DOCSLIB.ORG

Project Periodic Report: Final

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Project Periodic Report: Final VALCRI FP7-IP-608142 PROJECT PERIODIC REPORT: FINAL COVERING PERIODS 1-3, MAY 2014 – JUNE 2018 (M01-M50) VERSION 1.0 Date submitted: 17 May 2018 Dissemination Level: PU / PP / RE / CO WORK PACKAGE: WP1 MANAGEMENT WORK PACKAGE LEADER: B.L. WILLIAM WONG © 2018 VALCRI All rights reserved The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no FP7-IP-608142. 1 VALCRI FP7-IP-608142 2 VALCRI FP7-IP-608142 Table of Contents SECTION 1 PUBLISHABLE SUMMARY: 5 A. SUMMARY DESCRIPTION OF PROJECT CONTEXT AND OBJECTIVES 5 B. DESCRIPTION OF WORK PERFORMED AND MAIN RESULTS 6 C. FINAL RESULTS AND IMPACTS 8 D. PROJECT PUBLIC WEBSITE ADDRESS: 9 SECTION 2 CORE OF THE REPORT: 10 2.1 INTRODUCTION 10 2.1.1 Project Objectives 10 2.1.2 Problems faced by criminal intelligence analysts 10 2.1.3 The VALCRI Concept 12 2.1.4 Summary of Achievements 18 2.2 WORK PROGRESS AND ACHIEVEMENTS DURING THE PERIOD M17 – M32 21 2.2.1 Work Package 1 - MANAGEMENT 21 2.2.2 Work Package 2 - REQUIREMENTS AND DESIGN 23 2.2.3 Work Package 3 - HUMAN ISSUES 27 2.2.4 Work Package 4 - ANALYST USER INTERFACE 40 2.2.5 Work Package 5 - DATA SPACE 44 2.2.6 Work Package 6 - ANALYSIS SPACE 51 2.2.7 Work Package 7 - HYPOTHESIS SPACE – USER INTERFACE 55 2.2.8 Work Package 8 - DATA EXTRACTION 58 2.2.9 Work Package 9 - EVENT DETECTION 63 2.2.10 Work Package 10 - ONTOLOGY LIBRARY 66 2.2.11 Work Package 11 - RESEARCH DATA 73 2.2.12 Work Package 12 - SCALABLE & SECURE DISTRIBUTED PROCESSING & INTEGRATION 78 2.2.13 Work Package 13 - TRAINING AND EVALUATION 83 2.2.14 Work Package 14 - DISSEMINATION AND EXPLOITATION 91 2.3. ALIGNMENT VIA SUBGROUPS (SG), ACTIVITIES IN THE REPORTING PERIOD 98 2.3.1 SG1: DEA (data extraction and analysis) 98 2.3.2 SG2: DMO (data management and ontologies) 98 2.3.3 SG3: DAR (design and architecture) 99 3.3.4 SG4: RCC (requirements consolidation and concretization) 99 2.3.5 SG5: SEPL (security, ethics, privacy, legal) 99 2.3.6 SG6: UUC (UX/UI and cognitive aspects) 100 2.3.7 SG7: Training and Evaluation 101 2.4 PROJECT MANAGEMENT 103 2.4.1 Scientific Coordination 103 2.4.2 Administrative and financial stability 103 2.4.3 Exploitation: Phase 4 Plans and Project Extension 104 3 VALCRI FP7-IP-608142 ANNEX A ADVANCES AND GROUND BREAKERS 1 ADVANCES AND GROUNDBREAKING SCIENCE AND TECHNOLOGIES 2 AGB1. The Analyst’s User Interface: How Analysts Think 3 AGB2. The Analyst’s User Interface: The Reasoning Workspace and Thinking Landscape 6 AGB3. Insight into challenges of using sophisticated software in criminal investigations and meeting disclosure obligations with regard to the production of “relevant material” under CPIA 1996 8 AGB4. Shewmaps - Gridded Geographical Summaries Of Multiple Spc Charts 10 AGB5. A Descriptive, Practical, Hybrid Argumentation Model to Assist With the Formulation of Defensible Assessments in Uncertain Sense-Making Environments 11 AGB6. Interactive human-centered extraction of temporal-spatial and behavioral associations for crime analysis 13 AGB7. Aggregated Visualization of Elements outside of a Visualization Viewport (Off-Screen) 15 AGB8. Interactive Machine Learning for Crime Data Analysis 16 AGB9. Interactive Dimensionality-Reduction to foster Data Exploration and Sense Making 17 AGB10. Modelling the visual analytic process 18 AGB11. Provenance models, methods and system architecture to support legal, ethical, and privacy requirements when dealing with police data 20 AGB12. Alignment Cubes – a tool for comparing and evaluating ontology alignments 22 AGB13. Requirements for user involvement for ontology alignment systems 23 AGB14. RepOSE plugin for Protégé – a tool for completing ontologies 25 AGB15. Advances in Ontology Design Pattern (ODP) usage methods (the eXtreme Design methodology) and support tooling. 26 AGB16. RSP-SPIN Service and RDF Stream Processing architecture 28 AGB17. REST-SPIN and Query Template Library 30 AGB18. Advances in the understanding of design and interpretation of data dense interactive graphics in crime analysis. 32 AGB19. Furthering understanding and implementation of legal developments in the field of privacy and data protection and assisting in development of innovative legally compliant police technologies. 