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Ep 121: How Artificial Intelligence Creates Discrimination in HR & Recruiting

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Workology Podcast interview with Dr. Jutta Treviranus discussing how artificial intelligence can create opportunities for discrimination. The interview discusses how inclusive design can help eliminate discrimination in hiring, recruiting and employment decisions.

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Ep 121: How Artificial Intelligence Creates Discrimination in HR & Recruiting

  1. 1. Episode​ ​121:​ ​Future​ ​of​ ​Work:​ ​How​ ​Artificial  Intelligence​ ​Creates​ ​Discrimination​ ​in​ ​HR​ ​&  Recruiting     Episode​ ​Link:​ ​​http://workolo.gy/ep121-wp INTRO:​ ​​[00:00:00]​ ​​Welcome​ ​to​ ​the​ ​Workology​ ​podcast​ ​a​ ​podcast​ ​for​ ​the​ ​disruptive  workplace​ ​leader.​ ​Join​ ​host​ ​Jessica​ ​Miller-​ ​Merrell​ ​founder​ ​of​ ​Workology.com​ ​as​ ​she  sits​ ​down​ ​and​ ​gets​ ​to​ ​the​ ​bottom​ ​of​ ​trends​ ​tools​ ​and​ ​case​ ​studies​ ​for​ ​the​ ​business  leader​ ​HR​ ​and​ ​recruiting​ ​professional​ ​who​ ​is​ ​tired​ ​of​ ​this​ ​status​ ​quo.​ ​Now​ ​here's  Jessica​ ​with​ ​this​ ​episode​ ​of​ ​Workology.    Jessica:​ ​​[00:00:25]​ ​​Welcome​ ​to​ ​a​ ​new​ ​series​ ​on​ ​the​ ​Workology​ ​podcast​ ​we're​ ​kicking  off​ ​that​ ​focuses​ ​on​ ​the​ ​future​ ​of​ ​work.​ ​This​ ​series​ ​is​ ​in​ ​collaboration​ ​with​ ​the  partnership​ ​on​ ​employment​ ​and​ ​accessible​ ​technology​ ​or​ ​PEAT.​ ​You​ ​can​ ​learn​ ​more  about​ ​PEAT​ ​at​ ​peatworks.org.    Jessica:​ ​​[00:00:38]​ ​​Everywhere​ ​I​ ​turn​ ​there​ ​seems​ ​to​ ​be​ ​conversations​ ​in​ ​the​ ​human  resources​ ​and​ ​recruiting​ ​industry​ ​centered​ ​around​ ​artificial​ ​intelligence.​ ​It​ ​is​ ​truly  permeating​ ​our​ ​landscape,​ ​however,​ ​myself,​ ​along​ ​with​ ​PEAT​ ​we​ ​wondered​ ​if​ ​A.I.​ ​was  as​ ​inclusive​ ​as​ ​it​ ​could​ ​be.​ ​We’re​ ​talking​ ​digging​ ​deep​ ​looking​ ​at​ ​the​ ​inclusiveness​ ​of  machine​ ​learning​ ​technology​ ​and​ ​exploring​ ​the​ ​implications​ ​for​ ​people​ ​with​ ​disabilities.     I’m​ ​joined​ ​with​ ​Dr.​ ​Jutta​ ​Treviranus.​​ ​She​ ​​is​ ​the​ ​director​ ​of​ ​the​ ​​Inclusive​ ​Design​ ​Research  Centre​​ ​and​ ​a​ ​professor​ ​at​ ​OCAD​ ​University.​ ​Jutta​ ​has​ ​been​ ​working​ ​towards​ ​more  inclusive​ ​and​ ​accessible​ ​technology​ ​for​ ​much​ ​of​ ​her​ ​life.​ ​Her​ ​focus​ ​is​ ​now​ ​on​ ​improving  artificial​ ​intelligence​ ​systems​ ​so​ ​they​ ​can​ ​better​ ​serve​ ​everyone,​ ​including​ ​people​ ​with  disabilities,​ ​and​ ​organizes​ ​​hackathons​​ ​towards​ ​this​ ​goal.    Jutta,​ ​welcome​ ​to​ ​the​ ​Workology​ ​Podcast.​ ​Can​ ​you​ ​talk​ ​a​ ​little​ ​bit​ ​about​ ​your  background​ ​because​ ​I​ ​think​ ​it's​ ​really​ ​interesting​ ​and​ ​different​ ​than​ ​many​ ​of​ ​the​ ​guests  Workology​ ​Podcast​​ ​​|​ ​www.workologypodcast.com​ ​|​ ​@workology
  2. 2. on​ ​this​ ​podcast​ ​previously?    Jutta:​ ​​[00:01:42]​ ​​Ok.​ ​Sure.​ ​I've​ ​been​ ​interested​ ​in​ ​the​ ​liberating​ ​potential​ ​and​ ​worried  about​ ​the​ ​exclusionary​ ​risks​ ​of​ ​computers​ ​and​ ​networks​ ​and​ ​digital​ ​systems​ ​since​ ​the  emergence​ ​of​ ​personal​ ​computers​ ​in​ ​the​ ​late​ ​70s.​ ​I​ ​was​ ​working​ ​with​ ​a​ ​very​ ​very  diverse​ ​group​ ​of​ ​individuals​ ​at​ ​a​ ​university​ ​and​ ​I​ ​felt​ ​that​ ​computers​ ​and​ ​networks​ ​and  digital​ ​systems​ ​had​ ​the​ ​potential​ ​to​ ​be​ ​wonderful​ ​translators.     So​ ​I​ ​worked​ ​in​ ​that​ ​particular​ ​field​ ​getting​ ​to​ ​know​ ​the​ ​technology​ ​better​ ​and​ ​I​ ​started  the​ ​inclusive​ ​Design​ ​Research​ ​Center​ ​in​ ​1993​ ​to​ ​proactively​ ​work​ ​to​ ​make​ ​sure​ ​that  emerging​ ​socio​ ​technical​ ​practices​ ​new​ ​technologies​ ​are​ ​inclusive​ ​of​ ​everyone.​ ​The  team​ ​that​ ​I​ ​created​ ​which​ ​continues​ ​and​ ​will​ ​be​ ​25​ ​years​ ​as​ ​of​ ​next​ ​June​ ​is​ ​a​ ​very​ ​very  diverse​ ​distributed​ ​community​ ​with​ ​project​ ​partners​ ​all​ ​around​ ​the​ ​globe.​ ​We​ ​grew​ ​up  with​ ​the​ ​web​ ​and​ ​have​ ​espoused​ ​open​ ​practices​ ​and​ ​communicate​ ​practices​ ​before​ ​I  think​ ​even​ ​open​ ​was​ ​a​ ​common​ ​term.     We​ ​moved​ ​to​ ​Oxford​ ​University​ ​in​ ​2010​ ​and​ ​I​ ​started​ ​a​ ​graduate​ ​program​ ​in​ ​inclusive  design​ ​and​ ​the​ ​graduate​ ​program​ ​is​ ​intended​ ​to​ ​pioneer​ ​a​ ​more​ ​diversity​ ​supportive  form​ ​of​ ​education.