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Royal Statistical Scandal
Royal Statistical Scandal False and misleading claims by the Royal Statistical Society Including on human poverty and UN global goals Documentary evidence Matt Berkley Draft 27 June 2019 1 "The Code also requires us to be competent. ... We must also know our limits and not go beyond what we know.... John Pullinger RSS President" https://www.statslife.org.uk/news/3338-rss-publishes-revised-code-of- conduct "If the Royal Statistical Society cannot provide reasonable evidence on inflation faced by poor people, changing needs, assets or debts from 2008 to 2018, I propose that it retract the honour and that the President makes a statement while he holds office." Matt Berkley 27 Dec 2018 2 "a recent World Bank study showed that nearly half of low-and middle- income countries had insufficient data to monitor poverty rates (2002- 2011)." Royal Statistical Society news item 2015 1 "Max Roser from Oxford points out that newspapers could have legitimately run the headline ' Number of people in extreme poverty fell by 137,000 since yesterday' every single day for the past 25 years... Careless statistical reporting could cost lives." President of the Royal Statistical Society Lecture to the Independent Press Standards Organisation April 2018 2 1 https://www.statslife.org.uk/news/2495-global-partnership-for- sustainable-development-data-launches-at-un-summit 2 https://www.statslife.org.uk/features/3790-risk-statistics-and-the-media 3 "Mistaken or malicious misinformation can change your world... When the government is wrong about you it will hurt you too but you may never know how. -
Causal Inference in the Presence of Interference in Sponsored Search Advertising
Causal Inference in the Presence of Interference in Sponsored Search Advertising Razieh Nabi∗ Joel Pfeiffer Murat Ali Bayir Johns Hopkins University Microsoft Bing Ads Microsoft Bing Ads [email protected] [email protected] [email protected] Denis Charles Emre Kıcıman Microsoft Bing Ads Microsoft Research [email protected] [email protected] Abstract In classical causal inference, inferring cause-effect relations from data relies on the assumption that units are independent and identically distributed. This assumption is violated in settings where units are related through a network of dependencies. An example of such a setting is ad placement in sponsored search advertising, where the clickability of a particular ad is potentially influenced by where it is placed and where other ads are placed on the search result page. In such scenarios, confounding arises due to not only the individual ad-level covariates but also the placements and covariates of other ads in the system. In this paper, we leverage the language of causal inference in the presence of interference to model interactions among the ads. Quantification of such interactions allows us to better understand the click behavior of users, which in turn impacts the revenue of the host search engine and enhances user satisfaction. We illustrate the utility of our formalization through experiments carried out on the ad placement system of the Bing search engine. 1 Introduction In recent years, advertisers have increasingly shifted their ad expenditures online. One of the most effective platforms for online advertising is search engine result pages. Given a user query, the search engine allocates a few ad slots (e.g., above or below its organic search results) and runs an auction among advertisers who are bidding and competing for these slots. -
Canadian Privacy Law: the Personal Information Protection and Electronic Documents Act (PIPEDA)
International In-house Counsel Journal Vol. 2, No. 7, Spring 2009, 1135–1146 Canadian Privacy Law: The Personal Information Protection and Electronic Documents Act (PIPEDA) DOMINIC JAAR PATRICK E. ZELLER Legal Counsel Vice President & Deputy General Counsel Ledjit Consulting, Inc. Guidance Software, Inc. Canada United States This white paper provides a general overview of the Personal Information Protection and Electronic Documents Act (“PIPEDA”), and discusses both the privacy requirements imposed by that Act as well as the rules governing the use of electronic documents that it sets out. Overview Introduction to PIPEDA PIPEDA is the federal legislative response to growing concerns over the protection and use of personal information that is accumulated by both public and private organizations in the course of their day-to-day operations.1 The Act sets out rules governing how such information should be handled by the organizations that collect it, and under what circumstances it may be disclosed, either to third parties or to the individual who is the subject of the information. The Act contains two main parts. The first part sets out the rules governing the collection, retention and disclosure of personal information, as well as the remedies available in the event of a suspected breach. In essence, this part of the Act establishes a framework which attempts to balance the privacy rights of individuals with the needs of organizations to collect, use, and disclose personal information in the course of commercial activities. This part of the Act is discussed in sections I and II of this document. The second part of the Act describes the circumstances in which electronic alternatives may be used to fulfill legal obligations, which, under federal laws, require the use of paper documents to record or communicate information or transactions. -