34 AGB20. Methods for Discovering and Mitigating Cognitive Biases in a Visual Analytics Environment 38 AGB21. Sense-making in intelligence analysis and development of a framework of recommendations for the design of such systems 41 AGB22. Analysis of the new data protection framework for the Law Enforcement Agencies (LEA) sector in Europe to determine technical and organisational solution approaches for achieving legal compliance. 43 AGB23. The Master Analyst: A Reference Curriculum for Law Enforcement Professionals 46 AGB24. Visual Analytics for Federal Police of Antwerp 48 AGB25. Fuzzy integration tests over changing data 51 AGB26. Generators of faked yet realistic data. 53 AGB27. Enhancing VALCRI's operational efficacy by evaluating a dominant LPA data source - ISLP - for integration.56 AGB28. Development and testing environment 58 ANNEX B PUBLISHED PAPERS 1 ANNEX C VIDEOS 1 ANNEX D IEB Report and IEB Risk Assessment 3 Final Report of the Independent Ethics Board on the VALCRI Project 4 VALCRI Risk Assessment for IEB-related issues 20 ANNEX E EXPLOITATION PRINCIPLES AND COMMERCIAL IP LIST 1 Annex E, Appendix 1 - VALCRI – Commercial IP Exploitation Principles 1 Annex E, Appendix 2 - Commercial IP origination and individual or joint ownerships 1 4 VALCRI FP7-IP-608142 SECTION 1 PUBLISHABLE SUMMARY: Project Objectives, Work Progress, Achievements, and Project Management Period 3, M17 – M50, Sep 2014 – Jun 2018 A. SUMMARY DESCRIPTION OF PROJECT CONTEXT AND OBJECTIVES (4000 CHAR // 3998 CHARS) INTRODUCTION The purpose of VALCRI was to develop the next generation criminal intelligence analysis system for European LEAs. Working closely with three European police forces, the project researched and developed at TRL-5, an integrated system of over 75 software components of advanced data processing, analytic and sense-making tools. It includes multiple applications spanning strategic intelligence analysis to tactical intelligence and individual case management. The VALCRI system was routinely evaluated with project end-users. In the final nine months, it has been evaluated with 214 LEA officers in 50 agencies in 16 countries and 2 international LEAs (Europol and NATO Intelligence Fusion Centre). It is undergoing trials with actual data at the London Metropolitan Police, and the Pasco County Sheriff’s Department in Florida. Negotiations are also underway to purchase or licence various VALCRI technologies and non-software outcomes. VALCRI used a cognitive engineering approach to create a human-technology team that combined advanced concepts of human reasoning and analytic discourse with machine learning and database technologies. The result has been a semi-automated human-mediated semantic knowledge extraction capability that can facilitate and improve investigative sense making and problem solving in crime analysis and criminal investigation in a high ambiguity and constantly evolving environment. KEY DISTINGUISHING FEATURES 1. SUPPORT HOW ANALYSTS THINK, RATHER THAN WHAT ANALYSTS DO If VALCRI were designed to mainly support what analysts do, then the system would primarily automate current tasks and workflows. Instead, by designing for how analysts think, the VALCRI system is better able to respond to the variety of sense making, reasoning and inference making and problem solving strategies presented by human analysts. 2. FACILITATE EXPERT INTUITION TO SCIENTIFIC METHOD In many investigations, analysts are often only presented with fragments of data from which to create an understanding of the situation and to anticipate what might happen. Expert intuition is very useful in generating “hunches”, or early, plausible and tentative hypotheses. However, hunches can be error prone and subject to cognitive biases. VALCRI has designed quick ways for analysts to use the scientific method to test their hunches so that they may easily discard it if proven wrong. 5 VALCRI FP7-IP-608142 3. HUMANS DECIDE, MACHINES DO THE HEAVY LIFTING VALCRI has been designed so that humans and machines do what each is good at: Humans make decisions under ambiguity; machines are fast at tedious and repetitive task. So, when an analyst instructs VALCRI to “find me more reports like this …”, the machine learning-based automation will trawl through large volumes of structured and un-structured data (e.g. free text) to retrieve, triage, collate, thematically analyse the data, and then combines and presents the reports in context of the crime problem being investigated, e.g. Comparative Case Analysis. 