​ ​We're​ ​experimenting​ ​with​ ​an​ ​inclusive​ ​education​ ​to​ ​prepare  graduates​ ​for​ ​all​ ​of​ ​the​ ​variety​ ​of​ ​things​ ​that​ ​they're​ ​encountering​ ​and​ ​the​ ​many  changes​ ​that​ ​are​ ​happening​ ​in​ ​our​ ​community.​ ​So​ ​one​ ​of​ ​the​ ​things​ ​I'd​ ​love​ ​to​ ​tell​ ​you  about​ ​is​ ​our​ ​perspective​ ​on​ ​Disability​ ​at​ ​the​ ​DRC​ ​we​ ​define​ ​disability​ ​not​ ​as​ ​a​ ​personal  trait​ ​but​ ​as​ ​a​ ​mismatch​ ​between​ ​the​ ​needs​ ​of​ ​the​ ​individual​ ​and​ ​the​ ​service​ ​product​ ​or  environment​ ​offered.​ ​So​ ​in​ ​that​ ​sense​ ​we​ ​are​ ​all​ ​experiencing​ ​disability​ ​when​ ​the  system​ ​is​ ​not​ ​designed​ ​to​ ​match​ ​our​ ​needs.     I'm​ ​hoping​ ​that​ ​we​ ​can​ ​see​ ​disability​ ​as​ ​a​ ​design​ ​issue​ ​that​ ​can​ ​be​ ​addressed​ ​not​ ​as​ ​a  personal​ ​trait​ ​that​ ​creates​ ​an​ ​us​ ​them​ ​scenario.​ ​And​ ​so​ ​designing​ ​our​ ​artificial  intelligence​ ​in​ ​our​ ​HR​ ​systems​ ​and​ ​the​ ​way​ ​that​ ​recruit​ ​in​ ​our​ ​our​ ​workplace​ ​ultimately  to​ ​be​ ​more​ ​inclusive​ ​means​ ​that​ ​we're​ ​creating​ ​it​ ​to​ ​be​ ​better​ ​for​ ​all​ ​of​ ​us​ ​and​ ​to​ ​ensure  that​ ​fewer​ ​and​ ​fewer​ ​of​ ​us​ ​experience​ ​disability.    Workology​ ​Podcast​​ ​​|​ ​www.workologypodcast.com​ ​|​ ​@workology
  3. 3. Jessica:​ ​​[00:04:19]​ ​​This​ ​is​ ​fascinating​ ​to​ ​me​ ​because​ ​you​ ​have​ ​been​ ​working​ ​in​ ​this  area​ ​this​ ​field​ ​for​ ​25​ ​years.​ ​And​ ​I​ ​think​ ​long​ ​before​ ​maybe​ ​anyone​ ​had​ ​certainly​ ​thought  about​ ​artificial​ ​intelligence​ ​but​ ​really​ ​focused​ ​on​ ​inclusive​ ​technology​ ​and​ ​its​ ​use​ ​for​ ​a  very​ ​long​ ​time.    Jutta:​ ​​[00:04:35]​ ​​Yes.​ ​Actually​ ​25​ ​years​ ​ago​ ​I​ ​started​ ​the​ ​center​ ​but​ ​I​ ​started​ ​in​ ​this  when​ ​the​ ​apple​ ​two​ ​plus​ ​merged​ ​and​ ​Tandy​ ​Model​ ​100​ ​and​ ​all​ ​of​ ​those​ ​things​ ​that​ ​I'm  sure​ ​very​ ​few​ ​people​ ​remember.    Jessica:​ ​​[00:04:49]​ ​​Can​ ​you​ ​talk​ ​to​ ​our​ ​podcast​ ​listeners​ ​about​ ​your​ ​work​ ​in​ ​artificial  intelligence​ ​and​ ​disability​ ​inclusion?    Jutta:​ ​​[00:04:55]​ ​​Sure.​ ​I've​ ​been​ ​concerned​ ​about​ ​how​ ​diversity​ ​human​ ​variability​ ​and  especially​ ​outliers​ ​fare​ ​in​ ​quantified​ ​data​ ​systems​ ​for​ ​some​ ​time​ ​this​ ​predates​ ​our​ ​use  of​ ​data​ ​and​ ​big​ ​data​ ​analytics​ ​in​ ​artificial​ ​intelligence​ ​when​ ​we​ ​use​ ​or​ ​require​ ​big  numbers​ ​of​ ​homogenous​ ​outcomes​ ​to​ ​draw​ ​any​ ​conclusions.​ ​People​ ​with​ ​disabilities  are​ ​often​ ​the​ ​casualties.​ ​If​ ​you​ ​take​ ​say​ ​the​ ​needs​ ​and​ ​characteristics​ ​of​ ​any​ ​group​ ​of  people​ ​and​ ​plot​ ​them​ ​on​ ​a​ ​scatterplot​ ​you​ ​get​ ​something​ ​like​ ​a​ ​star.     First,​ ​it​ ​looks​ ​like​ ​an​ ​exploding​ ​star​ ​in​ ​the​ ​middle​ ​there​ ​will​ ​be​ ​a​ ​dense​ ​cluster​ ​and​ ​then  other​ ​deeds​ ​and​ ​characteristics​ ​will​ ​spread​ ​out​ ​toward​ ​the​ ​periphery.​ ​People​ ​with  disabilities​ ​are​ ​represented​ ​some​ ​distance​ ​from​ ​that​ ​dense​ ​core​ ​and​ ​if​ ​you​ ​look​ ​at​ ​the  needs​ ​and​ ​characteristics​ ​at​ ​the​ ​periphery​ ​or​ ​where​ ​people​ ​with​ ​disabilities​ ​are​ ​in​ ​that  starburst​ ​you​ ​will​ ​see​ ​that​ ​they​ ​are​ ​much​ ​further​ ​apart​ ​despite​ ​the​ ​fact​ ​that.     And​ ​this​ ​demonstrates​ ​that​ ​people​ ​with​ ​disabilities​ ​are​ ​more​ ​diverse​ ​than​ ​people​ ​in​ ​the  center​ ​but​ ​we​ ​tend​ ​to​ ​treat​ ​anyone​ ​beyond​ ​a​ ​certain​ ​invisible​ ​boundary​ ​away​ ​from​ ​the  dense​ ​core​ ​the​ ​same​ ​that​ ​huge​ ​variability​ ​between​ ​people​ ​with​ ​disabilities​ ​becomes​ ​a  problem​ ​because​ ​it​ ​means​ ​that​ ​you​ ​can​ ​never​ ​muster​ ​the​ ​numbers​ ​to​ ​be​ ​seen​ ​as  significant​ ​and​ ​quite​ ​quantify​ ​data​ ​systems​ ​even​ ​though​ ​the​ ​people​ ​we​ ​relegate​ ​to​ ​the  disability​ ​category​ ​are​ ​the​ ​world's​ ​largest​ ​minority.    Workology​ ​Podcast​​ ​​|​ ​www.workologypodcast.com​ ​|​ ​@workology