Off the Grid: Pinpointing Location-Based Technologies and the Law
Off the Grid: Pinpointing Location-based Technologies and the Law Written By: Geoffrey White, Barrister & Solicitor External Counsel The Public Interest Advocacy Centre 1204 - ONE Nicholas St. Ottawa, Ontario K1N 7B7 June 2015 Copyright 2015 PIAC Contents may not be commercially reproduced. Any other reproduction with acknowledgment is encouraged. The Public Interest Advocacy Centre (PIAC) Suite 1204 ONE Nicholas Street Ottawa, ON K1N 7B7 Tel: (613) 562-4002 Fax: (613) 562-0007 E-mail: [email protected] Website: www.piac.ca Canadian Cataloguing and Publication Data ISBN 978-1-927707-03-6 Off the Grid? Pinpointing Location-Based Technologies and the Law Off the Grid? Pinpointing Location-Based Technologies and the Law Page 2 of 109 Acknowledgement Financial support from Industry Canada to conduct the research on which this report is based is gratefully acknowledged. The views expressed in this report are not necessarily those of Industry Canada or of the Government of Canada. The author would also like to thank Kent Sebastian, PIAC Student-at-Law 2014-15, Sarah Mavula, PIAC Summer Student 2014, and Jonathan Bishop, PIAC’s Research & Parliamentary Affairs Analyst, for their research and contributions. Any mistakes are solely the author’s. Off the Grid? Pinpointing Location-Based Technologies and the Law Page 3 of 109 Executive Summary Knowledge of the whereabouts of a person, and of a person’s movement patterns, can be very valuable, from a marketing perspective. Indeed, the scale and scope of the business opportunities associated with so-called location-based behavioural marketing and location- based services (collectively, location-based technologies) have been well-documented in the business literature. -
Bibliography
Bibliography [1] M Aamir Ali, B Arief, M Emms, A van Moorsel, “Does the Online Card Payment Landscape Unwittingly Facilitate Fraud?” IEEE Security & Pri- vacy Magazine (2017) [2] M Abadi, RM Needham, “Prudent Engineering Practice for Cryptographic Protocols”, IEEE Transactions on Software Engineering v 22 no 1 (Jan 96) pp 6–15; also as DEC SRC Research Report no 125 (June 1 1994) [3] A Abbasi, HC Chen, “Visualizing Authorship for Identification”, in ISI 2006, LNCS 3975 pp 60–71 [4] H Abelson, RJ Anderson, SM Bellovin, J Benaloh, M Blaze, W Diffie, J Gilmore, PG Neumann, RL Rivest, JI Schiller, B Schneier, “The Risks of Key Recovery, Key Escrow, and Trusted Third-Party Encryption”, in World Wide Web Journal v 2 no 3 (Summer 1997) pp 241–257 [5] H Abelson, RJ Anderson, SM Bellovin, J Benaloh, M Blaze, W Diffie, J Gilmore, M Green, PG Neumann, RL Rivest, JI Schiller, B Schneier, M Specter, D Weizmann, “Keys Under Doormats: Mandating insecurity by requiring government access to all data and communications”, MIT CSAIL Tech Report 2015-026 (July 6, 2015); abridged version in Communications of the ACM v 58 no 10 (Oct 2015) [6] M Abrahms, “What Terrorists Really Want”,International Security v 32 no 4 (2008) pp 78–105 [7] M Abrahms, J Weiss, “Malicious Control System Cyber Security Attack Case Study – Maroochy Water Services, Australia”, ACSAC 2008 [8] A Abulafia, S Brown, S Abramovich-Bar, “A Fraudulent Case Involving Novel Ink Eradication Methods”, in Journal of Forensic Sciences v41(1996) pp 300-302 [9] DG Abraham, GM Dolan, GP Double, JV Stevens, -
Understanding Attention Metrics
Understanding Attention Metrics sponsored by: 01 introduction For years and years we’ve been told that if the click isn’t on its deathbed, it should be shivved to death immediately. While early display ads in the mid-90s could boast CTRs above 40%, the likely number you’ll see attached to a campaign these days is below 0.1%. But for some reason, even in 2015, click-through rate remains as vital to digital ad measurement although its importance is widely ridiculed. Reliance on the click means delivering unfathomable amounts of ad impressions to users, which has hyper-inflated the value of the pageview. This has led to a broken advertising system that rewards quantity over quality. Instead of building audiences, digital publishers are chasing traffic, trying to lure users to sites via “clickbait” headlines where the user is assaulted with an array of intrusive ad units. The end effect is overwhelming, ineffective ads adjacent to increasingly shoddy content. However, the rise of digital video and viewability have injected linear TV measurement capabilities into the digital space. By including time in its calculation, viewability has opened a conversation into the value of user attention. It has introduced metrics with the potential to assist premium publishers in building attractive audiences; better quantify exposure for advertisers; and ultimately financially reward publishers with more engaged audiences. This is a new and fast developing area, but one where a little education can go a long way. In addition to describing current attention metrics, this playbook will dive into what you should look for in an advanced metrics partner and how to use these metrics in selling inventory and optimizing campaign performance. -
Report of Findings: Joint Investigation of Clearview AI, Inc
