{"id":6468,"date":"2021-11-03T11:29:39","date_gmt":"2021-11-03T10:29:39","guid":{"rendered":"http:\/\/timetohire-backup.twelvetwentystage.com\/?post_type=news&#038;p=6468"},"modified":"2021-11-10T09:45:56","modified_gmt":"2021-11-10T08:45:56","slug":"recruitment-en-ai-is-ai-echt-zo-unbiased-als-we-denken","status":"publish","type":"news","link":null,"title":null,"content":{"rendered":"\n<p><\/p>\n\n\n\n<p><strong>In een eerdere blog \u2018Het Ontslag Algoritme\u2019 schreef ik over \u00e9\u00e9n van mijn favoriete onderwerpen; namelijk AI binnen recruitment. Dat er tegenwoordig veel mogelijk is, blijkt wel uit Sigmund en het bestaan van \u2018ontslag-algoritmes\u2019. Soms roept de inzet van AI wat ethische discussies op, maar dat maakt het niet minder interessant! E\u00e9n van de (ethische) discussies die mij ook altijd triggert, is de inzet van AI tijdens sollicitatieprocessen (bijvoorbeeld door middel van assessments) om discriminatie te voorkomen. Zo zou AI geen onderscheid maken tussen man\/vrouw, leeftijd, huidskleur en geloof. Het selecteert namelijk puur op hard-skills. Maar is AI werkelijk zo <em>unbiased <\/em>als wij denken?&nbsp;<\/strong><\/p>\n\n\n\n<p>Laten we even teruggaan naar het begin: wat is een AI? AI, Artificial Intelligence, oftewel; kunstmatige intelligentie, is het concept waarbij machines denken als mensen. Het brein achter kunstmatige intelligentie is een technologie die <em>&#8216;<a href=\"https:\/\/www.salesforce.com\/nl\/products\/einstein\/ai-deep-dive\/\" target=\"_blank\" rel=\"noreferrer noopener\">machine learning<\/a>&#8216;<\/em> heet.&nbsp;<br>Machine Learning is dus leren door middel van het verwerken van data. En hoe meer data een machine krijg, hoe beter deze kan leren. Theoretisch gezien zou het mogelijk zijn om een machine te leren om kandidaten te selecteren op bepaalde skills. Hiermee zou de menselijke bias, die wij allemaal (on)bewust hebben, worden buitengesloten. Maar in de praktijk gaat dit toch lastiger dan verwacht.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-discriminerende-data\">Discriminerende data<\/h4>\n\n\n\n<p>Een super interessant voorbeeld waarom het in de praktijk toch lastiger blijkt dan het werkelijk is, komt naar voren in een <a href=\"https:\/\/www.marcvanmeel.com\/why-bridges-are-racist-and-ai-totalitarian\/\" target=\"_blank\" rel=\"noreferrer noopener\">blog van Marc van Meel<\/a>. Een voorbeeld wat hij noemt gaat over Robert Moses. Moses was een bekend urban planner en heeft in de jaren 1920\u2019s bruggen gebouwd in Amerika. Het was bekend dat Robert een racist was, maar dit zal nooit te zien zijn in zijn werk\u2026 toch? Fout! Bruggen werden lager gebouwd zodat bussen er niet onderdoor konden. En toentertijd zaten vooral mensen van allochtonen afkomst in bussen. Dus een brug was (on)bewust (<em>deze discussie laat ik even in het midden<\/em>) aan het discrimineren.<\/p>\n\n\n\n<p>Het moraal van het verhaal: input (oftewel data) speelt een grote rol bij Machine Learning en kan (on)bewust <em>biased<\/em> kan zijn. Maar als wij heel realistisch zijn, is data uit het verleden (<em>want van welke data moet een machine anders leren<\/em>?) vaak wel discriminerend. Stel; als bedrijf heb je een assessment ontwikkeld die door middel van AI sterke potentiele leiders herkent. De doelgroep die je nodig hebt om dit assessment op te zetten, zijn competenties van leiders. Leiders zijn nu, en zeker in het verleden, grotendeels man. Dus de data waarmee Machine Learning leert, is voornamelijk gebaseerd op mannelijke leiderschapscompetenties. <\/p>\n\n\n\n<p>En om een stapje verder te gaan; er is algemeen bekend dat mannen en vrouwen andere leidersstijlen hanteren en dus ook andere competenties hebben. Je kan je dus afvragen of het assessment niet onbewust aan het discrimineren is? Want mannen en vrouwen hebben vaak nou eenmaal andere leiderschapsstijl, maar dat maakt een vrouw niet minder goed dan een man (en vice versa!). Wij moeten ons dus wel bewust zijn van deze verschillen en deze ook omarmen. Er zijn immers verschillende wegen naar Rome.