  4. 4. Jutta:​ ​​[00:06:27]​ ​​If​ ​not​ ​a​ ​majority​ ​as​ ​far​ ​as​ ​quantified​ ​data​ ​that​ ​requires​ ​statistical  power​ ​and​ ​that's​ ​what​ ​we've​ ​required​ ​in​ ​all​ ​of​ ​our​ ​data​ ​systems​ ​is​ ​concerned​ ​people  with​ ​disabilities​ ​are​ ​outliers​ ​and​ ​noise​ ​in​ ​the​ ​data​ ​set​ ​and​ ​what​ ​we​ ​do​ ​with​ ​noise​ ​and  with​ ​outliers​ ​is​ ​that​ ​we​ ​lemonade​ ​it​ ​when​ ​we​ ​clean​ ​the​ ​data​ ​or​ ​when​ ​we​ ​norm​ ​the​ ​data.  The​ ​difficulty​ ​with​ ​AI​ ​and​ ​why​ ​this​ ​becomes​ ​such​ ​a​ ​huge​ ​issue​ ​with​ ​AI​ ​is​ ​that​ ​AI​ ​bases  its​ ​decisions​ ​on​ ​this​ ​data​ ​and​ ​these​ ​are​ ​hugely​ ​important​ ​decisions​ ​that​ ​we​ ​don't  question​ ​because​ ​we​ ​think​ ​that​ ​artificial​ ​intelligence​ ​is​ ​not​ ​biased​ ​that​ ​is​ ​subjective​ ​that  it's​ ​not​ ​subject​ ​to​ ​human​ ​foibles.     So​ ​what​ ​happens​ ​is​ ​that​ ​AI​ ​AI​ ​does​ ​not​ ​recognize​ ​or​ ​understand​ ​people​ ​with​ ​disabilities  people​ ​with​ ​disabilities​ ​don't​ ​appear​ ​in​ ​or​ ​fit​ ​the​ ​models.​ ​Machines​ ​used​ ​to​ ​make  inferences​ ​and​ ​worse​ ​than​ ​that.​ ​I​ ​automates​ ​this​ ​bias​ ​against​ ​the​ ​outliers​ ​and​ ​amplifies  it.​ ​If​ ​AI​ ​is​ ​a​ ​black​ ​box​ ​we​ ​can​ ​we​ ​can't​ ​challenge​ ​the​ ​ai​ ​ai​ ​decisions.​ ​And​ ​so​ ​what​ ​is  happening​ ​is​ ​that​ ​people​ ​with​ ​disabilities​ ​are​ ​being​ ​impacted​ ​in​ ​quite​ ​a​ ​number​ ​of  pernicious​ ​ways.​ ​One​ ​of​ ​your​ ​top​ ​you​ ​we're​ ​talking​ ​about​ ​H.R.​ ​and​ ​especially  competitive​ ​HR​ ​decisions​ ​we​ ​use​ ​AI​ ​to​ ​filter​ ​the​ ​applications​ ​to​ ​determine​ ​who's​ ​going  to​ ​be​ ​the​ ​most​ ​promising​ ​candidate​ ​who's​ ​going​ ​to​ ​get​ ​an​ ​interview.    Jutta:​ ​​[00:08:04]​ ​​And​ ​if​ ​you're​ ​not​ ​part​ ​of​ ​the​ ​data​ ​set​ ​there's​ ​no​ ​data​ ​about​ ​your  positive​ ​performance​ ​and​ ​so​ ​someone​ ​with​ ​a​ ​disability​ ​who​ ​is​ ​an​ ​outlier​ ​who​ ​isn't​ ​part  of​ ​the​ ​model​ ​won't​ ​be​ ​selected.​ ​It​ ​however​ ​also​ ​permeates​ ​into​ ​other​ ​areas​ ​of​ ​our​ ​life  loans​ ​and​ ​credit​ ​insurance.​ ​If​ ​your​ ​asset​ ​portfolio​ ​or​ ​profile​ ​is​ ​not​ ​something​ ​the  machine​ ​is​ ​used​ ​to​ ​it​ ​won't​ ​determine​ ​that​ ​you​ ​are​ ​a​ ​good​ ​loan​ ​risk​ ​and​ ​if​ ​you​ ​have​ ​an  anomalous​ ​medical​ ​history​ ​then​ ​insurance​ ​will​ ​not​ ​be​ ​something​ ​that​ ​will​ ​be​ ​decided​ ​to  be​ ​a​ ​low​ ​risk​ ​if​ ​there​ ​is​ ​a​ ​security​ ​assessment​ ​or​ ​someone​ ​is​ ​determining​ ​sentencing  predicting​ ​recidivism.     Flagging​ ​security​ ​risks​ ​because​ ​you​ ​are​ ​unknown​ ​and​ ​something​ ​that​ ​is​ ​not​ ​part​ ​of  what​ ​is​ ​part​ ​of​ ​the​ ​model​ ​or​ ​what​ ​is​ ​understood​ ​by​ ​the​ ​machine​ ​then​ ​you're​ ​more​ ​likely  to​ ​get​ ​flagged.​ ​And​ ​so​ ​what​ ​you​ ​have​ ​is​ ​a​ ​vicious​ ​cycle.​ ​You​ ​are​ ​not​ ​part​ ​of​ ​the​ ​data  set​ ​you're​ ​not​ ​understood.​ ​You're​ ​not​ ​therefore​ ​going​ ​to​ ​become​ ​part​ ​of​ ​the​ ​data​ ​set.  So​ ​it's​ ​not​ ​a​ ​case​ ​of​ ​let's​ ​just​ ​add​ ​more​ ​data​ ​because​ ​the​ ​problem​ ​is​ ​that​ ​all​ ​of​ ​that  data​ ​is​ ​eliminating​ ​you.​ ​And​ ​so​ ​you'll​ ​never​ ​get​ ​an​ ​opportunity​ ​to​ ​be​ ​part​ ​of​ ​the​ ​data  Workology​ ​Podcast​​ ​​|​ ​www.workologypodcast.com​ ​|​ ​@workology
  5. 5. set.    Jessica:​ ​​[00:09:29]​ ​​This​ ​is​ ​fascinating​ ​because​ ​I​ ​feel​ ​like​ ​the​ ​sell​ ​in​ ​human​ ​resources  and​ ​recruiting​ ​industry​ ​for​ ​artificial​ ​intelligence​ ​is​ ​that​ ​it's​ ​going​ ​to​ ​remove​ ​bias​ ​and​ ​help  make​ ​better​ ​more​ ​consistent​ ​hiring​ ​decisions.​ ​But​ ​you're​ ​saying​ ​that​ ​it's​ ​removing  eliminating​ ​an​ ​important​ ​group​ ​of​ ​people​ ​from​ ​even​ ​being​ ​considered​ ​for​ ​hire​ ​or  promotion​ ​or​ ​any​ ​of​ ​these​ ​things​ ​related​ ​to​ ​the​ ​workplace.    Jutta:​ ​​[00:09:59]​ ​​Right.​ ​Because​ ​it's​ ​based​ ​on​ ​past​ ​data.​ ​So​ ​it​ ​will​ ​perpetuate​ ​what​ ​has  happened​ ​in​ ​the​ ​past.​ ​The​ ​evidence​ ​that​ ​someone​ ​has​ ​done​ ​well​ ​within​ ​a​ ​job​ ​will​ ​be  based​ ​upon​ ​past​ ​data​ ​and​ ​the​ ​anomalous​ ​data.​ ​Most​ ​data​ ​brokerages​ ​will​ ​eliminate​ ​the  anomalous​ ​state​ ​or​ ​they're​ ​trying​ ​to​ ​find​ ​the​ ​dominant​ ​patterns​ ​the​ ​strongest​ ​largest  group​ ​so​ ​that​ ​it​ ​can​ ​be​ ​a​ ​certain​ ​decision​ ​that​ ​is​ ​being​ ​made.​ ​And​ ​so​ ​in​ ​that​ ​way​ ​it​ ​is  eliminating​ ​what​ ​is​ ​seen​ ​as​ ​noise​ ​but​ ​in​ ​fact​ ​it's​ ​the​ ​individuals​ ​that​ ​are​ ​at​ ​the​ ​periphery  of​ ​that​ ​scatterplot​ ​I​ ​was​ ​talking​ ​about​ ​or​ ​people​ ​who​ ​are​ ​in​ ​small​ ​minorities.    Jessica:​ ​​[00:10:47]​ ​​So​ ​one​ ​of​ ​the​ ​areas​ ​that​ ​we've​ ​talked​ ​about​ ​throughout​ ​this​ ​Future  of​ ​Work​ ​podcast​ ​series​ ​with​ ​PEAT​ ​is​ ​a​ ​look​ ​at​ ​the​ ​gig​ ​economy​ ​and​ ​some​ ​of​ ​these​ ​web  based​ ​platforms.