4. ETHICS, LEGAL AND PRIVACY BY DESIGN In many LEA data analytics systems, once a person’s data is enmeshed in the system-data networks, that person will continue to be linked to those criminal profiles. Such profiles will be used by the system to predict membership characteristics and to set up alerts for “persons of interest”. This can lead to further stops and searches of the person, even though he may be innocent. This interferes with his private life. VALCRI advocates the need for ‘computational transparency’ as a mitigating approach: make visible the inner workings of ‘black box’ automated algorithms. A lower TRL prototype has been implemented in VALCRI to investigate how fine grain data access controls may be combined with computational transparency so that analysts and investigators are aware of the provenance of algorithm’s computed results and protect the rights of individuals. 5. UP-SKILLING OF ANALYTIC ABILITIES VALCRI has also identified and addressed varying deficiencies in the abilities of the intelligence analysis community. Some of this have been formalised in a new Master degree level analytics training course at Aston University in Birmingham, in partnership with the West Midlands Police; and some have been formalised into commercial intelligence analysis training packages focusing on analytic reasoning.
Load more

Recommended publications
  • A Task-Based Taxonomy of Cognitive Biases for Information Visualization
    A Task-based Taxonomy of Cognitive Biases for Information Visualization Evanthia Dimara, Steven Franconeri, Catherine Plaisant, Anastasia Bezerianos, and Pierre Dragicevic Three kinds of limitations The Computer The Display 2 Three kinds of limitations The Computer The Display The Human 3 Three kinds of limitations: humans • Human vision ️ has limitations • Human reasoning 易 has limitations The Human 4 ️Perceptual bias Magnitude estimation 5 ️Perceptual bias Magnitude estimation Color perception 6 易 Cognitive bias Behaviors when humans consistently behave irrationally Pohl’s criteria distilled: • Are predictable and consistent • People are unaware they’re doing them • Are not misunderstandings 7 Ambiguity effect, Anchoring or focalism, Anthropocentric thinking, Anthropomorphism or personification, Attentional bias, Attribute substitution, Automation bias, Availability heuristic, Availability cascade, Backfire effect, Bandwagon effect, Base rate fallacy or Base rate neglect, Belief bias, Ben Franklin effect, Berkson's paradox, Bias blind spot, Choice-supportive bias, Clustering illusion, Compassion fade, Confirmation bias, Congruence bias, Conjunction fallacy, Conservatism (belief revision), Continued influence effect, Contrast effect, Courtesy bias, Curse of knowledge, Declinism, Decoy effect, Default effect, Denomination effect, Disposition effect, Distinction bias, Dread aversion, Dunning–Kruger effect, Duration neglect, Empathy gap, End-of-history illusion, Endowment effect, Exaggerated expectation, Experimenter's or expectation bias,
    [Show full text]
  • The Art of Thinking Clearly
    For Sabine The Art of Thinking Clearly Rolf Dobelli www.sceptrebooks.co.uk First published in Great Britain in 2013 by Sceptre An imprint of Hodder & Stoughton An Hachette UK company 1 Copyright © Rolf Dobelli 2013 The right of Rolf Dobelli to be identified as the Author of the Work has been asserted by him in accordance with the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means without the prior written permission of the publisher, nor be otherwise circulated in any form of binding or cover other than that in which it is published and without a similar condition being imposed on the subsequent purchaser. A CIP catalogue record for this title is available from the British Library. eBook ISBN 978 1 444 75955 6 Hardback ISBN 978 1 444 75954 9 Hodder & Stoughton Ltd 338 Euston Road London NW1 3BH www.sceptrebooks.co.uk CONTENTS Introduction 1 WHY YOU SHOULD VISIT CEMETERIES: Survivorship Bias 2 DOES HARVARD MAKE YOU SMARTER?: Swimmer’s Body Illusion 3 WHY YOU SEE SHAPES IN THE CLOUDS: Clustering Illusion 4 IF 50 MILLION PEOPLE SAY SOMETHING FOOLISH, IT IS STILL FOOLISH: Social Proof 5 WHY YOU SHOULD FORGET THE PAST: Sunk Cost Fallacy 6 DON’T ACCEPT FREE DRINKS: Reciprocity 7 BEWARE THE ‘SPECIAL CASE’: Confirmation Bias (Part 1) 8 MURDER YOUR DARLINGS: Confirmation Bias (Part 2) 9 DON’T BOW TO AUTHORITY: Authority Bias 10 LEAVE YOUR SUPERMODEL FRIENDS AT HOME: Contrast Effect 11 WHY WE PREFER A WRONG MAP TO NO
