An AI with an Agenda: How Our Biases Leak Into Machine Learning (NDC Oslo 2021)

@arthurdoler Arthur Doler Resources: Boring details about me are at
https://arthurdoler.com AN AI WITH AN AGENDA bit.ly/art-ai-agenda-ndc-oslo-2021 How Our Biases Leak Into Machine Learning

@arthurdoler I WANT TO PLAY A GAME Luckily for you
this isn’t a Saw movie.

@arthurdoler “YOU’RE AN INTELLIGENT” Not grammatically correct, but thank you!

@arthurdoler “YOU’RE A CUTE” Aw, thank you, too!

@arthurdoler I get asked this question a lot, actually.

@arthurdoler A classic robot.

@arthurdoler The robots are coming for your gains!

@arthurdoler Yes, a Rubik’s Cube IS important.

@arthurdoler RUDE!

@arthurdoler General apologies to KC Green

@arthurdoler Ur robots broken y’all

@arthurdoler Maybe try kicking it like Fonzie and the jukebox

@arthurdoler REAL CONSEQUENCES

@arthurdoler https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

@arthurdoler http://blog.conceptnet.io/posts/2017/how-to-make-a-racist-ai-without-really-trying/ Aylin Caliskan-Islam1 , Joanna J. Bryson1,2, and Arvind
Narayanan1, 2016

@arthurdoler http://blog.conceptnet.io/posts/2017/how-to-make-a-racist-ai-without-really-trying/

@arthurdoler Reality is often a big problem when it comes
to AI

@arthurdoler SIX TYPES OF PROBLEM WITH AI/ML You could call
them “classes”, if you will…

@arthurdoler Oh no, Scoob, it’s a g-g-g-ghost!

@arthurdoler The pack is in progress, those things are EXPENSIVE.

@arthurdoler Reminds me of the old NES Ghostbuster game…

@arthurdoler Class I – Phantoms of False Correlation Class II
– Specter of Biased Sample Data Class III – Shade of Overly-Simplistic Maximization Class IV – The Shadow of Understanding Class V – The Simulation Surprise Class VI – Apparition of Fairness Class VII – The Feedback Devil Tobin’s Spirit Guide this ain’t.

@arthurdoler A flock of bookbats!

@arthurdoler A lazy summer day

@arthurdoler This is definitely not “good eatin’”.

@arthurdoler YES I DREW A MEME SHUT UP

@arthurdoler A good question!

@arthurdoler This brings back memories of lab reports using Excel

@arthurdoler http://www.tylervigen.com/spurious-correlations - Data sources: Centers for Disease Control &
Prevention and Internet Movie Database

@arthurdoler http://www.tylervigen.com/spurious-correlations - Data sources: National Vital Statistics Reports and
U.S. Department of Agriculture

@arthurdoler http://www.tylervigen.com/spurious-correlations - Data sources: National Spelling Bee and Centers
for Disease Control & Prevention

@arthurdoler Who you gonna call?

@arthurdoler KNOW WHAT QUESTION YOU’RE ASKING UP FRONT This is
harder to remember than you’d think.

@arthurdoler USE CONDITIONAL PROBABILITY OVER CORRELATION I know I promised
no math, but…

@arthurdoler https://versionone.vc/correlation-probability/

@arthurdoler Sometimes the old ways are the best ways.

@arthurdoler It’s the free data they hand out in the
grocery stores, right?

@arthurdoler MORTGAGE LENDING ANALYSIS … Uh oh.

@arthurdoler For more on this, I really recommend taking some
data science courses.

@arthurdoler AI/ML is basically nothing but spreadsheets, if/elses, and math

@arthurdoler UH OH

@arthurdoler Childhood memories!

@arthurdoler Honestly, every piece data carries bias in how we
chose to measure it.

@arthurdoler I was just barely too young to make some
Real Mistakes with Beanie Babies

@arthurdoler *bloop*

@arthurdoler Shades of Jurassic Park

@arthurdoler HISTORY *choirs of angels singing*

@arthurdoler https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting- tool-that-showed-bias-against-women-idUSKCN1MK08G

@arthurdoler Twitter - @quantoidasaurus (Used with permission)

@arthurdoler YOUR SAMPLE MIGHT NOT BE REPRESENTATIVE This is relatively
simple to fix…

@arthurdoler YOUR DATA MIGHT NOT BE REPRESENTATIVE …but this is
a lot harder to fix.