​ ​And​ ​there's​ ​a​ ​lot​ ​of​ ​different​ ​platforms​ ​in​ ​human​ ​resources​ ​and  recruiting​ ​as​ ​well​ ​as​ ​the​ ​gig​ ​economy​ ​that​ ​that​ ​use​ ​AI​ ​and​ ​machine​ ​learning  components.​ ​How​ ​can​ ​these​ ​technologies​ ​do​ ​better​ ​at​ ​incorporating​ ​all​ ​different​ ​types  of​ ​individuals​ ​outside​ ​of​ ​that​ ​scatterplot​ ​so​ ​that​ ​they​ ​aren't​ ​impacting​ ​those​ ​who​ ​are  different​ ​and​ ​were​ ​failing​ ​to​ ​bring​ ​those​ ​people​ ​into​ ​the​ ​community​ ​or​ ​giving​ ​them  opportunities.    Jutta:​ ​​[00:11:26]​ ​​So​ ​platforms​ ​and​ ​the​ ​flexible​ ​economy​ ​the​ ​sharing​ ​economy​ ​holds​ ​a  lot​ ​of​ ​promise​ ​for​ ​people​ ​who​ ​previously​ ​face​ ​barriers​ ​to​ ​employment​ ​or​ ​who​ ​have  difficulty​ ​participating​ ​in​ ​traditional​ ​employment​ ​and​ ​they​ ​hold​ ​a​ ​great​ ​deal​ ​of​ ​promise  for​ ​people​ ​with​ ​disabilities​ ​when​ ​you​ ​are​ ​an​ ​outlier​ ​it​ ​is​ ​hard​ ​to​ ​find​ ​someone​ ​nearby  with​ ​the​ ​same​ ​d​ ​that​ ​you​ ​have​ ​your​ ​subject​ ​to.​ ​As​ ​I​ ​was​ ​mentioning​ ​these​ ​vicious  economic​ ​cycles​ ​products​ ​are​ ​not​ ​made​ ​for​ ​you​ ​if​ ​they​ ​are​ ​they​ ​cost​ ​more.​ ​Meaning  you're​ ​scarce​ ​dollars​ ​worth​ ​less​ ​education.​ ​It's​ ​not​ ​optimized​ ​for​ ​you.​ ​Work  opportunities​ ​don't​ ​recognize​ ​your​ ​potential.​ ​If​ ​you're​ ​lucky​ ​enough​ ​to​ ​get​ ​a​ ​position  Workology​ ​Podcast​​ ​​|​ ​www.workologypodcast.com​ ​|​ ​@workology
  6. 6. the​ ​tools​ ​you​ ​need​ ​to​ ​do​ ​your​ ​work​ ​won't​ ​be​ ​accessible​ ​meaning​ ​you​ ​can't  demonstrate​ ​your​ ​optimal​ ​performance​ ​so​ ​that​ ​someone​ ​who​ ​is​ ​has​ ​a​ ​disability​ ​and  faces​ ​barriers​ ​to​ ​employment​ ​in​ ​the​ ​current​ ​job​ ​market.     There's​ ​many​ ​things​ ​that​ ​you're​ ​needing​ ​to​ ​battle.​ ​Platforms​ ​are​ ​a​ ​potential​ ​way​ ​to  support​ ​people​ ​in​ ​recognizing​ ​their​ ​diverse​ ​needs​ ​and​ ​thereby​ ​diversifying​ ​the​ ​demand  as​ ​well​ ​which​ ​can​ ​then​ ​help​ ​to​ ​trigger​ ​a​ ​diversification​ ​of​ ​production​ ​and​ ​supply.​ ​The  more​ ​we​ ​push​ ​both​ ​the​ ​demand​ ​and​ ​the​ ​supply​ ​out​ ​to​ ​those​ ​edges​ ​the​ ​better​ ​it​ ​is​ ​for  inclusion​ ​and​ ​for​ ​people​ ​that​ ​are​ ​outliers​ ​and​ ​it​ ​makes​ ​it​ ​it​ ​provides​ ​more​ ​choices​ ​for  everybody​ ​and​ ​when​ ​there​ ​are​ ​more​ ​choices​ ​than​ ​you​ ​can​ ​find​ ​choices​ ​that​ ​fit​ ​you  whether​ ​it's​ ​a​ ​product​ ​or​ ​whether​ ​it's​ ​a​ ​job​ ​or​ ​whether​ ​it's​ ​a​ ​service.     So​ ​platforms​ ​can​ ​reduce​ ​fragmentation​ ​allowing​ ​lú​ ​the​ ​sharing​ ​and​ ​pooling​ ​of  resources​ ​which​ ​makes​ ​it​ ​easier​ ​to​ ​address​ ​the​ ​requirements​ ​of​ ​individuals​ ​who​ ​can  benefit​ ​from​ ​economies​ ​of​ ​scale.​ ​They​ ​also​ ​provide​ ​feedback​ ​loops​ ​to​ ​continuously  refine​ ​available​ ​resources​ ​and​ ​not​ ​only​ ​can​ ​tools​ ​and​ ​resources​ ​be​ ​shared​ ​but​ ​also​ ​the  building​ ​blocks​ ​are​ ​development​ ​tools​ ​needed​ ​to​ ​create​ ​things​ ​like​ ​inclusive​ ​product  support​ ​training​ ​of​ ​people​ ​that​ ​face​ ​barriers​ ​to​ ​employment​ ​or​ ​to​ ​address​ ​gaps  however​ ​there.    Jutta:​ ​​[00:13:45]​ ​​So​ ​I've​ ​been​ ​a​ ​great​ ​proponent​ ​of​ ​platforms​ ​for​ ​those​ ​reasons.​ ​But  some​ ​of​ ​the​ ​platforms​ ​are​ ​largely​ ​extractive​ ​platforms.​ ​The​ ​value​ ​comes​ ​from​ ​the  workers​ ​but​ ​the​ ​workers​ ​don't​ ​govern​ ​the​ ​platform​ ​nor​ ​do​ ​they​ ​receive​ ​the​ ​profit.  Because​ ​the​ ​focus​ ​is​ ​on​ ​short​ ​term​ ​competitive​ ​wealth​ ​production​ ​for​ ​the​ ​owners.  These​ ​platforms​ ​are​ ​less​ ​likely​ ​to​ ​invest​ ​in​ ​diversification.​ ​Most​ ​of​ ​the​ ​AI​ ​products​ ​are  used​ ​to​ ​have​ ​a​ ​quick​ ​win.​ ​Not​ ​only​ ​will​ ​people​ ​with​ ​disabilities​ ​fare​ ​badly​ ​in​ ​the  predictive​ ​analytics.     But​ ​as​ ​I​ ​was​ ​mentioning​ ​if​ ​you​ ​are​ ​not​ ​dissipating​ ​there​ ​won't​ ​be​ ​data​ ​that​ ​proves​ ​your  successful​ ​performance​ ​and​ ​the​ ​AI​ ​that​ ​is​ ​frequently​ ​used​ ​in​ ​these​ ​systems​ ​is​ ​to​ ​find  the​ ​quickest​ ​way​ ​to​ ​address​ ​an​ ​immediate​ ​demand.​ ​There​ ​is​ ​an​ ​alternative​ ​and​ ​that​ ​is  there​ ​are​ ​there​ ​there's​ ​a​ ​platform​ ​co-op​ ​movements​ ​and​ ​they​ ​are​ ​governed​ ​by​ ​workers  and​ ​the​ ​workers​ ​share​ ​the​ ​profit.​ ​They​ ​are​ ​motivated​ ​to​ ​create​ ​a​ ​thriving​ ​diverse  Workology​ ​Podcast​​ ​​|​ ​www.workologypodcast.com​ ​|​ ​@workology
