainable artificial neural networks this is a branch of machine learning that's been around since the early 80s but mostly it's been locked in academia in part because of difficulties with

scale very recently there's been a number of breakthroughs that have made it possible to finally put this in real production so it's become realistically possible to extract really meaningful insights out of big data and I'm talking about in production

so another way to think of this is that deep learning relies on an an ins automatic discovery of patterns within a training data set and those are applied to drawing intuitions about future inputs so just in terms of process what this means is those inputs

that we call training data can be various things an array of words images sounds objects concepts and when I say in a way it's like a few terabytes so the so you have inputs and then execution is simply running a series of functions repeatedly on the array

and that iteration is referred to as layers and with each layer it's getting more and more precise more fine-grained it's running each of those except with algorithms on the next set of findings and so it's really getting down to a very precise

level of thousands of factors and this is all without having to label data categorize it you're just throwing the data at it and notice what it that means because deep learning is premise on a black box the an N has drilled down to those tens or hundreds

of thousands of factors but they're really subtle and it's belief has predictive value but we don't know what it's facing that on so right now this is a technology that's driving major advances in a variety of arras medical diagnostics

pharmaceuticals predictive text including skypes voice activated commands like siri fraud detection sentiment analysis language translation and even the self-driving cars and more specifically today we're going to look at some really concrete examples

of those including ad targeting behavioral prediction image classification and facial recognition but you know all this sounds a little abstract so let's look at a really sort of simple concrete example that's kind of fun this is mar io it's an

aann that teach itself how to play super mario world it starts with absolutely no clue whatsoever and I mean about its world about even the concept of rules or gaming all it does is manipulate numbers and it notices that sometimes things happen it notices

that some of those things cumulatively produce outcomes so it's learning movement and play via a purely self training session and it does this in those layers for 24 hours of experimentation that leads it to identify patterns and use those patterns to

predict insights such that it's actually able to play the game so speaking of games let's play one right now it looks something like bingo but it's called data mining fail insightful algorithms are full of pitfalls like these so by looking at case

studies we're able to explore some of the things on this board so are you ready alright so here's some that we're going to play if the first one is target in the retail sector the second month the second I'm sorry trimester of pregnancy is

referred to as the Holy Grail and the reason is because it's one of the few times in our lives where all of our shopping habits suddenly come up for sale again so are buying loyalty our store loyalty our brand loyalty everything we do in terms of spending

suddenly we're rethinking and it's a great opportunity obviously to create a lifetime customer potentially even if family lifetime customer this is valuable stuff most retailers are only able to use data to find about the third trimester so one day

target had a few marketers who walked across the office and asked one of the programmers a really simple question if we wanted to figure out if a customer is pregnant even if she doesn't want us to know could you do that this is a really interesting challenge

right I mean would you think about it would you wonder an experiment a bit so he actually did come up with an algorithm and it turned out to be very reliable so they started sending out ads ads for maternity ads for infant care one day a man comes into one

of the stores and he's very angry he's yelling how dare you you sent this to my teenage daughter are you trying to tell her I've sex are you trying to tell her to get pregnant now the store manager obviously is not in charge of the National mailers

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