The New Atlantis - Winter 2018 (Issue 54) uncompressed with cover

Recommendations

Info

Algorithmic Injustice has a tendency — despite its questionnaire ostensibly excluding race from consideration — to systematically give more unfavorable judgments to black defendants. For added effect, the ProPublica authors’ statistical analyses were interwoven with a number of stories contrasting two people, always one black and one white, who had been convicted of similar crimes but were assigned very different COMPAS scores. Each time, the black defendant was deemed “high-risk” but did not re-offend, while the white defendant was deemed “low-risk” but did re-offend. There is not a lot of room to draw any conclusion besides the COMPAS algorithm being racially biased. It’s a compelling piece of journalism. But, seen another way, the ProPublica piece can be read not as an indictment of COMPAS and other such tools as racist, but instead as an illustration of the daunting complexities inherent in applying our intuitive sense of justice to making fair statistical predictions. As we will see, it may even be impossible to fully satisfy our sense of justice when race enters the equation — at least in a society like ours, with a long history of racial discrimination. Confusing Standards of Reliability Northpointe, as you might expect, offered a vehement rejoinder to ProPublica’s analysis, which ProPublica in turn rebutted point by point. Numerous academic papers have also since been published on the subject. On close scrutiny, both Northpointe’s defense of its own system and a good deal of ProPublica’s criticism turn out to be questionable. The devil is in the details. As we will repeatedly encounter, much of the dispute arises from the fact that there are many different ways to measure predictive reliability and error, several of which can easily be confused with one another. For example, suppose we want to know how often COMPAS incorrectly judges defendants too harshly. One way we might measure mistaken harshness is in terms of how frequently people who are labeled “highrisk” did not go on to re-offend. This is known as the false discovery rate. Alternatively, we can measure mistaken harshness in terms of how often people who didn’t re-offend had been labeled “high-risk.” This is the false positive rate. These are similar-sounding but different concepts, and even if one rate is the same for white and black defendants, the other might be quite different. In their exchanges, Northpointe and ProPublica sometimes fail Winter 2018 ~ 9 Copyright 2018. All rights reserved. Print copies available at TheNewAtlantis.com/BackIssues.
Tafari Mbadiwe to specify which measurement of reliability they’re referring to, or use the wrong term or an unclear description. To help avoid these kinds of confusion, I’ve included two tables with an overview of different measurements of predictive reliability and error. A related problem is that the standards for what counts as good reliability differ from one measurement to the next. The standard of accuracy mentioned before refers to the algorithm’s overall correctness, and so incorporates both the high-risk and low-risk predictions. In this case, ProPublica’s comparison to the accuracy of a coin flip is appropriate. (ProPublica’s own estimate pins COMPAS’s overall accuracy at 66 percent — not too far off from Northpointe’s claim of 68 percent.) But there are many ways to judge the reliability of a predictive system, each of which tells a different, and often conflicting, story. If we look instead just at the people who were predicted to re-offend, the standard to beat shouldn’t be a coin flip, but instead however often people actually do re-offend. If, say, 60 percent of the overall population went on to re-offend, the “high-risk” population would need to re-offend significantly more than 60 percent of the time to consider the algorithm better than