  7. 7. community.​ ​If​ ​you're​ ​interested​ ​in​ ​the​ ​greater​ ​social​ ​good​ ​it​ ​pays​ ​to​ ​be​ ​as​ ​inclusive​ ​as  possible.​ ​And​ ​so​ ​there​ ​there​ ​is​ ​an​ ​evolution​ ​of​ ​some​ ​of​ ​these​ ​platforms​ ​and​ ​there​ ​are  these​ ​emergent​ ​platforms​ ​that​ ​are​ ​not​ ​looking​ ​at​ ​data​ ​to​ ​support​ ​the​ ​immediate​ ​quick  win​ ​but​ ​data​ ​to​ ​support​ ​a​ ​thriving​ ​community​ ​with​ ​a​ ​diversification​ ​of​ ​jobs​ ​and​ ​better  benefits​ ​for​ ​the​ ​workers​ ​but​ ​also​ ​for​ ​the​ ​employers​ ​and​ ​for​ ​the​ ​consumers​ ​of​ ​the  services​ ​at​ ​the​ ​inclusive​ ​Research​ ​Center.    Jessica:​ ​​[00:15:40]​ ​​Let's​ ​talk​ ​a​ ​little​ ​bit​ ​of​ ​a​ ​reset.​ ​This​ ​is​ ​Jessica​ ​Miller-Merrell.​ ​You're  listening​ ​to​ ​the​ ​Workology​ ​Podcast​ ​in​ ​partnership​ ​with​ ​PEAT.​ ​.​ ​Today​ ​we​ ​are​ ​talking  about​ ​machine​ ​learning​ ​and​ ​inclusion​ ​with​ ​Jutta​ ​Treviranus.​ ​You​ ​can​ ​connect​ ​with​ ​her  on​ ​Twitter​ ​@juttatrevira.     Sponsor​ ​tag:​ ​​[00:15:40]​ ​​Future​ ​of​ ​Work​ ​series​ ​is​ ​supported​ ​by​ ​PEAT​ ​the​ ​partnership  on​ ​employment​ ​and​ ​accessible​ ​technology.​ ​PEAT's​ ​initiative​ ​is​ ​to​ ​foster​ ​collaboration  and​ ​action​ ​around​ ​accessible​ ​technology​ ​in​ ​the​ ​workplace.​ ​PEAT​ ​is​ ​funded​ ​by​ ​the​ ​U.S.  Department​ ​of​ ​Labor's​ ​office​ ​of​ ​disability​ ​employment​ ​policy​ ​ODEP​ ​learn​ ​more​ ​about  PEAT​ ​at​ ​PEATworks.org.     Jessica:​ ​​[00:15:40]​ ​​What​ ​suggestions​ ​do​ ​you​ ​have​ ​for​ ​podcast​ ​listeners​ ​maybe​ ​who  are​ ​thinking​ ​about​ ​adding​ ​artificial​ ​intelligence​ ​technology​ ​to​ ​to​ ​their​ ​human​ ​resources  are​ ​creating​ ​our​ ​workplace​ ​sort​ ​of​ ​technology​ ​stack​ ​so​ ​that​ ​maybe​ ​they​ ​select​ ​the​ ​right  one​ ​or​ ​maybe​ ​select​ ​one​ ​that​ ​is​ ​more​ ​inclusive​ ​than​ ​the​ ​other.You​ ​focus​ ​on​ ​three  dimensions​ ​of​ ​inclusive​ ​design.​ ​Can​ ​you​ ​walk​ ​us​ ​through​ ​these​ ​and​ ​maybe​ ​talk​ ​about  how​ ​they​ ​apply​ ​to​ ​machine​ ​learning​ ​and​ ​AI​ ​technologies.    Jutta:​ ​​[00:15:50]​ ​​Sure.​ ​So​ ​Universal​ ​Design​ ​has​ ​a​ ​set​ ​of​ ​principles​ ​and​ ​accessibility​ ​has  a​ ​checklist.​ ​And​ ​one​ ​of​ ​the​ ​things​ ​that​ ​I​ ​was​ ​encouraged​ ​to​ ​do​ ​was​ ​to​ ​come​ ​up​ ​with​ ​a  set​ ​of​ ​principles​ ​for​ ​inclusive​ ​design​ ​but​ ​inclusive​ ​design​ ​is​ ​intended​ ​to​ ​be​ ​relative​ ​to  the​ ​individual.​ ​So​ ​it's​ ​not​ ​a​ ​one​ ​size​ ​fits​ ​all​ ​approach​ ​it's​ ​a​ ​one​ ​size​ ​fits​ ​one​ ​approach  because​ ​we​ ​grew​ ​up​ ​in​ ​the​ ​digital​ ​domain​ ​and​ ​the​ ​digital​ ​can​ ​be​ ​adaptive.​ ​Unlike​ ​a  building​ ​where​ ​you​ ​have​ ​to​ ​have​ ​the​ ​entrance​ ​work​ ​for​ ​everyone​ ​that​ ​might​ ​approach  the​ ​building​ ​on​ ​a​ ​digital​ ​system​ ​can​ ​morphin​ ​adapt​ ​and​ ​present​ ​a​ ​different​ ​design​ ​to  each​ ​one​ ​that​ ​that​ ​visits​ ​that​ ​digital​ ​says.   Workology​ ​Podcast​​ ​​|​ ​www.workologypodcast.com​ ​|​ ​@workology
  8. 8.   So​ ​instead​ ​of​ ​a​ ​set​ ​set​ ​of​ ​principles​ ​I​ ​devised​ ​the​ ​three​ ​dimensions​ ​and​ ​the​ ​three  dimensions​ ​are​ ​the​ ​first​ ​dimension​ ​is​ ​that​ ​recognize​ ​that​ ​everybody​ ​unique​ ​and​ ​help  people​ ​to​ ​understand​ ​their​ ​own​ ​uniqueness​ ​and​ ​create​ ​systems​ ​that​ ​fit​ ​that​ ​unique  diversity​ ​of​ ​requirements​ ​or​ ​one​ ​size​ ​fits​ ​one​ ​the​ ​the​ ​second​ ​dimension​ ​is​ ​ensure​ ​that  there​ ​is​ ​an​ ​inclusive​ ​process.​ ​This​ ​means​ ​designing​ ​the​ ​tables​ ​so​ ​that​ ​everyone​ ​can  participate​ ​in​ ​the​ ​decision​ ​making​ ​and​ ​in​ ​the​ ​design​ ​because​ ​we​ ​all​ ​benefit​ ​from  diverse​ ​perspectives.     We​ ​have​ ​far​ ​better​ ​planning​ ​better​ ​prediction​ ​and​ ​much​ ​greater​ ​creativity​ ​if​ ​we​ ​have​ ​a  diversity​ ​of​ ​perspectives​ ​participating​ ​in​ ​the​ ​design​ ​and​ ​what​ ​you​ ​want​ ​in​ ​inclusive  design​ ​as​ ​co-designer​ ​so​ ​authentic​ ​code​ ​design​ ​where​ ​the​ ​individual​ ​that​ ​the​ ​design​ ​is  intended​ ​to​ ​be​ ​used​ ​by​ ​is​ ​part​ ​of​ ​the​ ​design​ ​process​ ​and​ ​then​ ​the​ ​third​ ​dimension​ ​is  recognize​ ​that​ ​we're​ ​in​ ​a​ ​complex​ ​adaptive​ ​system.​ ​No​ ​design​ ​decision​ ​is​ ​made​ ​in  isolation.​ ​It​ ​reverberates​ ​out​ ​to​ ​all​ ​of​ ​the​ ​connected​ ​systems​ ​that​ ​are​ ​in​ ​the​ ​context​ ​of  the​ ​design.