tl;dr → A paean. “The mobile” is the Bee’s Knees!
and → The signal is given: a new S-Curve is commencing.
and → The unbunding / rebundling / unbundling / rebundling cycle, a metaphor of growth-cum-renewal.
Solon Barocas (Princeton University), Andrew D. Selbst (U.S. Court of Appeals); Big Data’s Disparate Impact; California Law Review, Vol. 104, 2016 (to appear); 62 pages; ssrn:2477899; 2015-08-14; separately filled.
Recitation of embarassments
Flickr → automatically tagging Black (African) men as “animal” or “ape”
Google → search results showing criminality (more frequently) against Black- (African American)-sounding names.
Frank Pasquale (University of Maryland); Digital Star Chamber; In Aeon; 2015-08-15; 3,700 words.
Teaser: Algorithms are producing profiles of you. What do they say? You probably don’t have the right to know
Embarassment over machine-learning categorization failures.
Photo labeling faliures:
African-ancestry person labeled as “apes”
African-ancestry person labeled as “gorilla”
Concentration camp labeled as “jungle gym”
Different ads shown to boys and girls.
The boys where shown “better” ads.
Flickr, Yahoo, 2015-05.
Carnegie Mellon University
Web Transparency and Accountability Project, Princeton University
Solon Barocas (Princeton University), Andrew D. Selbst (U.S. Court of Appeals); Big Data’s Disparate Impact; California Law Review, Vol. 104, 2016 (to appear); 62 pages; SSRN; separately filled.
for color, background & verisimilitude
Vivienne Ming, CTO, Guild Inc.; expert.
Adeyemi Ajao, vice president of technology strategy, Workday.
Andrew Selbst, U.S. Court of Appeals Third Circuit.
Paul Viola, ex-staff, Massachusetts Institute of Technology (MIT).
T.M. Ravi, co-founder and director, Hive (an incubator)
<quote>Xerox Corp., for example, quit looking at job applicants’ commuting time even though software showed that customer-service employees with the shortest commutes were likely to keep their jobs at Xerox longer. Xerox managers ultimately decided that the information could put applicants from minority neighborhoods at a disadvantage in the hiring process.</quote>, uncited.
Digital imaging devices have gained an important role in everyone’s life, due to a continuously decreasing price, and of the growing interest on photo sharing through social networks. As a result of the above facts, everyone continuously leaves visual “traces” of his/her presence and life on the Internet, that can constitute precious data for forensic investigators. Digital Image Forensics is the task of analysing such digital images for collecting evidences. In this ﬁeld, the recent introduction of techniques able to extract a unique ‘ﬁngerprint’ of the source camera of a picture, e.g. based on the Sensor Pattern Noise (SPN), has set the way for a series of useful tools for the forensic investigator. In this paper, we propose a novel usage of SPN, to ﬁnd social network accounts belonging to a certain person of interest, who has shot a given photo. This task, that we name Picture-to-Identity linking, can be useful in a variety of forensic cases, e.g., ﬁnding stolen camera devices, cyber-bullying, or on-line child abuse. We experimentally test a method for Picture-to-Identity linking on a benchmark data set of publicly accessible social network accounts collected from the Internet. We report promising result, which show that such technique has a practical value for forensic practitioners.