When do you personally stop using a product, a service, or something that brings you convenience, after you have learned that it has seriously harmed people along the way as it was being constructed?? 2) What message do we give to the next generation when we ‘let it go’ and find reasons why our lack of direct involvement justifies the use of the product/service, etc. What are the material consequences of letting things go? 3) What is the biblical response to acquiescing to something when it is wrong, even when you are not directly involved in the wrongdoing? 4) What were the surprises you found in the PEF AI 2017 report? 5) What overall conclusions do you draw from this? 600 words, APAPEFAIreportSeptember2017WEB.pdf

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Artificial​ ​Intelligence:  Practice​ ​and  Implications​ ​for  Journalism

September​ ​2017 

  Mark​ ​Hansen*   Meritxell​ ​Roca-Sales**  Jon​ ​Keegan**  George​ ​King**    *​ ​Brown​ ​Institute​ ​for​ ​Media​ ​Innovation   **​ ​Tow​ ​Center​ ​for​ ​Digital​ ​Journalism         

Platforms​ ​and​ ​Publishers:​ ​Policy​ ​Exchange​ ​Forum​ ​I  June​ ​13,​ ​2017​ ​|​ ​Columbia​ ​Journalism​ ​School 

Organized​ ​by​ ​the​ ​Tow​ ​Center​ ​for​ ​Digital​ ​Journalism   and​ ​the​ ​Brown​ ​Institute​ ​for​ ​Media​ ​Innovation 

Artificial​ ​Intelligence:​ ​Practice​ ​and​ ​Implications​ ​for​ ​Journalism ​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​1 

Executive​ ​Summary 2 

Introduction 4 

Discussion​ ​I:​ ​Al​ ​in​ ​the​ ​Newsroom 7 

Case​ ​Studies:​ ​‘A​ ​Spectrum​ ​of​ ​Autonomy’ 8 

Data 9 

Challenges​ ​for​ ​Publishers:​ ​Large​ ​Newsrooms​ ​and​ ​Small 9 

Discussion​ ​II:​ ​Technology 10 

Automation​ ​and​ ​Personalization​ ​of​ ​Stories 10 

Commenting​ ​Systems​ ​and​ ​Audience​ ​Engagement 12 

Proprietary​ ​Versus​ ​Open​ ​Algorithms 13 

Challenges​ ​and​ ​Limitations 13 

Discussion​ ​III:​ ​Algorithms​ ​and​ ​Ethics 14 

Transparency​ ​and​ ​Accountability 14 

Editorial​ ​Decisions​ ​and​ ​Bias 15 

Ethical​ ​Use​ ​of​ ​Data 16 

Concluding​ ​Remarks 17 

    The​ ​Policy​ ​Exchange​ ​Forums​ ​are​ ​a​ ​critical​ ​component​ ​of​ ​the​ ​Tow​ ​Center’s​ ​Platforms​ ​and  Publishers​ ​research​ ​project.​ ​In​ ​these​ ​sessions,​ ​participants​ ​representing​ ​both​ ​the​ ​platforms​ ​and  publishing​ ​sides​ ​of​ ​the​ ​news​ ​industry​ ​can​ ​engage​ ​on​ ​issues​ ​related​ ​to​ ​the​ ​ethical​ ​and​ ​civic  values​ ​of​ ​journalism.​ ​The​ ​forum​ ​focuses​ ​on​ ​the​ ​relationships​ ​between​ ​technology,​ ​business,  journalism,​ ​and​ ​ethics,​ ​and​ ​brings​ ​together​ ​diverse​ ​stakeholders​ ​to​ ​discuss​ ​current​ ​issues​ ​and  surface​ ​potential​ ​new​ ​ones.    The​ ​project​ ​is​ ​underwritten​ ​by​ ​the​ ​John​ ​D.​ ​and​ ​Catherine​ ​T.​ ​MacArthur​ ​Foundation,​ ​with  additional​ ​support​ ​by​ ​the​ ​John​ ​S.​ ​and​ ​James​ ​L.​ ​Knight​ ​Foundation,​ ​the​ ​Foundation​ ​to​ ​Promote  Open​ ​Society,​ ​and​ ​The​ ​Abrams​ ​Foundation.         