​ ​So​ ​create​ ​a​ ​design​ ​that​ ​benefits​ ​everyone​ ​and​ ​create​ ​virtuous​ ​not​ ​vicious  cycles.    Jessica:​ ​​[00:18:17]​ ​​I​ ​feel​ ​like​ ​all​ ​of​ ​these​ ​could​ ​be​ ​extremely​ ​helpful​ ​in​ ​many​ ​of​ ​these  artificial​ ​intelligence​ ​machine​ ​learning​ ​companies​ ​that​ ​are​ ​building​ ​these​ ​platforms​ ​or  anyone​ ​in​ ​really​ ​in​ ​technology​ ​to​ ​consider​ ​when​ ​when​ ​they​ ​are​ ​trying​ ​to​ ​to​ ​create​ ​a  community​ ​or​ ​a​ ​technology​ ​that's​ ​inclusive​ ​to​ ​all.    Jutta:​ ​​[00:18:41]​ ​​Yes​ ​definitely.​ ​Yeah.​ ​The​ ​Actually​ ​when​ ​we​ ​take​ ​it​ ​inclusive​ ​design  approach​ ​to​ ​AI​ ​AI​ ​one​ ​of​ ​the​ ​things​ ​that​ ​that​ ​we​ ​frequently​ ​do​ ​with​ ​it​ ​in​ ​our​ ​inclusive  design​ ​sessions​ ​or​ ​one​ ​activity​ ​that​ ​I​ ​often​ ​do​ ​is​ ​an​ ​activity​ ​called​ ​the​ ​grandparent  grandchild​ ​conversation.​ ​I​ ​don't​ ​know​ ​if​ ​you​ ​have​ ​a​ ​toddler​ ​or​ ​if​ ​you​ ​don't​ ​have​ ​a  toddler​ ​there's​ ​that​ ​continuously​ ​the​ ​unpacking​ ​of​ ​why​ ​are​ ​we​ ​doing​ ​this.     And​ ​that​ ​is​ ​a​ ​little​ ​bit​ ​of​ ​a​ ​characterization​ ​of​ ​what​ ​happened​ ​when​ ​I​ ​started​ ​to​ ​look​ ​at  the​ ​inclusive​ ​design​ ​of​ ​artificial​ ​intelligence​ ​because​ ​it​ ​prompted​ ​us​ ​to​ ​unpack​ ​not​ ​just  what​ ​is​ ​happening​ ​with​ ​in​ ​a​ ​eye​ ​but​ ​also​ ​being​ ​part​ ​of​ ​an​ ​academic's​ ​situation.​ ​It  prompted​ ​us​ ​to​ ​think​ ​about​ ​how​ ​are​ ​we​ ​dealing​ ​with​ ​evidence​ ​how​ ​do​ ​we​ ​determine  Workology​ ​Podcast​​ ​​|​ ​www.workologypodcast.com​ ​|​ ​@workology
  9. 9. truth.​ ​Are​ ​our​ ​research​ ​methods​ ​that​ ​we're​ ​using​ ​research​ ​methods​ ​that​ ​are​ ​supportive  of​ ​diversity​ ​and​ ​understanding​ ​of​ ​diversity.     And​ ​you​ ​can​ ​trace​ ​the​ ​the​ ​history​ ​of​ ​where​ ​we​ ​ended​ ​up​ ​with​ ​a​ ​I​ ​and​ ​with​ ​machine  learning​ ​and​ ​how​ ​we're​ ​teaching​ ​our​ ​machines.​ ​It​ ​traces​ ​right​ ​back​ ​to​ ​statistical  research​ ​and​ ​how​ ​we​ ​are​ ​determining​ ​what​ ​is​ ​true​ ​and​ ​what​ ​is​ ​statistically​ ​significant​ ​or  what​ ​has​ ​statistical​ ​power​ ​within​ ​all​ ​of​ ​our​ ​research​ ​and​ ​academia​ ​the​ ​in​ ​terms​ ​of  applying​ ​those​ ​three​ ​dimensions.​ ​Diversity​ ​supportive​ ​data​ ​and​ ​evidence​ ​is​ ​something  that​ ​has​ ​benefit​ ​for​ ​all.​ ​We​ ​have​ ​been​ ​exploring​ ​something​ ​called​ ​small​ ​thick​ ​bottom​ ​up  data​ ​that​ ​understands​ ​the​ ​outliers.     Making​ ​ourselves​ ​smarter​ ​not​ ​just​ ​machines​ ​smarter​ ​as​ ​well.​ ​Small​ ​data​ ​is​ ​is​ ​also  called​ ​and​ ​equals​ ​me​ ​data​ ​or​ ​and​ ​equals​ ​one​ ​Data​ ​think​ ​data​ ​is​ ​looking​ ​at​ ​data​ ​in  context.​ ​The​ ​process​ ​that​ ​we​ ​try​ ​to​ ​encourage​ ​is​ ​transparent​ ​parent​ ​purchase​ ​the  parent​ ​patrie​ ​AI​ ​code​ ​design​ ​inclusive​ ​process​ ​and​ ​in​ ​adding​ ​diverse​ ​perspectives.    Jutta:​ ​​[00:21:16]​ ​​And​ ​the​ ​third​ ​dimension​ ​understanding​ ​the​ ​complex​ ​adaptive​ ​system  that​ ​we​ ​are​ ​in​ ​if​ ​we​ ​create​ ​machines​ ​that​ ​understand​ ​and​ ​recognize​ ​diversity​ ​and​ ​have  inclusion​ ​embedded​ ​in​ ​their​ ​rules​ ​and​ ​models​ ​it​ ​will​ ​definitely​ ​benefit​ ​everyone​ ​in​ ​all​ ​of  the​ ​experiments​ ​that​ ​we've​ ​done​ ​so​ ​far​ ​show​ ​that​ ​this​ ​is​ ​something​ ​that's​ ​critical​ ​not  just​ ​for​ ​people​ ​with​ ​disabilities​ ​but​ ​for​ ​all​ ​of​ ​us.​ ​The​ ​goals​ ​that​ ​we​ ​have​ ​for​ ​artificial  intelligence​ ​are​ ​better​ ​match​ ​when​ ​we​ ​don't​ ​ignore​ ​the​ ​outliers​ ​when​ ​in​ ​fact​ ​we​ ​are​ ​a  diversity​ ​friendly​ ​in​ ​our​ ​knowledge​ ​and​ ​understanding.     The​ ​really​ ​interesting​ ​and​ ​this​ ​is​ ​not​ ​part​ ​of​ ​your​ ​question​ ​but​ ​the​ ​really​ ​interesting​ ​thing  that​ ​I​ ​found​ ​is​ ​that​ ​previously​ ​when​ ​we​ ​talked​ ​about​ ​stereotypes​ ​or​ ​bias​ ​or​ ​prejudice​ ​in  conversations​ ​with​ ​hard​ ​scientists​ ​or​ ​with​ ​our​ ​fellow​ ​academia​ ​and​ ​it​ ​was​ ​seen​ ​as​ ​a  soft​ ​science​ ​as​ ​something​ ​that​ ​was​ ​not​ ​easily​ ​verifiable​ ​and​ ​therefore​ ​in​ ​a​ ​sort​ ​of  hierarchy​ ​of​ ​academic​ ​rigor​ ​it​ ​wasn't​ ​seen​ ​as​ ​something​ ​that​ ​was​ ​as​ ​well​ ​respected.  And​ ​but​ ​now​ ​that​ ​we​ ​can​ ​actually​ ​manifest​ ​the​ ​issues​ ​within​ ​artificial​ ​intelligence​ ​we  can​ ​show​ ​that​ ​look​ ​at​ ​if​ ​you​ ​don't​ ​include​ ​the​ ​state​ ​here​ ​or​ ​if​ ​you​ ​use​ ​only​ ​the​ ​dominant  patterns​ ​and​ ​you​ ​ignore​ ​the​ ​full​ ​diversity​ ​if​ ​you​ ​base​ ​all​ ​of​ ​your​ ​knowledge​ ​and  assumption​ ​on​ ​homogenous​ ​data​ ​or​ ​the​ ​same​ ​effect​ ​in​ ​one​ ​large​ ​data​ ​group​ ​then  Workology​ ​Podcast​​ ​​|​ ​www.workologypodcast.com​ ​|​ ​@workology
