CATHY O’NEIL ~ WEAPONS OF MATH DESTRUCTION ~ 2016

~ TC-MIATCT-M-WNODEITWP-BAFMOT-THC-TCOMFI/TROU-AHBAAAB-MWMF ~ The Crash – Made It All Too Clear That – Mathematics – Was Not Only Deeply Entangled In The World’s Problems – But Also Fueling Many Of Them – The Housing Crisis / The Collapse Of Major Financial Institutions / The Rise Of Unemployment – All Had Been Aided And Abetted By – Mathematicians Wielding Magic Formulas

~ IWHBC-H-WAWHTASBATPTFOHMHBM-A-HWC-PASCITF-B-I-ITWOTC-NMT-WHTE-A-EISMD-MAS-W-SO-D/M/SP-TWPOT-A-COPA-S/W/L/C ~ If We Had Been Clear-Headed – We All Would Have Taken A Step Back At This Point To Figure Out How Math Had Been Misused – And – How We Could – Prevent A Similar Catastrophe In The Future – But – Instead – In The Wake Of The Crisis – New Mathematical Techniques – Were Hotter Than Ever – And – Expanding Into Still More Domains – Mathematicians And Statisticians – Were – Studying Our – Desires / Movements / Spending Power – They Were Predicting Our Trustworthiness – And –Calculating Our Potential As – Students / Workers / Lovers / Criminals

~ T-T-M-PA-PTDE-WBO-CMBFHB-SOTCWNDMWTBI-N-MOTME-H-P/M/B-IT-SS-TIMOL ~ Trouble – The – Math-Powered Applications – Powering The Data Economy – Were Based On – Choices Made By Fallible Human Beings – Some Of These Choices Were No Doubt Made With The Best Intentions – Nevertheless – Many Of These Models Encoded – Human – Prejudice / Misunderstanding / Bias – Into The – Software Systems – That Increasingly Managed Our Lives

LG-T-MM-W-O-TWITABTHPITD-TV-EW-WOH-W-BDOA-A-TTTPTPATOIOS-WMTRR ~ Like Gods – These – Mathematical Models – Were – Opaque – Their Workings Invisible To All But The Highest Priests In Their Domain – Their Verdicts – Even When – Wrong Or Harmful – Were – Beyond Dispute Or Appeal – And – They Tended To Punish The Poor And The Oppressed In Our Society – While Making The Rich Richer

~ ATR-H-B/P/P-T-A-I-NEJ Attempting To Reduce – Human – Behaviour / Performance / Potential – To – Algorithms – Is – No Easy Job

~ A-BD-C-R-RCT-MTOV ~ At – Big Data – Companies – Researchers – Run Constant Tests – And – Monitor Thousands Of Variables

~ WF-A-SE-CCSOFADA-WNLFIM ~ Without Feedback – A – Statistical Engine – Can Continue Spinning Out Faulty And Damaging Analysis – While Never Learning From Its Mistakes

~ DTOR-A-UITJTR-TTOM-I-S-P-HD-A-VC ~ Define Their Own Reality – And – Use It To Justify Their Results – This Type Of Model – Is – Self-Perpetuating – Highly Destructive – And – Very Common

~ P-DIAS-WCTSLUD-A-T-HV-AHTAFHSOETTAT ~ Probability – Distilled Into A Score – Which Can Turn Someone’s Life Upside Down – And – The – Human Victims – Are Held To A Far Higher Standard Of Evidence Than The Algorithms Themselves

~ TTIT-PEUSAA-SI-O-P-F-T-WSTDCCUAAA-THB-DS-ATOLSOTFOTREOTT ~ The Trouble Is That – Profits End Up Serving As A – Stand-In – Or – Proxy – For – Truth – We’ll See This Dangerous Confusion Crop Up Again And Again – This Happens Because – Data Scientists – All Too Often Lose Sight Of The Folks On The Receiving End Of The Transaction

~ BD-HPOE ~ Big Data – Has Plenty Of Evangelists

~ TCAM-WMCAWIETI-STWIA-TV-TCBEU-A-FWWCI-I-FAA ~ To Create A Model – We Make Choices About What’s Important Enough To Include – Simplifying The World Into A – Toy Version – That Can Be Easily Understood – And – From Which We Can Infer – Important – Facts And Actions

~ AM-BS-RT-JAP-OI-C ~ A Model’s – Blind Spots – Reflect The – Judgments And Priorities – Of Its – Creators

~ AMBFT-WWABWT-IWGSIINCU ~ A Model Built For Today – Will Work A Bit Worse Tomorrow – It Will Grow Stale If It’s Not Constantly Updated

~ R-I-TMSOPM-IIPB-HDG-A-SC-RB-II-A-PB-CB ~ Racism – Is – The Most Slovenly Of Predictive Models – It Is Powered By – Haphazard Data Gathering – And – Spurious Correlations – Reinforced By – Institutional Inequities – And – Polluted By – Confirmation Bias

~ T-PFL-T-DL-GRAR-A-ITP-TMBMAMU ~ The – Pernicious Feedback Loop – This – Destructive Loop – Goes Round And Round – And – In The Process – The Model Becomes More And More Unfair