Artificial​ ​Intelligence:​ ​Practice​ ​and​ ​Implications​ ​for​ ​Journalism ​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​2 

Executive​ ​Summary    The​ ​increasing​ ​presence​ ​of​ ​artificial​ ​intelligence​ ​and​ ​automated​ ​technology​ ​is​ ​changing  journalism.​ ​While​ ​the​ ​term​​ ​​artificial​ ​intelligence​ ​dates​ ​back​ ​to​ ​the​ ​1950s,​ ​and​ ​has​ ​since​ ​acquired  several​ ​meanings,​ ​there​ ​is​ ​a​ ​general​ ​consensus​ ​around​ ​the​ ​nature​ ​of​ ​AI​ ​as​ ​the​ ​theory​ ​and  development​ ​of​ ​computer​ ​systems​ ​able​ ​to​ ​perform​ ​tasks​ ​normally​ ​requiring​ ​human​ ​intelligence.  Since​ ​many​ ​of​ ​the​ ​AI​ ​tools​ ​journalists​ ​are​ ​now​ ​using​ ​come​ ​from​ ​other​ ​disciplines—computer  science,​ ​statistics,​ ​and​ ​engineering,​ ​for​ ​example—they​ ​tend​ ​to​ ​be​ ​general​ ​purpose.     Now​ ​that​ ​journalists​ ​are​ ​using​ ​AI​ ​in​ ​the​ ​newsroom,​ ​what​ ​must​ ​they​ ​know​ ​about​ ​these  technologies,​ ​and​ ​what​ ​must​ ​technologists​ ​know​ ​about​ ​journalistic​ ​standards​ ​when​ ​building  them?    On​ ​June​ ​13,​ ​2017,​ ​the​ ​Tow​ ​Center​ ​for​ ​Digital​ ​Journalism​ ​and​ ​the​ ​Brown​ ​Institute​ ​for​ ​Media  Innovation​ ​convened​ ​a​ ​policy​ ​exchange​ ​forum​ ​of​ ​technologists​ ​and​ ​journalists​ ​to​ ​consider​ ​how  artificial​ ​intelligence​ ​is​ ​impacting​ ​newsrooms​ ​and​ ​how​ ​it​ ​can​ ​be​ ​better​ ​adapted​ ​to​ ​the​ ​field​ ​of  journalism.​ ​The​ ​gathering​ ​explored​ ​questions​ ​like:​ ​How​ ​can​ ​journalists​ ​use​ ​AI​ ​to​ ​assist​ ​the  reporting​ ​process?​ ​Which​ ​newsroom​ ​roles​ ​might​ ​AI​ ​replace?​ ​What​ ​are​ ​some​ ​areas​ ​of​ ​AI​ ​that  news​ ​organizations​ ​have​ ​yet​ ​to​ ​capitalize​ ​on?​ ​Will​ ​AI​ ​eventually​ ​be​ ​a​ ​part​ ​of​ ​the​ ​presentation​ ​of  every​ ​news​ ​story?    Findings   

– AI​ ​tools​ ​can​ ​help​ ​journalists​ ​tell​ ​new​ ​kinds​ ​of​ ​stories​ ​that​ ​were​ ​previously​ ​too  resource-impractical​ ​or​ ​technically​ ​out​ ​of​ ​reach.​ ​While​ ​AI​ ​may​ ​transform​ ​the​ ​journalism  profession,​ ​it​ ​will​ ​enhance,​ ​rather​ ​than​ ​replace,​ ​journalists’​ ​work.​ ​In​ ​fact,​ ​for​ ​AI​ ​to​ ​be​ ​used  properly,​ ​it​ ​is​ ​essential​ ​that​ ​humans​ ​stay​ ​in​ ​the​ ​loop.  

– There​ ​is​ ​both​ ​a​ ​knowledge​ ​gap​ ​and​ ​communication​ ​gap​ ​between​ ​technologists​ ​designing  AI​ ​and​ ​journalists​ ​using​ ​it​ ​that​ ​may​ ​lead​ ​to​ ​journalistic​ ​malpractice. 