    [Show full text]
  • Communication Science to the Public
    David M. Berube North Carolina State University ▪ HOW WE COMMUNICATE. In The Age of American Unreason, Jacoby posited that it trickled down from the top, fueled by faux-populist politicians striving to make themselves sound approachable rather than smart. (Jacoby, 2008). EX: The average length of a sound bite by a presidential candidate in 1968 was 42.3 seconds. Two decades later, it was 9.8 seconds. Today, it’s just a touch over seven seconds and well on its way to being supplanted by 140/280- character Twitter bursts. ▪ DATA FRAMING. ▪ When asked if they truly believe what scientists tell them, NEW ANTI- only 36 percent of respondents said yes. Just 12 percent expressed strong confidence in the press to accurately INTELLECTUALISM: report scientific findings. ▪ ROLE OF THE PUBLIC. A study by two Princeton University researchers, Martin TRENDS Gilens and Benjamin Page, released Fall 2014, tracked 1,800 U.S. policy changes between 1981 and 2002, and compared the outcome with the expressed preferences of median- income Americans, the affluent, business interests and powerful lobbies. They concluded that average citizens “have little or no independent influence” on policy in the U.S., while the rich and their hired mouthpieces routinely get their way. “The majority does not rule,” they wrote. ▪ Anti-intellectualism and suspicion (trends). ▪ Trump world – outsiders/insiders. ▪ Erasing/re-writing history – damnatio memoriae. ▪ False news. ▪ Infoxication (CC) and infobesity. ▪ Aggregators and managed reality. ▪ Affirmation and confirmation bias. ▪ Negotiating reality. ▪ New tribalism is mostly ideational not political. ▪ Unspoken – guns, birth control, sexual harassment, race… “The amount of technical information is doubling every two years.
    [Show full text]
  • Infographic I.10
    The Digital Health Revolution: Leaving No One Behind The global AI in healthcare market is growing fast, with an expected increase from $4.9 billion in 2020 to $45.2 billion by 2026. There are new solutions introduced every day that address all areas: from clinical care and diagnosis, to remote patient monitoring to EHR support, and beyond. But, AI is still relatively new to the industry, and it can be difficult to determine which solutions can actually make a difference in care delivery and business operations. 59 Jan 2021 % of Americans believe returning Jan-June 2019 to pre-coronavirus life poses a risk to health and well being. 11 41 % % ...expect it will take at least 6 The pandemic has greatly increased the 65 months before things get number of US adults reporting depression % back to normal (updated April and/or anxiety.5 2021).4 Up to of consumers now interested in telehealth going forward. $250B 76 57% of providers view telehealth more of current US healthcare spend % favorably than they did before COVID-19.7 could potentially be virtualized.6 The dramatic increase in of Medicare primary care visits the conducted through 90% $3.5T telehealth has shown longevity, with rates in annual U.S. health expenditures are for people with chronic and mental health conditions. since April 2020 0.1 43.5 leveling off % % Most of these can be prevented by simple around 30%.8 lifestyle changes and regular health screenings9 Feb. 2020 Apr. 2020 OCCAM’S RAZOR • CONJUNCTION FALLACY • DELMORE EFFECT • LAW OF TRIVIALITY • COGNITIVE FLUENCY • BELIEF BIAS • INFORMATION BIAS Digital health ecosystems are transforming• AMBIGUITY BIAS • STATUS medicineQUO BIAS • SOCIAL COMPARISONfrom BIASa rea• DECOYctive EFFECT • REACTANCEdiscipline, • REVERSE PSYCHOLOGY • SYSTEM JUSTIFICATION • BACKFIRE EFFECT • ENDOWMENT EFFECT • PROCESSING DIFFICULTY EFFECT • PSEUDOCERTAINTY EFFECT • DISPOSITION becoming precise, preventive,EFFECT • ZERO-RISK personalized, BIAS • UNIT BIAS • IKEA EFFECT and • LOSS AVERSION participatory.