REPORT OF FINDINGS Joint investigation of Clearview AI, Inc. by the Office of the Privacy Commissioner of Canada, the Commission d’accès à l’information du Québec, the Information and Privacy Commissioner for British Columbia, and the Information Privacy Commissioner of Alberta OPC PIPEDA-039525/CAI QC-1023158/OIPC BC P20- 81997/OIPC AB-015017 Joint Investigation by the Privacy Commissioner of Canada (OPC), the Commission d’accès à l’information du Québec (CAI), the Information and Privacy Commissioner for British Columbia (OIPC BC), and the Information and Privacy Commissioner of Alberta (OIPC AB) into Clearview AI, Inc.’s compliance with the Personal Information Protection and Electronic Documents Act (PIPEDA), the Act Respecting the Protection of Personal Information in the Private Sector, the Act to Establish a Legal Framework for Information Technology (LCCJTI), the Personal Information Protection Act (PIPA BC), and the Personal Information Protection Act (PIPA AB) Page 1 / 29 Contents Overview ..................................................................................................................................................... 3 Background ............................................................................................................................................... 5 Issues .......................................................................................................................................................... 6 Methodology ............................................................................................................................................. -
WTF IS NATIVE ADVERTISING? Introduction
SPONSORED BY IS NATIVE ADVERTISING? Table of Contents Introduction WTF is Native 3 14 Programmatic? Nomenclature The Native Ad 4 16 Triumvirate 5 Decision tree 17 The Ad Man 18 The Publisher Issues Still Plaguing 19 The Platform 6 Native Advertising Glossary 7 Scale 20 8 Metrics 10 Labelling 11 The Church/State Divide 12 Credibility 3 / WTF IS NATIVE ADVERTISING? Introduction In 2013, native advertising galloped onto the scene like a masked hero, poised to hoist publishers atop a white horse, rescuing them from the twin menaces of programmatic advertising and sagging CPMs. But who’s really there when you peel back the mask? Native advertising is a murky business. Ad executives may not consider it advertising. Editorial departments certainly don’t consider it editorial. Even among its practitioners there is debate — is it a format or is it a function? Publishers who have invested in the studio model position native advertising as the perfect storm of context, creative capital and digital strategy. For platforms, it may be the same old banner advertising refitted for the social stream. Digiday created the WTF series to parse murky digital marketing concepts just like these. WTF is Native Advertising? Keep reading to find out..... DIGIDAY 4 / WTF IS NATIVE ADVERTISING? Nomenclature Native advertising An advertising message designed Branded content Content created to promote a Content-recommendation widgets Another form to mimic the form and function of its environment brand’s products or values. Branded content can take of native advertising often used by publishers, these a variety of formats, not all of them technically “native.” appear to consumers most often at the bottom of a web Content marketing Any marketing messages that do Branded content placed on third-party publishing page with lines like “From around the web,” or “You not fit within traditional formats like TV and radio spots, sites or platforms can be considered native advertising, may also like.” print ads or banner messaging. -
Detailed Course Outline
Ninth International Conference in Computer Vision ( ICCV ), Nice 2003. Short Course : Learning and Inference in Vision: Generative Methods (3½ hours). Presenters: Bill Freeman (MIT AI Laboratory) and Andrew Blake (Microsoft Research Cambridge) (0) Intro – roadmap for learning and inference in vision (1) Bayesian inference introduction; integration of sensory data applications: color constancy, Bayes Matte (2) Learning and inference in temporal and spatial Markov processes Techniques: 2.1 PCA, FA, TCA : inference – linear (Wiener) filter learning: by Expectation Maximization (EM); (tutorial: EM for 2-line fitting) applications: face simulation, denoising, Weiss’s intrinsic images and furthermore: Active Appearance Models, Simoncelli, ICA & non-Gaussianity, filter banks 2.2 Markov chain & HMM : inference: - MAP by Dynamic Programming, Forward and Forward-Backward (FB) algorithms; learning: by EM – Baum-Welch; representations: pixels, patches applications: stereo vision and furthermore: gesture models (Bobick-Wilson) 2.3 AR models : Inference: Kalman-Filter, Kalman Smoother, Particle Filter; learning: by EM-FB; representations: patches, curves, chamfer maps, filter banks applications: tracking (Isard-Blake, Black-Sidenbladh, El Maraghi-Jepson-Fleet); Fitzgibbon-Soatto textures and furthermore: EP 2.4 MRFs: Inference: ICM, Loopy Belief Propagation (BP), Generalised BP, Graph Cuts; Parameter learning : Pseudolikelihood maximisation; representations: color pixels, patches applications: Texture segmentation, super resolution (Freeman-Pasztor), distinguishing shading from paint and furthermore: Gibbs sampling, Discriminative Random Field (DRF) 2.5 Bayes network: Inference : Belief Propagation (BP) Parameter learning : Pseudolikelihood maximisation; applications: scene context analysis: combine top down with bottom up (Murphy et al) 2.6 Markov network: Inference : MCMC applications: low level segmentation (Zhu et al.) (3) Summary and finish Biographies of Presenters. -