– Readers​ ​deserve​ ​to​ ​be​ ​given​ ​a​ ​transparent​ ​methodology​ ​of​ ​how​ ​AI​ ​tools​ ​were​ ​used​ ​to  perform​ ​an​ ​analysis,​ ​identify​ ​a​ ​pattern,​ ​or​ ​report​ ​a​ ​finding​ ​in​ ​a​ ​story. 

– While​ ​the​ ​intersection​ ​of​ ​AI​ ​and​ ​data​ ​offers​ ​new​ ​kinds​ ​of​ ​opportunities​ ​for​ ​reader  engagement,​ ​monetization,​ ​and​ ​news​ ​feed​ ​personalization,​ ​with​ ​this​ ​comes​ ​the​ ​challenge  of​ ​finding​ ​a​ ​balance​ ​between​ ​creating​ ​echo​ ​chambers​ ​and​ ​remaining​ ​committed​ ​to  journalism’s​ ​public​ ​service​ ​mission.  

– Ethical​ ​use​ ​and​ ​disclosure​ ​of​ ​data​ ​(how​ ​information​ ​from​ ​users​ ​is​ ​collected,​ ​stored,​ ​used,  analyzed,​ ​and​ ​shared)​ ​is​ ​a​ ​fundamental​ ​issue​ ​that​ ​journalists​ ​need​ ​to​ ​confront. 

Artificial​ ​Intelligence:​ ​Practice​ ​and​ ​Implications​ ​for​ ​Journalism ​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​3 

– The​ ​potential​ ​for​ ​AI​ ​to​ ​augment​ ​the​ ​work​ ​of​ ​the​ ​human​ ​data​ ​journalist​ ​holds​ ​great  promise,​ ​but​ ​open​ ​access​ ​to​ ​data​ ​remains​ ​a​ ​challenge.  

-​ ​​ ​​ ​​ ​Artificial​ ​intelligence​ ​is​ ​unpredictable;​ ​we​ ​don’t​ ​feel​ ​that​ ​confident​ ​predicting​ ​where​ ​the  biggest​ ​problems​ ​will​ ​crop​ ​up.​ ​Vigilance​ ​on​ ​the​ ​part​ ​of​ ​both​ ​technologists​ ​and​ ​journalists  is​ ​necessary​ ​to​ ​keep​ ​these​ ​systems​ ​in​ ​check. 

  Recommendations   

– Investment​ ​in​ ​training​ ​editors​ ​and​ ​reporters​ ​is​ ​crucial.​ ​As​ ​AI​ ​tools​ ​enter​ ​newsrooms,  journalists​ ​need​ ​to​ ​understand​ ​how​ ​to​ ​use​ ​new​ ​resources​ ​for​ ​storytelling—not​ ​only  ethically,​ ​but​ ​also​ ​efficiently. 

– Developing​ ​and​ ​promoting​ ​the​ ​use​ ​of​ ​shared​ ​guidelines​ ​among​ ​journalists​ ​and  technologists​ ​around​ ​ethical​ ​use​ ​of​ ​data​ ​and​ ​public​ ​disclosure​ ​of​ ​methodology​ ​is​ ​a​ ​must.  Existing​ ​AI​ ​tools,​ ​like​ ​chatbots​ ​and​ ​commenting​ ​systems,​ ​should​ ​be​ ​used​ ​as​ ​opportunities  for​ ​thinking​ ​about​ ​how​ ​to​ ​apply​ ​editorial​ ​values​ ​and​ ​standards​ ​to​ ​the​ ​early​ ​stages​ ​of​ ​new  journalistic-specific​ ​technology. 

– For​ ​custom-built​ ​AI,​ ​which​ ​is​ ​too​ ​expensive​ ​for​ ​smaller​ ​operations​ ​to​ ​afford,​ ​newsrooms  should​ ​consider​ ​investing​ ​time​ ​in​ ​partnerships​ ​with​ ​academic​ ​institutions. 