    [Show full text]
  • John Collins, President, Forensic Foundations Group
    On Bias in Forensic Science National Commission on Forensic Science – May 12, 2014 56-year-old Vatsala Thakkar was a doctor in India but took a job as a convenience store cashier to help pay family expenses. She was stabbed to death outside her store trying to thwart a theft in November 2008. Bloody Footwear Impression Bloody Tire Impression What was the threat? 1. We failed to ask ourselves if this was a footwear impression. 2. The appearance of the impression combined with the investigator’s interpretation created prejudice. The accuracy of our analysis became threatened by our prejudice. Types of Cognitive Bias Available at: http://en.wikipedia.org/wiki/List_of_cognitive_biases | Accessed on April 14, 2014 Anchoring or focalism Hindsight bias Pseudocertainty effect Illusory superiority Levels-of-processing effect Attentional bias Hostile media effect Reactance Ingroup bias List-length effect Availability heuristic Hot-hand fallacy Reactive devaluation Just-world phenomenon Misinformation effect Availability cascade Hyperbolic discounting Recency illusion Moral luck Modality effect Backfire effect Identifiable victim effect Restraint bias Naive cynicism Mood-congruent memory bias Bandwagon effect Illusion of control Rhyme as reason effect Naïve realism Next-in-line effect Base rate fallacy or base rate neglect Illusion of validity Risk compensation / Peltzman effect Outgroup homogeneity bias Part-list cueing effect Belief bias Illusory correlation Selective perception Projection bias Peak-end rule Bias blind spot Impact bias Semmelweis
    [Show full text]
  • Common Cognitive Biases
    Common Cognitive Biases Anchoring The first number or piece of information given acts as an anchor for all subsequent numbers or information. Once an anchor is set, we quickly make other judgments by adjusting away from that anchor. For example, we tend to pay more for something if the first number we see is much higher. We compare other prices to the initial figure, which makes the price we are prepared to pay look more reasonable. Anticipation of An a priori anticipation of possible regret can Regret influence decision making. When we are facing a decision, we may anticipate the regret that may arise from a negative outcome. This bias is linked to Risk Aversion. Authority Bias We are more easily influenced or convinced by figures of authority figures. A good example of this is when toothpaste is always presented by a dentist or expert wearing a lab coat. We are influenced both by real experts and also by those just appearing to be in authority. ãChequered Leopard Ltd 1 Choice Overload When we are given too many choices, we either prefer the default option or we avoid making any decision at all. As the number of options increases, our level of certainty about our choice decreases. Additionally, the anticipation that we will regret our choice increases.” When we are tired, we tend to conserve our energy by making choices based on a single factor like price, for example, rather than considering all the other determinants that go into making the best decision. Clustering Illusion We are naturally pattern-seeking because it helps speed up the process recognising people and things.
