Where data has been mis used and negatively impacted customers And Employees It S An Important Perspective On How Data employees It s an important perspective on how data be misused and offers ideas on how we need to adapt our control culture in an increasing data driven worldThe author passion for the subject can be felt through the book and although enjoyed it did leave me feeling a little saddened to
#See So Many Examples #so many examples poorly implemented analyticsThe book would have benefitted from a broader perspective and also included success stories to show how data can also be a force for good to simplify and improve our lives At the end of the day these are tools and it s how people use them that can produce these WMD sAlso reminds us of the need to educate the wider community on how data is used and that data asset needs to benefit everyone I was already familiar with some of the matters discussed in the book but I did not know the full extent of the use of the technology described nor data regarding its abuseThe book covers several different fields all of them tied by the common theme of how the lack of understanding of algorithms sometimes unintentionally sometimes for personal gain leads to entirely false evaluations of phenomena based on spurious correlations or too simplistic approaches that uite too often provide false bases on top of which important but entirely unsubstantiated decisions are already being madeI strongly advise anyone but in particular people working in IT to read this book since it is important that people are made aware of how little their choices and who they really are may matter in their lives once their data is fed to the algorithms most companies use nowadaysIT people can and should use this knowledge to push for a review of how people data is used to make decisions that affect people s live. Iety These weapons of math destruction score teachers and students sort CVs grant or deny loans evaluate workers target voters and monitor our health O'Neilcalls on modellers to takeresponsibility for their algorithms and on policy makers to regulate their use But in the end it's up to us to becomesavvy about the models that govern our lives.
Cathy ONeil ò 4 Download
Etc and ou have a recipe for so called innovation leaving us much worse off Not much insightful WMD is a well known book by now and I have been meanin to read it for some time It is short and easy to read Plenty of examples from different industries starting with finance Cathy s experience in this and other Big Data and Data Scientist roles helps to give her message credance She explains the examples wellIf ou are looking for a really in depth text this is not message credance She explains the examples wellIf ou are looking for a really in depth text this is not This is meant for everyone and in that respect it is a good book When I started reading it I thought perhaps it could do with the Michael Lewis touch Flashboys Moneyball etc but when I finished I thought not It looks like this is the way the future looks and it is not pretty the way it is right now Let s hope that the technology and scale leads to greater beneifts to all and *not mass categorisation with a dumb central machine This book is a great introduction to the moral dimensions of science *mass categorisation with a dumb central machine This book is a great introduction to the moral dimensions of science many people s mind science is independent objective and dispassionate This book succinctly challenges that naive view of science It demonstrates sometimes too often how science is a very human activity and inarguably exists within a very powerful cultural matrix that determines what we choose to measure how we interpret data and the subtle forces of self interestAs someone who develops WMD s for industry I found the book extremely useful in articulating our moral responsibilities in the field My hope is this book and it s cautionary tales becomes a compulsory text for those engaged in the field and that it provides a moral mirror in which we can reflect often Well written and simplified This is an easy to read book with a raft of real world examples across a range of industries from education finance to ecommerse on. And necessary book the opposite is true The models being used today are opaue unregulated and incontestable even when they're wrong Most troubling they reinforce discrimination creating a toxic cocktail for democracyTracing the arc of a person's life Cathy O'Neil exposes the black box models that shape our future as individuals and as a soc. ,
This book offers the readers a uniue and balanced point of view on how big data can be used to create ineuality for many people in our society to benefit a very few groups ie the 1% the technocrats and government agencies Interesting but very American based and many examples were meaningless to me in Britain This books makes an important argument about the lack of regulatory oversight as ever sections of our lives This books makes an important argument about the lack of regulatory oversight as ever sections of our lives to be analysed and decided via computational means Certainly this is an area of public policy that needs marked urgent attention While I somewhat agree with O Neil S Point Of View s point of view level detail and the depth to which her arguments are developed seemed somewhat cursory We re often left having to rely on the author s say so as the evidence for the conclusions is seldom fully developed It reads somewhat like an extended NYT think piece which is a fine style for a Sunday newspaper article but I expected uite a bit detail in a long form work Written by one of the articulate practitioners of Data Science Weapons of Math Destruction uestions the use of algorithms and analytics in a range of domains including education the justice system and politicsThe essential issue seems to be that some of these models have two flaws1 the results are to some extent self fulfilling eg Recidivism models that inflict longer sentences on some offenders when time in jail makes people likely to reoffend2 the models are not adjusted in light of incorrect predictions eg Models of teacher performance that don t respond to huge Mistida year toear swings in scores and incorporate no feedbacks from students later life experienceCouple this with a peculiarly American willingness to screw its citizens over with unpredictable work schedules and docking of pay when wellness targets are not met. We live in the age of the algorithm Increasingly the decisions that affect our lives whether we get a job or a loan how much we pay for insurance are being made by mathematical models In theory this should lead to greater fairness everyone is judged according to the same rules and bias is eliminated But as Cathy O'Neil reveals in this urgent. ,