  10. 10. you're​ ​not​ ​going​ ​to​ ​do​ ​all​ ​of​ ​the​ ​things​ ​that​ ​you​ ​hope​ ​to​ ​achieve.​ ​Whether​ ​it's  innovation​ ​or​ ​whether​ ​it's​ ​risk​ ​detection​ ​or​ ​whether​ ​it's​ ​better​ ​prediction​ ​or​ ​planning.    Jessica:​ ​​[00:23:14]​ ​​I​ ​felt​ ​like​ ​that​ ​last​ ​piece​ ​is​ ​I​ ​think​ ​something​ ​that​ ​employers​ ​or  businesses​ ​can​ ​can​ ​really​ ​take​ ​advantage​ ​and​ ​think​ ​about​ ​right.​ ​What​ ​happens​ ​when  you​ ​aren't​ ​including​ ​all​ ​people​ ​from​ ​different​ ​backgrounds​ ​education​ ​levels​ ​interests.  And​ ​how​ ​does​ ​that​ ​impact​ ​creativity​ ​the​ ​success​ ​of​ ​your​ ​organization​ ​moving​ ​forward  with​ ​a​ ​project​ ​now.​ ​You​ ​have​ ​some​ ​data​ ​maybe​ ​some​ ​research​ ​to​ ​support​ ​the​ ​case​ ​for  diversity​ ​work​ ​itself​ ​is​ ​changing​ ​quite​ ​significantly.    Jutta:​ ​​[00:23:48]​ ​​We​ ​still​ ​have​ ​systems​ ​and​ ​understandings​ ​that​ ​come​ ​from​ ​the  industrial​ ​age​ ​when​ ​we​ ​need​ ​a​ ​direct​ ​replicate​ ​able​ ​workers.​ ​And​ ​that's​ ​really​ ​not​ ​the  reality​ ​at​ ​the​ ​moment.​ ​If​ ​a​ ​organization​ ​wishes​ ​to​ ​survive​ ​in​ ​the​ ​current​ ​reality​ ​then​ ​what  you​ ​need​ ​is​ ​you​ ​need​ ​an​ ​agile​ ​diverse​ ​collaborative​ ​team​ ​and​ ​we've​ ​set​ ​up​ ​our​ ​systems  and​ ​the​ ​data​ ​that​ ​we're​ ​teaching​ ​those​ ​systems​ ​information​ ​and​ ​rules​ ​and​ ​algorithms  from​ ​another​ ​era​ ​the​ ​best​ ​way​ ​to​ ​effect​ ​the​ ​cultural​ ​change​ ​that​ ​we​ ​need​ ​within​ ​work​ ​is  to​ ​create​ ​AI​ ​systems​ ​that​ ​are​ ​diversity​ ​supportive​ ​that​ ​doesn’t​ ​rid​ ​us​ ​of​ ​those​ ​that  outlying​ ​information​ ​and​ ​that​ ​don't​ ​ignore​ ​people​ ​who​ ​are​ ​at​ ​the​ ​periphery​ ​and​ ​who  offer​ ​an​ ​alternative​ ​or​ ​greater​ ​variability​ ​of​ ​skills​ ​and​ ​understandings​ ​and​ ​perspectives.    Jutta:​ ​​[00:25:54]​ ​​So​ ​the​ ​question​ ​was​ ​what​ ​advice​ ​to​ ​give.​ ​In​ ​choosing​ ​the​ ​AI​ ​and  machine​ ​learning​ ​programs​ ​like​ ​if​ ​somebody​ ​was​ ​purchasing​ ​it​ ​is​ ​that.    Jessica:​ ​​[00:26:03]​ ​​Yes​ ​so​ ​let​ ​me​ ​give​ ​you​ ​an​ ​example.​ ​There​ ​isn't​ ​an​ ​artificial  intelligence​ ​technology​ ​it's​ ​a​ ​video​ ​technology.​ ​And​ ​they​ ​use​ ​AI​ ​to​ ​measure  truthfulness​ ​and​ ​your​ ​body​ ​language​ ​to​ ​determine​ ​if​ ​you're​ ​happy​ ​angry​ ​sad​ ​mad​ ​if​ ​you  are​ ​what​ ​type​ ​of​ ​personality​ ​are​ ​so​ ​HR​ ​and​ ​recruiting​ ​leaders​ ​are​ ​seeing​ ​a​ ​lot​ ​of  different​ ​types​ ​of​ ​technology​ ​that​ ​whether​ ​it's​ ​video​ ​or​ ​sourcing​ ​technology​ ​that​ ​will  pull​ ​the​ ​best​ ​candidates​ ​to​ ​the​ ​front​ ​lines​ ​and​ ​score​ ​them​ ​and​ ​present​ ​them​ ​to​ ​an  employer.     What​ ​kind​ ​of​ ​things​ ​should​ ​they​ ​be​ ​thinking​ ​about​ ​when​ ​they're​ ​looking​ ​at​ ​these​ ​these  technologies.​ ​Because​ ​all​ ​of​ ​them​ ​are​ ​not​ ​created​ ​equal​ ​and​ ​they're​ ​certainly​ ​not​ ​all  Workology​ ​Podcast​​ ​​|​ ​www.workologypodcast.com​ ​|​ ​@workology
  11. 11. likely​ ​thinking​ ​about​ ​inclusive​ ​design.    Jutta:​ ​​[00:26:55]​ ​​The​ ​first​ ​thing​ ​that​ ​I​ ​would​ ​recommend​ ​is​ ​that​ ​you​ ​choose​ ​a​ ​system  where​ ​it's​ ​apparent​ ​what​ ​the​ ​rules​ ​are​ ​and​ ​you​ ​know​ ​what​ ​set​ ​of​ ​data​ ​the​ ​system​ ​is  trained​ ​on.​ ​Is​ ​the​ ​system​ ​trained​ ​on​ ​a​ ​full​ ​diversity​ ​of​ ​the​ ​types​ ​of​ ​employees​ ​that​ ​you  hoped​ ​to​ ​choose.​ ​Or​ ​is​ ​it​ ​trained​ ​on​ ​a​ ​homogenous​ ​data​ ​set​ ​that​ ​has​ ​been​ ​cleaned​ ​of  edge​ ​scenarios.​ ​The​ ​other​ ​thing​ ​that​ ​I​ ​would​ ​encourage​ ​you​ ​to​ ​do​ ​is​ ​to​ ​find​ ​a​ ​system  where​ ​there​ ​is​ ​the​ ​opportunity​ ​for​ ​a​ ​feedback​ ​loop​ ​where​ ​new​ ​data​ ​can​ ​be​ ​added  where​ ​you​ ​can​ ​add​ ​additional​ ​filters​ ​or​ ​at​ ​least​ ​query​ ​the​ ​filters​ ​and​ ​remove​ ​filters​ ​that  cause​ ​that​ ​may​ ​cause​ ​bias.     The​ ​other​ ​thing​ ​that​ ​I​ ​would​ ​do​ ​is​ ​to​ ​choose​ ​a​ ​system​ ​that​ ​is​ ​reactive​ ​or​ ​that​ ​is  responsive​ ​to​ ​the​ ​scenario​ ​that​ ​you​ ​that​ ​you​ ​are​ ​working​ ​in.