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-ai-voor-assesments\">AI voor assesments<\/h4>\n\n\n\n<p>Dat AI binnen recruitment niet zo voor de hand liggend is als lijkt, blijkt ook bij assessments die worden afgenomen bij een sollicitatieprocedure. Je neemt het assessment af bij een grote doelgroep, bijvoorbeeld;<\/p>\n\n\n\n<ol class=\"wp-block-list\" type=\"1\"><li>Kandidaten die in dienst komen en niet goed functioneren.<\/li><li>Kandidaten die in dienst komen en goed functioneren.<\/li><li>Kandidaten die niet in dienst komen en ook niet goed functioneren.<\/li><li>Kandidaten die niet in dienst komen maar w\u00e9l goed functioneren.<\/li><\/ol>\n\n\n\n<p>Je hebt dus vier verschillende groepen die je eigenlijk wilt meten om succes goed, en unbiased, te kunnen voorspellen tijdens het sollicitatiegesprek. Maar wij vergeten dat de helft van de doelgroep afvalt tijdens een sollicitatieproces; namelijk de mensen die niet in dienst komen. Hierdoor meet je alleen de mensen die w\u00e9l in dienst zijn gekomen, waardoor de data (on)bewust wordt gevormd. Jammer, want het zou super interessant zijn om te weten hoe succesvol mensen zouden zijn geweest die niet in dienst zijn genomen. Dan zou je data dus wel compleet zijn. Wat w\u00e9l mogelijk is, is het meten \u00e9n voorspellen van de potentie op het moment dat iemand al in de organisatie is. Dan kan je meten met verschillende doelgroepen; laagvliegers, hoogvliegers en de gemiddelde werknemer.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-hoe-moet-het-dan-wel\">Hoe moet het dan wel?<\/h4>\n\n\n\n<p>De conclusie is dus dat het inzetten van AI (en data!) vaak lastiger is dan in eerste instantie lijkt. Wil je als organisatie toch aan de slag met AI, hou dan rekening met onderstaande tips:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Zorg dat de organisatie zich bewust is van data. Er is met data ontzettend veel mogelijk maar dan moet het wel op de juiste manier ingezet worden. Bewustwording is hier de eerste stap in.<\/li><li>Formuleer duidelijk welke factoren je meetbaar wilt maken \u00e9n waarom! Soms willen we iets meten (meten voor het meten) terwijl dit eigenlijk niet logisch is. Blijf dus de \u2018waarom\u2019-vraag stellen.<\/li><li>Zorg dat de data valide is en blijf constant de kritische vraag \u201cmeet de data \u00e9cht wat jij als organisatie wilt weten?&#8221; stellen.<\/li><li>Wees niet bang om hulp van een expert in te schakelen, die kunnen met een kritische blik meedenken.<\/li><\/ul>\n\n\n\n<p>Natuurlijk zal je niet alle valkuilen kunnen wegnemen en dat is ok\u00e9. Zolang jij je er maar bewust van bent, dan kun je pas echt un<em>biased <\/em>werven!<\/p>\n\n\n\n<p>Over de auteur&nbsp;<a href=\"https:\/\/www.linkedin.com\/in\/kimberlydejager\/\" target=\"_blank\" rel=\"noreferrer noopener\">Kimberly de Jager<\/a>&nbsp;is Recruitment Specialist bij<a href=\"http:\/\/timetohire-backup.twelvetwentystage.com\/\">&nbsp;Timetohire<\/a>&nbsp;\u2013 The Recruitment Success Company. Zij is gespecialiseerd in Recruitment Process Outsourcing (RPO) met een special interest in IT &amp; tech.<\/p>\n","protected":false},"excerpt":{"rendered":"In een eerdere blog \u2018Het Ontslag Algoritme\u2019 schreef ik over \u00e9\u00e9n van mijn favoriete onderwerpen; namelijk AI binnen recruitment. Dat er tegenwoordig veel mogelijk is, blijkt wel uit Sigmund en het bestaan van \u2018ontslag-algoritmes\u2019. Soms roept de inzet van AI wat ethische discussies op, maar dat maakt het niet minder interessant! E\u00e9n van de (ethische) 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