Future of Work Podcast, Episode 2.

Jutta Treviranus, Director and Founder of the Inclusive Design Research Centre, discusses how to improve artificial intelligence systems so they can better serve everyone, including people with disabilities.

Artificial Intelligence (AI) technologies are increasingly becoming part of HR and recruiting technologies. In this interview, Jutta Treviranus, Director and Founder of the Inclusive Design Research Centre, discusses how AI pattern identification algorithms can eliminate candidates or employees who don’t fit the common pattern, and how these systems can be improved to better serve everyone.

This podcast is developed in partnership with Workology.com as part of PEAT’s Future of Work series, which works to start conversations around how emerging workplace technology trends are impacting people with disabilities.​

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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 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.
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

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.

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 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​  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.  So​ ​instead​ ​of​ ​a​ ​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  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 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 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.

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​ology.com​ ​to​ ​listen​ ​to​ ​all  our​ ​previous​ ​podcast​ ​episodes.