​ ​So​ ​not​ ​something​ ​that​ ​only  gives​ ​you​ ​generic​ ​data​ ​but​ ​that​ ​allows​ ​you​ ​to​ ​continuously​ ​adapt​ ​the​ ​system​ ​to​ ​fine  tune​ ​the​ ​working​ ​environment​ ​and​ ​the​ ​requirements​ ​that​ ​you​ ​have.     And​ ​then​ ​lastly​ ​I​ ​would​ ​encourage​ ​you​ ​to​ ​get​ ​a​ ​system​ ​that​ ​allows​ ​you​ ​to​ ​do​ ​some  predictive​ ​modeling​ ​so​ ​scenarios​ ​that​ ​may​ ​not​ ​have​ ​been​ ​found​ ​within​ ​the​ ​current​ ​data  set​ ​because​ ​the​ ​the​ ​one​ ​thing​ ​as​ ​I​ ​was​ ​describing​ ​people​ ​with​ ​disabilities​ ​or​ ​people  who​ ​have​ ​been​ ​traditionally​ ​left​ ​out​ ​of​ ​a​ ​work​ ​environment​ ​will​ ​not​ ​have​ ​data​ ​that​ ​can  be​ ​used​ ​for​ ​decisions.​ ​But​ ​there​ ​are​ ​ways​ ​to​ ​model​ ​current​ ​situations​ ​that​ ​were​ ​not  there​ ​in​ ​the​ ​past​ ​to​ ​see​ ​well​ ​what​ ​with​ ​this​ ​particular​ ​skill​ ​what​ ​with​ ​this​ ​new​ ​strategy  what​ ​would​ ​this​ ​add​ ​to​ ​the​ ​team​ ​that​ ​I​ ​have.     That's​ ​the​ ​the​ ​last​ ​piece​ ​that​ ​I​ ​would​ ​say​ ​is​ ​that​ ​you​ ​want​ ​any​ ​system​ ​that​ ​understands  a​ ​team​ ​not​ ​in​ ​a​ ​system​ ​that​ ​is​ ​trying​ ​to​ ​produce​ ​a​ ​set​ ​of​ ​repeatable​ ​similar​ ​homogenous  workers.​ ​So​ ​the​ ​an​ ​AI​ ​system​ ​that​ ​can​ ​orchestrate​ ​a​ ​variety​ ​of​ ​diverse​ ​perspectives​ ​and  diverse​ ​skills​ ​to​ ​make​ ​a​ ​good​ ​team​ ​within​ ​the​ ​workplace.    Jessica:​ ​​[00:29:18]​ ​​Awesome.​ ​Well​ ​thank​ ​you​ ​so​ ​much​ ​for​ ​taking​ ​the​ ​time​ ​to​ ​talk​ ​with  us​ ​today.​ ​Where​ ​can​ ​people​ ​go​ ​to​ ​learn​ ​more​ ​about​ ​you​ ​and​ ​you​ ​what​ ​you​ ​do.    Workology​ ​Podcast​​ ​​|​ ​www.workologypodcast.com​ ​|​ ​@workology
  12. 12. Jutta:​ ​​[00:29:27]​ ​​They​ ​can​ ​go​ ​to​ ​the​ ​inclusive​ ​Design​ ​Research​ ​Center​ ​Web​ ​site.  Actually​ ​my​ ​name​ ​is​ ​a​ ​unique​ ​identifier​ ​so​ ​if​ ​you​ ​search​ ​from​ ​my​ ​name​ ​many​ ​things​ ​will  come​ ​up​ ​related​ ​to​ ​inclusive​ ​design.You​ ​can​ ​learn​ ​more​ ​about​ ​the​ ​challenges​ ​that​ ​we  design​ ​challenges​ ​that​ ​we've​ ​been​ ​engaging​ ​in​ ​to​ ​stretch​ ​the​ ​design​ ​of​ ​artificial  intelligence​ ​to​ ​be​ ​more​ ​inclusive.    And​ ​there​ ​we​ ​have​ ​a​ ​design​ ​challenge​ ​called​ ​start​ ​your​ ​machine​ ​learning​ ​engines​ ​and  race​ ​to​ ​the​ ​edge​ ​in​ ​it.​ ​We​ ​have​ ​many​ ​different​ ​schools​ ​that​ ​are​ ​putting​ ​together​ ​edge  scenarios​ ​that​ ​current​ ​artificial​ ​intelligence​ ​systems​ ​may​ ​not​ ​understand​ ​or​ ​may​ ​not  have​ ​heard​ ​of​ ​or​ ​recognize​ ​and​ ​we​ ​are​ ​using​ ​those​ ​edge​ ​scenarios​ ​to​ ​challenge​ ​the​ ​ai  ai​ ​developers​ ​to​ ​see​ ​which​ ​one​ ​is​ ​better​ ​at​ ​encompassing​ ​the​ ​full​ ​range​ ​of​ ​human  diversity.    Jessica:​ ​​[00:30:36]​ ​​Well​ ​we'll​ ​go​ ​ahead​ ​and​ ​put​ ​links​ ​to​ ​the​ ​Bigidea.one​ ​and​ ​then​ ​kad  your​ ​program​ ​and​ ​then​ ​as​ ​well​ ​as​ ​some​ ​other​ ​research​ ​that​ ​I​ ​have​ ​found​ ​that​ ​was  pretty​ ​interesting​ ​in​ ​your​ ​work.​ ​I'm​ ​focused​ ​on​ ​inclusive​ ​design​ ​so​ ​thank​ ​you​ ​so​ ​much  for​ ​taking​ ​the​ ​time​ ​to​ ​talk​ ​with​ ​us.​ ​Thank​ ​you.     This​ ​by​ ​far​ ​has​ ​been​ ​a​ ​really​ ​eye​ ​opening​ ​discussion​ ​for​ ​me​ ​and​ ​hopefully​ ​for​ ​you​ ​on  the​ ​subject​ ​of​ ​artificial​ ​intelligence​ ​and​ ​human​ ​resources​ ​and​ ​recruitment.​ ​I’m​ ​looking  at​ ​some​ ​ways​ ​that​ ​this​ ​tech​ ​might​ ​identify​ ​patterns​ ​that​ ​a​ ​know​ ​and​ ​only​ ​eliminate  candidates​ ​or​ ​employees​ ​who​ ​don't​ ​fit​ ​traditional​ ​patterns​ ​or​ ​models.​ ​While​ ​I​ ​love  technology​ ​I​ ​absolutely​ ​do.​ ​It​ ​is​ ​such​ ​an​ ​important​ ​part​ ​of​ ​what​ ​I​ ​do.     We​ ​do​ ​need​ ​to​ ​push​ ​back​ ​a​ ​bit​ ​on​ ​these​ ​AI​ ​technologies​ ​in​ ​our​ ​industry​ ​and​ ​educate  ourselves​ ​outside​ ​of​ ​their​ ​demos​ ​and​ ​slick​ ​marketing​ ​to​ ​really​ ​understand​ ​the​ ​things  that​ ​we​ ​need​ ​to​ ​be​ ​asking​ ​questions​ ​about.​ ​Thank​ ​you​ ​for​ ​joining​ ​the​ ​work​ ​I'll​ ​you  podcast​ ​a​ ​podcast​ ​for​ ​the​ ​disruptive​ ​workplace​ ​leader​ ​who​ ​is​ ​tired​ ​of​ ​the​ ​status​ ​quo.  This​ ​is​ ​Jessica​ ​Miller-Merrell.​ ​Until​ ​next​ ​time​ ​can​ ​visit​ ​work​ ​alci​ ​dot​ ​com​ ​to​ ​listen​ ​to​ ​all  our​ ​previous​ ​podcast​ ​episodes.    Episode​ ​Link:​ ​​http://workolo.gy/ep121-wp  Workology​ ​Podcast​​ ​​|​ ​www.workologypodcast.com​ ​|​ ​@workology

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