~ B-TPINW-SPB-IT-SMS-TMPBA-SDITFOMOP-OFTFOR-A-ONA-TU ~ But – The Point Is Not Whether – Some People Benefit – It’s That – So Many Suffer – These Models Powered By Algorithms – Slam Doors In The Face Of Millions Of People – Often For The Flimsiest Of Reasons – And – Offer No Appeal – They’re Unfair

~ A-HOMTAA:TCL-FOFTTN-A-TOD ~ And – Here’s One More Thing About Algorithms: They Can Leap – From One Field To The Next – And – They Often Do

~ BA-TVSS-TTSTC ~ Bet Against – The Very Same Securities – That They’d Sold To Customers

~ TBWMAPWRWABS-UC-EA-U-TWTC ~ The Bonds Were Marketed As Products Whose Risk Was Assessed By Specialists – Using Cutting-Edge Algorithms – Unfortunately – This Wasn’t The Case

~ TMWDATCAASS ~ The Math Was Directed Against The Consumer As A Smoke Screen

~ B-O-T-O-U-IOU ~ Billions – Or – Trillions – Of – Unpayable – IOUs

~ VFPHT-EATIR-TKWWAGOS-A-MOTPWD-LTITSU ~ Very Few People Had The – Expertise And The Information Required – To Know What Was Actually Going On – And – Most Of The People Who Did – Lacked The Integrity To Speak Up

~ T-F-A-NMPWDATST-WBOT-T-STBD-T-DWL-RAU

~ The – False – Assumption – Not Many People Would Default At The Same Time – Was Based On The – Theory – Soon To Be Disproven – That – Defaults Were Largely – Random And Unrelated

~ TPO-MC-FF-AASUIH ~ The Power Of – Modern Computing – Fueled Fraud – At A Scale Unequalled In History

~ TWE-FSTPOTOSOTA ~ That’s When Everyone – Finally Saw The People On The Other Side Of The Algorithms

~ B-2009-IWCTTLOTMCHBNNDTTWOFAHINNV-TLS-FTMP-A-TGRTS:TRIDM-EFAFRTAAFHTJT ~ By – 2009 – It Was Clear That The Lessons Of The Market Collapse Had Brought No New Direction To The World Of Finance And Had Instilled No New Values – The Lobbyists Succeed – For The Most Part – And – *The Game Remained The Same: To Rope In Dumb Money – Except For A Few Regulations That Added A Few Hoops To Jump Through

~ TRTAR-RDIF ~ The Refusal To Acknowledge Risk – Runs Deep In – Finance

~ TAWMST-TD-L-WRTW ~ The Algorithms Would Make Sure That – Those Deemed – Losers – Would Remain That Way

~ ALM-WGEMCOT-DE-TI-OF-A-CTATW-TTDI ~ A Lucky Minority – Would Gain Ever More Control Over The – Data Economy – Taking In – Outrageous Fortunes – And – Convincing Themselves All The While – That They Deserved It

~ H-WUCAMFP-IIFST-GI-B-PAETMTTCRTR ~ However – When You Create A Model From Proxies – It Is Far Simpler To – Game It – Because – Proxies Are Easier To Manipulate Than The Complicated Reality They Represent

~ W-CP-BATW-TO-T-ANO-S-B-F ~ We – Criminalize Poverty – Believing All The While – That Our – Tools – Are Not Only – Scientific – But – Fair

~ J-CJBSTOPOSIUTO ~ Justice – Cannot Just Be Something That One Part Of Society Inflicts Upon The Other

~ OR ~ Operations Research

~ B-ITWFI ~ Bogus – Is The Word For It

~ C-GT ~ Computer-Generated Terrorism

~ B-AOTATP-HOMOP-F-TODS-IGTGALOTFW ~ But – Any Operation That Attempts To Profile – Hundreds Of Millions Of People – From – Thousands Of Different Sources – Is Going To Get A Lot Of The Facts Wrong

~ M-A-LO-ALA-TSRFOTE ~ Mistakes – Are – Learning Opportunities – As Long As – The System Receives Feedback On The Error

~ R-ISWU-TI-FMSF ~ Redlining – Is Still With Us – Though In – Far More Subtle Forms

~ IWGTB-EBTL-O-TEAV-WCSF-STDUIDC-A-TUD ~ If We’re Going To Be – Equal Before The Law – Or – Treated Equally As Voters – We Cannot Stand For – Systems That Drop Us Into Different Castes – And – Treat Us Differently

~ TG-OC-HAP-RR-TP ~ The Government – Of Course – Has A Powerful – Regulatory Role – To Play

~ MAC-NFD-B-FTCWM-A-WDTPAT-A-WTLO-TCANJA-L/P/E-TA-FM ~ Models Are Constructed – Not Just From Data – But – From The Choices We Make – About – Which Data To Pay Attention To – And – Which To Leave Out – Those Choices Are Not Just About – Logistics / Profits / Efficiency – They Are – Fundamentally Moral

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