– There​ ​needs​ ​to​ ​be​ ​a​ ​concerted​ ​and​ ​continued​ ​effort​ ​to​ ​fight​ ​hidden​ ​bias​ ​in​ ​AI,​ ​often  unacknowledged​ ​but​ ​always​ ​present,​ ​since​ ​tools​ ​are​ ​programmed​ ​by​ ​humans.​ ​Journalists  must​ ​strive​ ​to​ ​insert​ ​transparency​ ​into​ ​their​ ​stories,​ ​noting​ ​in​ ​familiar​ ​and​ ​non-technical  terms​ ​how​ ​AI​ ​was​ ​used​ ​to​ ​help​ ​their​ ​reporting​ ​or​ ​production.   

Artificial​ ​Intelligence:​ ​Practice​ ​and​ ​Implications​ ​for​ ​Journalism ​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​4 

Introduction  By​ ​Mark​ ​Hansen,​​ ​​director​ ​of​ ​Columbia’s​ ​Brown​ ​Institute​ ​for​ ​Media​ ​Innovation    Our​ ​conversation​ ​at​ ​June’s​ ​forum​ ​began​ ​where​ ​these​ ​discussions​ ​often​ ​do:​ ​with​ ​the​ ​idea​ ​that​ ​we  can​ ​enhance​ ​human​ ​ability​ ​through​ ​computation.​ ​Our​ ​specific​ ​focus​ ​was​ ​on​ ​journalism​ ​and​ ​tasks  associated​ ​with​ ​reporting,​ ​writing,​ ​and​ ​designing​ ​impactful​ ​visualizations​ ​and​ ​other​ ​journalistic  “experiences.”      First​ ​and​ ​foremost,​ ​computation,​ ​as​ ​a​ ​tool,​ ​extends​ ​our​ ​ability​ ​to​ ​perform​ ​basic​ ​calculations—that’s  the​ ​old​ ​magic​ ​of​ ​spreadsheets​ ​and​ ​the​ ​success​ ​of​ ​computer-assisted​ ​reporting.​ ​But​ ​advances​ ​in  computation​ ​also​ ​bring​ ​the​ ​ability​ ​to​ ​recognize​ ​new​ ​data​ ​types,​ ​new​ ​digital​ ​objects​ ​that​ ​are​ ​open  to​ ​computational​ ​techniques​ ​of​ ​analysis.​ ​And​ ​with​ ​new​ ​data​ ​types​ ​come​ ​new​ ​kinds​ ​of​ ​questions  about​ ​the​ ​world​ ​around​ ​us.​ ​More​ ​and​ ​more​ ​of​ ​our​ ​world​ ​is​ ​being​ ​rendered​ ​in​ ​digital​ ​data,​ ​so​ ​that  (in​ ​journalistic​ ​terms)​ ​our​ ​data​ ​sources​ ​are​ ​becoming​ ​more​ ​diverse—and​ ​the​ ​information​ ​we​ ​can  draw​ ​from​ ​them,​ ​deeper​ ​and​ ​more​ ​interesting.​ ​It​ ​almost​ ​begs​ ​for​ ​a​ ​kind​ ​of​ ​aesthetic​ ​that​ ​prizes  new​ ​computational​ ​voices​ ​in​ ​the​ ​same​ ​way​ ​we​ ​value​ ​a​ ​new​ ​human​ ​source​ ​with​ ​a​ ​unique  perspective​ ​on​ ​a​ ​story.     To​ ​ground​ ​what​ ​we​ ​mean​ ​by​ ​“enhancing​ ​our​ ​abilities”​ ​and​ ​the​ ​shift​ ​to​ ​new​ ​data​ ​types,​ ​let’s  consider​ ​how​ ​standard​ ​journalistic​ ​practice​ ​has​ ​changed​ ​when​ ​it​ ​comes​ ​to​ ​wading​ ​through​ ​piles  of​ ​documents,​ ​perhaps​ ​returned​ ​by​ ​a​ ​FOIA​ ​request.​ ​With​ ​machine​ ​learning,​ ​we​ ​can​ ​pore​ ​over  thousands​ ​upon​ ​thousands​ ​of​ ​documents​ ​in​ ​a​ ​kind​ ​of​ ​mechanistic​ ​reading.​ ​“Reading”​ ​at​ ​this​ ​scale  was​ ​not​ ​possible​ ​a​ ​couple​ ​decades​ ​ago,​ ​not​ ​without​ ​a​ ​lot​ ​of​ ​human​ ​effort.​ ​Now,​ ​instead​ ​of​ ​taking  in​ ​text​ ​line-by-line​ ​and​ ​word-by-word—as​ ​you​ ​may​ ​now​ ​be​ ​doing​ ​with​ ​this​ ​text—machine​ ​learning,  or​ ​more​ ​specifically​ ​Natural​ ​Language​ ​Processing,​ ​helps​ ​us​ ​to​ ​create​ ​summaries​ ​of​ ​texts​ ​or  divides​ ​them​ ​into​ ​groups​ ​with​ ​common​ ​features​ ​(called​ ​clusters).     Italo​ ​Calvino​ ​provides​ ​a​ ​simplified​ ​view​ ​of​ ​this​ ​in​ ​​If​ ​on​ ​a​ ​Winter’s​ ​Night​ ​a​ ​Traveler​.​ ​A​ ​character  from​ ​the​ ​book​ ​named​ ​Ludmilla​ ​explains​ ​that​ ​she​ ​has​ ​a​ ​computer​ ​program​ ​that​ ​reduces​ ​a​ ​text​ ​to  individual​ ​words​ ​and​ ​their​ ​frequencies.​ ​From​ ​here,​ ​she​ ​can​ ​much​ ​more​ ​easily​ ​“read”:     

