LEADER 03587nam 22005775 450 001 9910357826903321 005 20200703010337.0 010 $a3-030-31780-3 024 7 $a10.1007/978-3-030-31780-5 035 $a(CKB)4100000009844728 035 $a(MiAaPQ)EBC5978963 035 $a(DE-He213)978-3-030-31780-5 035 $a(EXLCZ)994100000009844728 100 $a20191114d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBig Data Analytics in U.S. Courts$b[electronic resource] $eUses, Challenges, and Implications /$fby Dwight Steward, Roberto Cavazos 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Palgrave Pivot,$d2019. 215 $a1 online resource (89 pages) 225 1 $aPalgrave Advances in the Economics of Innovation and Technology,$x2662-3862 311 $a3-030-31779-X 320 $aIncludes bibliographical references and index. 327 $a1. Data Analytics and Litigation -- 2. History of Data Analysis in US Courts -- 3. Examples of Litigation Involving Big Data Analytics -- 4. The Courts as Gatekeeper of Big Data Evidence -- 5. Indirect Use of Big Data Analytics in US Courts -- 6. Future Challenges and Recommendations. 330 $aThis Palgrave Pivot identifies the key legal, economic, and policy issues surrounding the allowance to use and interpret electronic data consistently and in a scientifically valid manner in U.S. courts. Evidence based on the analysis of large amounts of electronic data ("Big Data") plays an increasing role in civil court disputes, providing information that could not have been obtained from a witness stand. While Big Data evidence presents opportunities, it also presents legal and public policy challenges and concerns. How can one be sure that deviations found in Big Data fall outside the norm? If statistical analyses can be conducted and presented different ways, how can judges and juries make sense of conflicting interpretations? When does Big Data extraction stop being investigative and instead become an invasion of privacy? This book traces the history of Big Data use in U.S. courts, couples current case studies with legal challenges to explore key controversies, and suggests how courts can change the way they handle Big Data to ensure that findings are statistically significant and scientifically sound. 410 0$aPalgrave Advances in the Economics of Innovation and Technology,$x2662-3862 606 $aLaw and economics 606 $aEconomic policy 606 $aPublic finance 606 $aLaw and Economics$3https://scigraph.springernature.com/ontologies/product-market-codes/W39000 606 $aR & D/Technology Policy$3https://scigraph.springernature.com/ontologies/product-market-codes/W43000 606 $aPublic Economics$3https://scigraph.springernature.com/ontologies/product-market-codes/W34000 615 0$aLaw and economics. 615 0$aEconomic policy. 615 0$aPublic finance. 615 14$aLaw and Economics. 615 24$aR & D/Technology Policy. 615 24$aPublic Economics. 676 $a347.736 700 $aSteward$b Dwight$4aut$4http://id.loc.gov/vocabulary/relators/aut$0999600 702 $aCavazos$b Roberto$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910357826903321 996 $aBig Data Analytics in U.S. Courts$92294580 997 $aUNINA