Google Analytics Education and Skills for the Professional Advertiser
AN INDUSTRY GUIDE TO Google Analytics Education and Skills For The Professional Advertiser MODULE 5 American Advertising Federation The Unifying Voice of Advertising Google Analytics is a freemium service that records what is happening at a website. CONTENTS Tracking the actions of a website is the essence of Google MODULE 5 Google Analytics Analytics. Google Analytics delivers information on who is visiting a Learning Objectives .....................................................................................1 website, what visitors are doing on that website, and what Key Definitions .......................................................................................... 2-3 actions are carried out at the specific URL. Audience Tracking ................................................................................... 4-5 Information is the essence of informed decision-making. The The A,B,C Cycle .......................................................................................6-16 website traffic activity data of Google Analytics delivers a full Acquisition understanding of customer/visitor journeys. Behavior Focus Google Analytics instruction on four(4) Conversions basic tenets: Strategy: Attribution Model ............................................................17-18 Audience- The people who visit a website Case History ........................................................................................... 19-20 Review .............................................................................................................. -
87Th Josiah Willard Gibbs Lecture Wednesday, January 15, 2014 8:30–9:30 PM
87th Josiah Willard Gibbs Lecture Wednesday, January 15, 2014 8:30–9:30 PM JOSIAH WILLARD GIBBS 1839–1903 Photograph by Pictorial Mathematics Scripta Mathematica Yeshiva College, New York, 1942 American Mathematical Society Josiah Willard Gibbs Lecture Wednesday, January 15, 2014 8:30–9:30 PM Ballrooms 1 & 2 4th Floor Baltimore Convention Center Baltimore, MD Machines that See, Powered by Probability Andrew Blake Microsoft Research Cambridge To commemorate the name of Professor Gibbs, the American Mathematical Society established an honorary lectureship in 1923 to be known as the Josiah Willard Gibbs Lectureship. The lectures are of a semipopular nature and are given by invitation. They are usually devoted to mathematics or its applications. It is hoped that these lectures will enable the public and the academic community to become aware of the contribu- tion that mathematics is making to present-day thinking and to modern civilization. Abstract Machines with some kind of ability to see have become a reality in the last decade, and we see vision capabilities in cameras and photography, cars, graphics software, and in the user interfaces to appliances. Such machines bring benefits to safety, consumer experiences, and healthcare, and their operation is based on mathematical ideas. The visible world is inherently ambiguous and uncertain so estimation of physical properties by machine vision often relies on probabilistic meth- ods. Prior distributions over shape can help significantly to make estima- tors for finding and tracking objects more robust. Learned distributions for colour and texture are used to make the estimators more discriminative. These ideas fit into a philosophy of vision as inference: exploring hypothe- ses for the contents of a scene that explain an image as fully as possible. -
Auditing Wikipedia's Hyperlinks Network on Polarizing Topics
Auditing Wikipedia’s Hyperlinks Network on Polarizing Topics Cristina Menghini Aris Anagnostopoulos Eli Upfal DIAG DIAG Dept. of Computer Science Sapienza University Sapienza University Brown University Rome, Italy Rome, Italy Providence, RI, USA [email protected] [email protected] [email protected] ABSTRACT readers to deepen their understanding of a topic by conveniently ac- People eager to learn about a topic can access Wikipedia to form cessing other articles." Consequently, while reading an article, users a preliminary opinion. Despite the solid revision process behind are directly exposed to its content and indirectly exposed to the the encyclopedia’s articles, the users’ exploration process is still content of the pages it points to. influenced by the hyperlinks’ network. In this paper, we shed light Wikipedia’s pages are the result of collaborative efforts of a com- on this overlooked phenomenon by investigating how articles de- mitted community that, following policies and guidelines [4, 20], scribing complementary subjects of a topic interconnect, and thus generates and maintains up-to-date and high-quality content [28, may shape readers’ exposure to diverging content. To quantify 40]. Even though tools support the community for curating pages this, we introduce the exposure to diverse information, a metric that and adding links, it lacks a systematic way to contextualize the captures how users’ exposure to multiple subjects of a topic varies pages within the more general articles’ network. Indeed, it is im- click-after-click by leveraging navigation models. portant to stress that having access to high-quality pages does not For the experiments, we collected six topic-induced networks imply a comprehensive exposure to an argument, especially for a about polarizing topics and analyzed the extent to which their broader or polarizing topic.