What​ ​is​ ​the​ ​reading​ ​of​ ​the​ ​text,​ ​in​ ​fact,​ ​except​ ​the​ ​recording​ ​of​ ​certain​ ​thematic​ ​re-occurrences,  certain​ ​insistences​ ​of​ ​forms​ ​and​ ​meanings?     In​ ​a​ ​novel​ ​of​ ​fifty​ ​to​ ​a​ ​hundred​ ​thousand​ ​words​ ​.​ ​.​ ​.​ ​I​ ​advise​ ​you​ ​to​ ​observe​ ​immediately​ ​the​ ​words  that​ ​are​ ​repeated​ ​about​ ​twenty​ ​times.​ ​Look​ ​here​ ​.​ ​.​ ​.  

   blood,​ ​cartridge​ ​belt,​ ​commander,​ ​do,​ ​have,​ ​immediately,​ ​it,​ ​life,​ ​seen,​ ​sentry,​ ​shots,​ ​spider,  teeth,​ ​together,​ ​you​ ​.​ ​.​ ​.  

Artificial​ ​Intelligence:​ ​Practice​ ​and​ ​Implications​ ​for​ ​Journalism ​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​5 

Don’t​ ​you​ ​already​ ​have​ ​a​ ​clear​ ​idea​ ​what​ ​it’s​ ​about?     With​ ​computation,​ ​we​ ​extend​ ​our​ ​abilities​ ​to​ ​“read”​ ​thousands​ ​or​ ​millions​ ​of​ ​documents.​ ​(Franco  Moretti​ ​at​ ​Stanford​ ​formalizes​ ​this​ ​difference,​ ​contrasting​ ​“distant,”​ ​or​ ​machine-mediated​ ​reading,  with​ ​“close,”​ ​or​ ​line-by-line,​ ​reading.)​ ​These​ ​new​ ​abilities,​ ​however,​ ​necessarily​ ​change​ ​how​ ​we  think​ ​about​ ​collections​ ​of​ ​documents​ ​and​ ​the​ ​knowledge​ ​we​ ​pull​ ​from​ ​them—our​ ​abilities​ ​extend,  but​ ​also​ ​our​ ​perspective​ ​changes.     As​ ​with​ ​text​ ​sources,​ ​digital​ ​images,​ ​audio,​ ​and​ ​video​ ​are​ ​also​ ​all​ ​now​ ​open​ ​to​ ​computation.​ ​In​ ​the  same​ ​way,​ ​ou

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