Datascienceethicsisallaboutwhatisrightandwrongwhenconductingdatascience.Datasciencehassofarbeenprimarilyusedforpositiveoutcomesforbusinessesandsociety.However,justaswithanytechnology,datasciencehasalsocomewithsomenegativeconsequences:anincreaseofprivacyinvasion,data-drivendiscriminationagainstsensitivegroups,anddecisionmakingbycomplexmodelswithoutexplanations.Whiledatascientistsandbusinessmanagersarenotinherentlyunethical,theyarenottrainedtoweightheethicalconsiderationsthatcomefromtheirwork-iDataScienceEthics/iaddressesthisincreasinglysignificantgapandhighlightsdifferentconceptsandtechniquesthataidunderstanding,rangingfromk-anonymityanddifferentialprivacytohomomorphicencryptionandzero-knowledgeproofstoaddressprivacyconcerns,techniquestoremovediscriminationagainstsensitivegroups,andvariousexplainableAItechniques.Real-lifecautionarytalesfurtherillustratetheimportanceandpotentialimpactofdatascienceethics,includingtalesofracistbots,searchcensoring,governmentbackdoors,andfacerecognition.Thebookispunctuatedwithstructuredexercisesthatprovidehypotheticalscenariosandethicaldilemmasforreflectionthatteachreadershowtobalancetheethicalconcernsandtheutilityofdata.