    [Show full text]
  • 1 Embrace Your Cognitive Bias
    1 Embrace Your Cognitive Bias http://blog.beaufortes.com/2007/06/embrace-your-co.html Cognitive Biases are distortions in the way humans see things in comparison to the purely logical way that mathematics, economics, and yes even project management would have us look at things. The problem is not that we have them… most of them are wired deep into our brains following millions of years of evolution. The problem is that we don’t know about them, and consequently don’t take them into account when we have to make important decisions. (This area is so important that Daniel Kahneman won a Nobel Prize in 2002 for work tying non-rational decision making, and cognitive bias, to mainstream economics) People don’t behave rationally, they have emotions, they can be inspired, they have cognitive bias! Tying that into how we run projects (project leadership as a compliment to project management) can produce results you wouldn’t believe. You have to know about them to guard against them, or use them (but that’s another article)... So let’s get more specific. After the jump, let me show you a great list of cognitive biases. I’ll bet that there are at least a few that you haven’t heard of before! Decision making and behavioral biases Bandwagon effect — the tendency to do (or believe) things because many other people do (or believe) the same. Related to groupthink, herd behaviour, and manias. Bias blind spot — the tendency not to compensate for one’s own cognitive biases. Choice-supportive bias — the tendency to remember one’s choices as better than they actually were.
    [Show full text]
  • Common Types of Biases and How to Mitigate Them Appendix to “Unconscious Biases Impacting Your Business” by Omar Khan, Senior Manager
    Common Types of Biases and How to Mitigate Them Appendix to “Unconscious Biases Impacting Your Business” by Omar Khan, Senior Manager Anchoring Problem: Anchoring is a well-known bias where an initial piece of information can change how we perceive something. We often see it exploited in negotiations and marketing. Anchors can be completely arbitrary (e.g., a unrelated number like your SSN) or just a decoy choice (also known as a dominated option, click here for an example starting just after the 11 minute mark). Example: The exploitation of anchoring happens all around us all the time as described in the video referenced above. In the business world, anchoring often presents itself in business cases where we are influenced by things that aren’t related to the actual decision needing to be made, like if you’re making a choice between two pieces of software and there is a reference to a third piece of software that is not being considered (effectively a decoy option). Similarly, there are a lot of things you never pay list price for, but vendors will be quick to include this theoretical cost on the invoice (for example, showing you that the list price value of the software you’re getting is $1,000,000, but you’re also getting a discount of $900,000, so you only pay 1/10 the list price). In these cases, the realness of the list price needs to be considered, as does the contractual language, for renewal purposes. On performance reviews, it is easy for employees to self-assess to the highest rating possible and thus influence you into limiting your assessment into the highest rating and the rating just below that as options.
    [Show full text]
  • Superstition in the Machine
    Superstition in the Machine Alexander Riegler Center Leo Apostel, Vrije Universiteit Brussel Krijgskundestr. 33, B-1160 Brussels, Belgium [email protected] Abstract. It seems characteristic for humans to detect structural patterns in the world to anticipate future states. Therefore, scientific and common sense cogni- tion could be described as information processing which infers rule-like laws from patterns in data-sets. Since information processing is the domain of com- puters, artificial cognitive systems are generally designed as pattern discoverers. This paper questions the validity of the information processing paradigm as an explanation for human cognition and a design principle for artificial cogni- tive systems. Firstly, it is known from the literature that people suffer from conditions such as information overload, superstition, and mental disorders. Secondly, cognitive limitations such as a small short-term memory, the set- effect, the illusion of explanatory depth, etc. raise doubts as to whether human information processing is able to cope with the enormous complexity of an infi- nitely rich (amorphous) world. It is suggested that, under normal conditions, humans construct information rather than process it. The constructed information contains anticipations which need to be met. This can be hardly called information processing, since patterns from the “outside” are not used to produce action but rather to either justify an- ticipations or restructure the cognitive apparatus. When it fails, cognition switches to pattern processing, which, given the amorphous nature of the experiential world, is a lost cause if these patterns and inferred rules do not lead to a (partial) reorganisation of internal structures such that constructed anticipations can be met again.
    [Show full text]
  • Cognitive Biases: 15 More to Think About
    Tribunals, Autumn 2016 The JAC is not complacent and recognises there is more to do to increase judicial diversity. In 2015–16, 9% of JAC selections were BAME individuals; we want to improve that. We also want to see more candidates applying from different professional backgrounds, such as academia and the public sector. Diversity is improving across the judiciary with faster progress in some areas than others. Last year, 45% of the JAC’s recommended candidates were women. Recent statistics published by the Judicial Office show that 46% of tribunal judges are women and 12% are BAME. They also showed that the younger cohorts of judges are more diverse, a positive indicator for the future. We want the judiciary to reflect the society it serves and will continue to work with the government, judiciary and legal profession to ensure further progress is made. lori Frecker is Head of Equality and Diversity at the JAC Back to contents Cognitive biases: 15 more to think about DECISION MAKING By leslie Cuthbert In Lydia Seymour’s article in the Spring 2014 edition of Tribunals, she explained about a key unconscious bias known as ‘confrmation bias’. In the Autumn 2015 edition, I then described about the risks involved in being overconfdent. However, these are only two of the many cognitive biases that exist. Here are 15 other common cognitive, or unconscious, biases that we are all prone to falling foul of whether as witness, party or decision-maker. 1) Anchoring. This involves people being over-reliant on the frst piece of information that they receive.