a Measurements of Predictive Reliability Applies to risk-rating type Type of reliability Measures the likelihood that... → Betterthanrandom guess must be higher than Accuracy “high-risk” vs. “low-risk” Correct predictions overall Any defendant Is predicted correctly 50% Concordance index 1-to-10 risk scores Relative correctness of risk ratings Any reoffender’s risk score Is higher than any nonreoffender’s risk score 50% Precision “high-risk” vs. “low-risk” Correct predictions of re- offense A defendant labeled “high-risk” Re-offends The overall re-offense rate 10 ~ The New Atlantis Copyright 2018. All rights reserved. Print copies available at TheNewAtlantis.com/BackIssues.
Page 1 and 2: Algorithmic Injustice Tafari Mbadiw
Page 3 and 4: The New Atlantis (1627) was the tit
Page 5 and 6: Tafari Mbadiwe economic growth. The
Page 7 and 8: Tafari Mbadiwe admitted to reporter
Page 9: Tafari Mbadiwe analysis. Through an
Page 13 and 14: Tafari Mbadiwe The Fairness Paradox
Page 15 and 16: Tafari Mbadiwe The trouble is that
Page 17 and 18: Tafari Mbadiwe But since the algori
Page 19 and 20: Tafari Mbadiwe Before the adjustmen
Page 21 and 22: Tafari Mbadiwe Following these argu
Page 23 and 24: Tafari Mbadiwe Malcolm Gladwell spe
Page 25 and 26: Tafari Mbadiwe laws in order to mak
Page 27 and 28: Tafari Mbadiwe or destination — u
Page 29 and 30: Tafari Mbadiwe nothing else, we’v
Page 31 and 32: Natalie Elliot Martin Hargreaves (i
Page 33 and 34: Natalie Elliot play — that man is
Page 35 and 36: Natalie Elliot Castle, called Elsin
Page 37 and 38: Natalie Elliot recognized until we
Page 39 and 40: Natalie Elliot try to answer questi
Page 41 and 42: Natalie Elliot probably arose not o
Page 43 and 44: Natalie Elliot What is particularly
Page 45 and 46: Natalie Elliot that makes it possib
Page 47 and 48: Natalie Elliot infects ladies’ li
Page 49 and 50: Natalie Elliot Against the backdrop
Page 51 and 52: Natalie Elliot to create the langua
Page 53 and 54: Waleed Al-Shobakky KAUST (pronounce
Page 55 and 56: Waleed Al-Shobakky oil minister Ahm
Page 57 and 58: Waleed Al-Shobakky Arabia to a know
Page 59 and 60: Waleed Al-Shobakky lab, speeding up
Page 61 and 62:
Waleed Al-Shobakky What explains th
Page 63 and 64:
Waleed Al-Shobakky Although the ins
Page 65 and 66:
Waleed Al-Shobakky an explicit risk
Page 67 and 68:
Kirsten A. Hall David M. Buisán (J
Page 69 and 70:
Kirsten A. Hall The Great Reversal
Page 71 and 72:
Kirsten A. Hall conjectures, all bo
Page 73 and 74:
Kirsten A. Hall This omen, as we co
Page 75 and 76:
Kirsten A. Hall child, Godwin wrote
Page 77 and 78:
Kirsten A. Hall The trumpet of a pr
Page 79 and 80:
Kirsten A. Hall idea, so very diffe
Page 81 and 82:
Kirsten A. Hall On the other hand,
Page 83 and 84:
Kirsten A. Hall The novel challenge
Page 85 and 86:
Brendan P. Foht for supremely frigh
Page 87 and 88:
Brendan P. Foht Like playing God, r
Page 89 and 90:
Brendan P. Foht an apprentice picki
Page 91 and 92:
Brendan P. Foht “one of the first
Page 93 and 94:
Brendan P. Foht is rooted in a sens
Page 95 and 96:
Brendan P. Foht making three-parent
Page 97 and 98:
Disenchantment, Actually Doug Sikke
Page 99 and 100:
Doug Sikkema when man felt himself
Page 101 and 102:
Doug Sikkema they were working to e
Page 103 and 104:
Doug Sikkema preclude the existence
Page 105 and 106:
R & R The Political Path to GPS Ant
Page 107 and 108:
Anthony Paletta political mapmaking
Page 109 and 110:
Anthony Paletta effort to span prov
Page 111 and 112:
Anthony Paletta The radio beams wou
Page 113 and 114:
Anthony Paletta missile at it, the
Page 115 and 116:
Olga Rachello mortality itself. Fro
Page 117 and 118:
Olga Rachello is room for a broader
Page 119 and 120:
Olga Rachello is supposed to be. Ca
Page 121:
DO YOU HAVE ISSUES? WE’VE GOT MOR
show all

The New Atlantis - Winter 2018 (Issue 54) uncompressed with cover

Create successful ePaper yourself

Delete template?

Save as template?