@arthurdoler Blinky and Sue are out on vacation

@arthurdoler MODELS REPRESENT WHAT WAS THEY DON’T TELL YOU WHAT
SHOULD BE CARVE THIS INTO YOUR SOUL

@arthurdoler FIND A BETTER DATA SET! There are too many
efforts out there to really list at this point CONCEPTNET.IO

@arthurdoler BUILD A BETTER DATA SET! … if you take
this path, I wish you the best of luck

@arthurdoler A reasonable step, but…

@arthurdoler BEWARE SHADOW COLUMNS *dramatic music*

@arthurdoler MAKE SURE YOUR SAMPLE SET IS REPRESENTATIVE https://humansofdata.atlan.com/2017/07/6-sampling-techniques-choose-representative-subset https://blog.socialcops.com/academy/resources/6-sampling-techniques-choose-representative-subset

@arthurdoler Build your own dataset, Mark II!

@arthurdoler You’re in control here.

@arthurdoler IBM’S AI FAIRNESS TOOLKIT https://aif360.mybluemix.net

@arthurdoler AI FAIRNESS TOOLKIT https://aif360.mybluemix.net

@arthurdoler https://aif360.mybluemix.net

@arthurdoler https://pair-code.github.io/what-if-tool

@arthurdoler HAVE A GOOD PROCESS This is WAY harder than
it sounds

@arthurdoler KEEP IN MIND YOU NEED TO KNOW WHO CAN
BE AFFECTED IN ORDER TO UN-BIAS This means that you’re constantly playing catch-up!

@arthurdoler The absolute worst ghost

@arthurdoler PRICING ALGORITHMS Exciting!

@arthurdoler Calvano, Calzolari, Denicolò and Pastorello (2018)

@arthurdoler Cournot, 1838

@arthurdoler Calvano, Calzolari, Denicolò and Pastorello (2018)

@arthurdoler WHAT IF AMAZON BUILT A SALARY TOOL INSTEAD? Big
Oof

@arthurdoler THE BRATWURST PROBLEM Thanks always to my coworker Charlie
for creating this example

@arthurdoler Class III and Class IV are very closely related

@arthurdoler They’ve taken quite the beating this year

@arthurdoler The word “everyday” in that sentence is doing so
much work

@arthurdoler It was an honest question

@arthurdoler The best possible place for advice

@arthurdoler I guess in the end, we live in a
world of contrasts

@arthurdoler HUMANS ARE RARELY SINGLE-MINDED We’re probably closer to “uncountably
infinite-minded”

@arthurdoler All bosses wear ties. This is a fact.

@arthurdoler Poor robots.

@arthurdoler https://www.alexirpan.com/2018/02/14/rl-hard.html; Gu, Lillicrap, Sutskever, & Levine, 2016

@arthurdoler ♫ Pacman Fever…

SHOULD BE Once again I am asking you to remember this

@arthurdoler DON’T TRUST ALGORITHMS TO MAKE SUBTLE OR LARGE MULTI-VARIABLE
JUDGEMENTS Don’t trust them to make judgements at all, tbqh

@arthurdoler

@arthurdoler The ghosts are getting nastier

@arthurdoler Lehman, Clune, & Misevic, 2018

@arthurdoler Ellefsen, Mouret, & Clune, 2015 Lehman, Clune, & Misevic,
2018

@arthurdoler Pac-Man’s music/SFX came from a custom-made 3-channel waveform generator

@arthurdoler BE READY Luckily this one is rather simple to
deal with

@arthurdoler DON’T CONFUSE THE MAP WITH THE TERRITORY There is
an entire hour’s worth of content for this topic alone

@arthurdoler VERIFY AND CHECK SOLUTIONS DERIVED FROM SIMULATION This means
it’s harder to use in complex or large scenarios!