    [Show full text]
  • Copyrighted Material
    Index academic style 97–102 critical reading 112–115 ad hominem fallacy 13–14 critical thinking, defi nitions advertising 28–31 of 2–3 apophenia 51,52 appeal to questionable authority data mining 47 15–16 depth, in essays 103 argument, defi ned 1–2 Dewey, Paul 2 argument from ignorance 10 discourse analysis 65 argumentum ad hominem – see ad domino eff ect 7 hominem fallacy argumentum ad ignorantiam – see externality 109–110 argument from ignorance argumentum ad verecundiam – fallacy 5 see appeal to questionable false binary opposition 12–13 authority feedback 94–106 arts, the 22–28 fringe science 58 astrology 59–61 availability heuristic 74–75 gambler’s fallacy 51, 53–54 graphology 61–64 begging the question 14–15 Grice’s Maxims 19, 21–22 blind/double-blind procedures 67 British Crime Survey 72 hyperbole 20–21 burden of proof, shifting 11 Bush, George W. 12 impressionists, the 24 butterfl y eff ect 7 jargon 55–56 correlation 66,COPYRIGHTED 69 MATERIAL clinical signifi cance 70–71 libel 31 clustering illusion 51, 53 complementary and alternative Markov chain 8 medicine 75–79 mindfulness 109, 110–112 confi rmation bias 75 control groups 66–67 Pareidolia 51, 52 crime statistics 71–73 Paul, Richard 3 bbindex.inddindex.indd 113535 112/03/122/03/12 77:46:46 AAMM 136 INDEX phrenology 64–65 refl ection 43–44 propositional logic 5 regression 69 protoscience 54, 57–58 research methods and statistics 65–73 non sequitur 6 rhetoric 20 Nostradamus 46–48 slippery slope 6–7 observational methods 67–68 snowball eff ect 7 Occam’s Razor 35–36, 38, 85 Socratic
    [Show full text]
  • Pareidolia - Wikipedia, the Free Encyclopedia
    Pareidolia - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Pareidolia Pareidolia From Wikipedia, the free encyclopedia Pareidolia (/pærɨˈdoʊliə/ parr-i-DOH-lee-ə) is a psychological phenomenon involving a stimulus (an image or a sound) wherein the mind perceives a familiar pattern where none actually exists. Common examples are perceived images of animals, faces, or objects in cloud formations, the "man in the moon", the "moon rabbit", and hidden messages within recorded music played in reverse or at higher- or lower-than-normal speeds. A satellite photo of a mesa Pareidolia is the visual or auditory form of in Cydonia, often called apophenia, which is the perception of patterns the Face on Mars. Later within random data. Combined with apophenia imagery from other angles and hierophany (manifestation of the sacred), did not show the illusion. pareidolia may have helped ancient societies organize chaos and make the world intelligible. [1][2] Contents 1 Etymology 2 Explanations 3 Mimetoliths 4 Projective tests 5 Art 6 Religious 7 Computer vision 8 Related phenomena 9 Examples 10 See also 11 References 12 External links Etymology The word is derived from the Greek words para (παρά, "beside, alongside, instead [of]", in this context meaning something faulty or wrong) and the noun eidōlon (εἴδωλον "image, form, shape", the diminutive of eidos). 1 sur 8 07/10/2015 17:07 Pareidolia - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Pareidolia Explanations Pareidolia can cause people to interpret random images, or patterns of light and shadow, as faces.[3]A 2009 magnetoencephalography study found that objects perceived as faces evoke an early (165 ms) activation of the fusiform face area at a time and location similar to that evoked by faces — whereas other common objects do not evoke such activation.
    [Show full text]