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View undoing-the-pain.org.md
View out-of-service.org
View NORTH STAR 1(1) Dec 3 1847.md

THE NORTH STAR

FREDERICK DOUGLASS, M. R. DELANY, Editors

RIGHT IS OF NO SEX - TRUTH IS OF NO COLOR - GOD IS THE FATHER OF US ALL, AND ALL WE ARE BRETHEREN

WILLIAM C. NEIL, Publisher JOHN DICK, Printer


View merry-xmas-from-kimmmmmy.sh
# 🌟 #
jq\
-rn '"ggEDC
grrggEDCarraaFEDbrrr
GGFDErrrEEErEEErEGCDE"|gsub
("\\s";"")|explode|.[]as$note
|"rg.a.bC
.D.EF.G.A.BC"|gsub
("\\s";"")|explode|index
View donut.jq.md

donut.c, but it's in jq

Run this in your terminal:

                  jq -nr 'def R(A;B;C
            ):range(A;B;C);def R(A):range
          (A);30as$s|1as$R1|2as$R2|7as$K2|(
        $s*$K2*3/(8*($R1+$R2)))as$K1|def t($A;$B
     ):($A|cos)as$cA|($B|cos)as$cB|($A|sin)as$sA|($B
View kimmys-treasure-hunt-galaxy.p8
pico-8 cartridge // http://www.pico-8.com
version 29
__lua__
-- kimmys-treasure-hunt-galaxy.p8
points={
0xfffe.ddfa,0x0009.d8e2,0xfffe.ace5,0x0001.0000,
0x0006.b93d,0xfffa.ab8c,0xfffa.dcf1,0x0001.0000,
0xfffa.5019,0x0007.7bd4,0x0003.69e8,0x0001.0000,
0xfffa.ca0e,0x0007.1a5f,0xfffb.4463,0x0001.0000,
View Congress.ipynb
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@gcr
gcr / rapid-image-viewer.rkt
Last active May 25, 2017
Tool for showing images
View rapid-image-viewer.rkt
#lang racket
;;
;; Displays a directory full of images in rapid succession.
;; Handy for checking annotations or finding irregularities
;; in large-scale datasets.
;;
;; Usage:
;; - racket rapid-image-viewer.rkt [imglist]
;; Will show all of the '*.jpg' files given as lines in `imglist`,
View OJ.csv
Purchase WeekofPurchase StoreID PriceCH PriceMM DiscCH DiscMM SpecialCH SpecialMM LoyalCH SalePriceMM SalePriceCH PriceDiff Store7 PctDiscMM PctDiscCH ListPriceDiff STORE
1 CH 237 1 1.75 1.99 0 0 0 0 0.5 1.99 1.75 0.24 No 0 0 0.24 1
2 CH 239 1 1.75 1.99 0 0.3 0 1 0.6 1.69 1.75 -0.06 No 0.150754 0 0.24 1
3 CH 245 1 1.86 2.09 0.17 0 0 0 0.68 2.09 1.69 0.4 No 0 0.091398 0.23 1
4 MM 227 1 1.69 1.69 0 0 0 0 0.4 1.69 1.69 0 No 0 0 0 1
5 CH 228 7 1.69 1.69 0 0 0 0 0.956535 1.69 1.69 0 Yes 0 0 0 0
6 CH 230 7 1.69 1.99 0 0 0 1 0.965228 1.99 1.69 0.3 Yes 0 0 0.3 0
7 CH 232 7 1.69 1.99 0 0.4 1 1 0.972182 1.59 1.69 -0.1 Yes 0.201005 0 0.3 0
8 CH 234 7 1.75 1.99 0 0.4 1 0 0.977746 1.59 1.75 -0.16 Yes 0.201005 0 0.24 0
9 CH 235 7 1.75 1.99 0 0.4 0 0 0.982197 1.59 1.75 -0.16 Yes 0.201005 0 0.24 0
View NIPS 2016 ML in the Law Symposium.org

ML in law symposium

Andreas’ idea: Given explainability / the ability to explain decisions, let’s maximize the performance we can get.

My three takeaways

  • Tech folks have a tendency to “fly in and fix everything.” That feels like a dangerous approach here. It’s far better to stand on the shoulders of existing legal precedent, which has studied fairness, discrimination, and bias for decades, even if that slows down progress.
  • Machine learning systems mirror and amplify bias by default. We cannot simply ignore sensitive attributes because the system averages loss over the majority. (Disparate mistreatment). Pithy corollary: this problem will only go away if we devote resources into making it go away.
  • Providing explanations for decisions is the only humane way to build automatic classification systems. Why? If I can’t test a result, I can’t contest it. If the decisions must be testable and explainable, they will be much more reliable as a result.