@arthurdoler Azatlor the Destroyer, in repose

@arthurdoler The dialog here was sourced from 2016

@arthurdoler BUT WHAT HAPPENS WITH DIALECTAL LANGUAGE? Blodgett, Green, and
O’Connor, 2016

@arthurdoler MANY AI/ML TOOLS ARE TRAINED TO MINIMIZE AVERAGE LOSS
Methods like mean squared error, etc

@arthurdoler Good old R-squared

@arthurdoler REPRESENTATION DISPARITY Hashimoto, Srivastava, Namkoong, and Liang, 2018

@arthurdoler The story of why “Pac-Man” is named that is
a hilarious corporate tale

@arthurdoler CONSIDER PREDICTIVE ACCURACY AS A RESOURCE TO BE ALLOCATED
Hashimoto, Srivastava, Namkoong, and Liang, 2018

@arthurdoler DISTRIBUTIONALLY ROBUST OPTIMIZATION Hashimoto, Srivastava, Namkoong, and Liang, 2018

@arthurdoler Pictured: a juvenile Slor, restrained

@arthurdoler LET’S BUILD A PRODUCT WITH OUR TWITTER NLP Like
it’s 2019 again

@arthurdoler WHAT HAPPENS TO PEOPLE WHO USE DIALECT? OH NOOOOO

@arthurdoler PREDICTIVE POLICING Bear with me, I try not to
make this political

@arthurdoler Image via Reddit, Author u/jakeroot

@arthurdoler Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017

@arthurdoler Pac-Man was created by only three people.

@arthurdoler IGNORE OR ADJUST FOR ALGORITHM-SUGGESTED RESULTS Again, there’s easily
an hours’ worth of content here

@arthurdoler LOOK TO CONTROL ENGINEERING YES! My computer engineering degree
finally pays off!

@arthurdoler By Arturo Urquizo - http://commons.wikimedia.org/wiki/File:PID.svg, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=17633925

@arthurdoler

@arthurdoler CLASS I - PHANTOMS OF FALSE CORRELATION Know what
question you’re asking Trust conditional probability over straight correlation Push those phantoms to the curb!

@arthurdoler CLASS II - SPECTER OF BIASED SAMPLE DATA Recognize
data is biased even at rest Make sure your sample set is crafted properly Excise problematic predictors, but beware their shadow columns Build a learning system that can incorporate false positives and false negatives as you find them Try using adversarial techniques to detect bias Shank those specters!

@arthurdoler CLASS III - SHADE OF OVERLY-SIMPLISTIC MAXIMIZATION Remember models
tell you what was, not what should be Try combining dependent columns and predicting that Try complex algorithms that allow more flexible reinforcement Shoot those shades!

@arthurdoler CLASS V – THE SIMULATION SURPRISE Don’t confuse the
map with the territory Always reality-check solutions from simulations Smack down those surprises!

@arthurdoler CLASS VI - APPARITION OF FAIRNESS Consider predictive accuracy
as a resource to be allocated Possibly seek external auditing of results, or at least another team Abjure those apparitions!

@arthurdoler CLASS VII - THE FEEDBACK DEVIL Ignore or adjust
for algorithm-suggested results Look to control engineering for potential answers Dunk on those devils!

@arthurdoler

SHOULD BE Oh hey it’s this slide again

@arthurdoler They need jobs too!

@arthurdoler OR GET TRAINING There are a lot of options
these days

@arthurdoler The cursing is common when solving data problems, too

@arthurdoler AI Now Institute Georgetown Law Center on Privacy and
Technology Knight Foundation’s AI ethics initiative fast.ai Algorithmic Justice League And more!

@arthurdoler ABIDE BY ETHICS GUIDELINES https://towardsdatascience.com/5-principles-for-big-data-ethics-b5df1d105cd3

@arthurdoler Privacy / Consent Transparency of Use Transparency of Algorithms
Ownership Thorny but important issues

@arthurdoler https://www.accenture.com/_acnmedia/PDF-24/Accenture-Universal-Principles-Data-Ethics.pdf

@arthurdoler Arthur Doler My website is probably still https://arthurdoler.com Resources:
bit.ly/art-ai-agenda-ndc-oslo-2021

An AI with an Agenda: How Our Biases Leak Into ...

An AI with an Agenda: How Our Biases Leak Into Machine Learning (NDC Oslo 2021)

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