Estudo: Tecnologia de IA não ébala de prata para contratação dos melhores funciona¡rios
A tecnologia de inteligaªncia artificial agora éusada por um número crescente de empresas que procuram contratar os melhores funciona¡rios, mas uma nova pesquisa da Universidade Rice adverte como ela pode incorporar vieses e ignorar...
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NVWpo1WWWV2SnBIhK6lCyEJ2VXC6BXZZEplOwhCUhOdRG00Q5BKJguL28zXns/TpzvBOeWXVDPGLIkxMAk5rQpIGOIjSBJCi1UFJWRnCdl8s8vRw1mdiVbq8UiNVtZHRToFm15h6M2eNMs98tNGglJWVWLqcjrR0bMevK1WqWmGkMcd6ObDo41pjGOaotCjNLAkkTAlEyp6Szy069M/MFeqo4WY9dRwsp66vz+c9Lo8b0D0UvN2noDgyO4+GztnFZ2nxWdk47OuclnVOVE6tPJqL+N0sGOkWWZ1CbipGuSMbWJOIJsiTIrbLBkpYwuhWYtNYrcp+jgnapcxNFllcx1uurL8tpdltzIpJk1ZKXLIkW12Uj6vJ6p1HcuYTBDBBElBQKMm1nOe953he2JiexVmloEpq2SOdHqi8k6zrkrsKORHtKzjx7aONo3UmU6Eo5h1YnLN1NZzpbjz66mk4S9KR5qXo1XBt7KOdotrLqYVrzuZ2OPnacTNsiCu7PKJimoNwJ1hBVWXmYs2QzouKHZc6l346lnlBFKrJ1THXbAjdXeKjbmKTTXLbPOEGBoorlB1eV1Ze+rIyRYgiq0lFFApxCxwWUMcF1qxFZZEiKZKSkDTE0DAGgGmHI4nsMp5yzp4pYSyUHTt4oenPNyT0t3mbq9Ho4e86r5dxrjCwqVkCEbApr0i4o7g5tfVUvIXZDjPsByX1Q5cumHPntLMS2hgr6aORn7qTzeb11VeXfY4pIhIujorlrK1ZdfilZvzQhW+iMbJTjfLTC/PFjrsWvoYtp6iKeZXXZnRpAxitQxS6ubiqWXVh1LmU40LbXydponOREaBjIkkAAxAxAJgozCnPticjF6OtfMUerznmV280c+6VR1eh5dnrX53oWbbKNFNzcQcgUkDEEiATIIsK4raUxNBkRsMEDpHKrOycOB3jztceg5XOqCN7WqOuMZTWjJK8soV4VO0I3QVW1p2WW57Kjtxaz0FlCmNVFsZcsqcq9GnFimtXPr6BX07b7B18+NHJr1W1dt3o4Q5cbNXnu9VsboFcqLiQmMRDAoAUARMBKQQJllMNKMle9LysnfUeYo9XUvl596qOVLphzTqM5R1pLx32ZHEfbZw5dtnFl2Wcg7MU5EuoHNfRDnvfFcT2xMcdqMWfpwOe9kjCujAwvaGB7kYXtiZzUGXfRaUYvRh5J+i5qZNuPqnSi464zsokseP2qq8xT3+ZnVPU5Fk16JcIXo5FvH0a7Esz1cxZ039OyG1VkufXyJdfW5Ha1zvr0zm+Vl61Scx9XjzVjyGda3jDa8LN0ueHRfOR03zA6b5QdV8kTrLkpesckOquWHVOUjrnJDry4wdo4odo4odt8JndXDR3Dhs7Zww7hxA7i4gdpcZ12Djidd8gXsLkCdc5COuccOwcYl7MePE6ufIS966i/WRDM+DrctOpCUOnFuIScQee9nJw+hpPNw7uGbp286vO+0uNI69uHoG2/FBNXNo5ysh2LNXRhcjiqFhdzN9xr5PQyLxy2jn1kRY3EJEQkRZIiEiINAJBAIGIGJLIixiBuKJkGNxCREJEQkRCREJEQkRCRFEyKJEWNIGRIkRFkREmRDvX5795YgOV0+UnXgHTigEYAADAWOYExYgm80Qz0u0BDrAorA194NTcwirGFZ+iGubQS4OaGOjAmgAGAAAAMAAIigEwAAEAIAYAAoACCwYSgCIAAAAABRgiAUYCAEAAEDAkAdy8NZACPLCz//EADAQAAICAQMCBAYCAgMBAQAAAAABAhEDBBITECEUIDAxIjIzNEBQFUEjQgUkNUNg/9oACAEBAAEFAv0TMhn9zGQIi/LzRuL6tjkMoXVsQkL02W/Lovu/0TMhn9zGQIi/LZnjtl0n0rpZuL6IQvwdF93+iZkM/uYzGRF+Zqo/A30kiurXShCQvwtF93+iZkM/uYzGR/NzK8bk04u+lDXWihL8TRfd/omZDP7mIxoX52TS45mbTPEbqF3TRRtNpRX4mi+7/RMyGf3MPvAX58kms2k+OMHAaK/I0X3f6JmQz+5gIC/PZtMuG0/xV5NF93+iZkM/uYWQYmX0sv8AEv0X7ZlWT8jRfd/omZDN7kGQmRmKRuNxuNxuLLLLLLLLLLLLLLLLLLLLLLLLLLLNR83SjaS7C/D0P3f6JmQzLvtKE2iMxZTlOU5TlOU5TlOU5TlOU5TlOU5TlOQ5DkOQ5DkOQ5TmOZHMjnRzo5kcyOZGaV+RzJO/Jf4Gh+7fv+hZInCziOI4jjOM4zjOM4zjOM4zjNhsNhsNhsNhsNhsNhsNhsNhsNhsNhsNhsNhsGuw5Dl+Nofu/wC/0TKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKNptNptNo42ppxGV5X1v1tD95+FZfo2WWWWX+qlFSOCJlx0Ndy+lfg6D7vd8frvrZZZZZZZZZZZZZZZZfmvy3+fJWskKGV+HooVqF9X8Oyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyzcbjcbjcbjcbia3JwKH0XrxjuNG34xfV9B//AIeUCUCvN7mHTktPFrJj2PzRuBoXu1n/ANfSooooorqx/vWiSrypNmDARVKTNTK/PLInDRRS1K+r+A30Y/32SNjXRRbI4jHiRFEmN2Zvfpfkao0Dfi//AKeivM2OQn0ZRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRXSvLQ8aZxIUEhIXY3USmSfwzlb/roukVZffRfeL6norytkmNkWLyUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUJdn2JyFZL2ku/ljKhK1oPvV9T0H5WyTJS6QYmX+krpRRRRXWiiiiiiiiiiiiiiiiiiiihk2SImRkhj60UP20H3K+p52xssT7WNjkSme/XcKQmIX7+cujEyYxKycaH5F76K/GL5/JZY5EpjyCmQfZslIlK/IxsUiMyMhP99knRKVtEn0fsQ+Z490eI4yePsuxihZpV/2P9yyzcORuJMyNlkZmCVmV0278ljY+lmORFi/d2SkZJX0RI/t+zI/NH5G+kpoaME6NPJeJl8+4cxzHMuxI2mSBONdMEqnmVx6tjflizGyIvUor9ZJk8o8lkmWJjP7fsxe8JfA2Ske4u6iqlo/u8rocxyNxZjF0kjKiRHs18WMsbL6UV5MbIC9ev1LMkifkixi9/8AV9IPs2MihdpP5tH93qflsvrjI9JMyMl000rhm7SsSH7oSGiS64yAv3EjKS6IaE+v9Ppj9pIUSK7/AO3+2krxGq+n5ISIvpIyjEaaVT1K7GPusi7kRoyR6w98YvSsv9bIyy7jRfT+xH+r6Yxxsj2f+3tKjRfcav6fVsxK3FdJkx9MfzZFuxmAzLpjRROPaS6Q98YvRs3CkbhS8r/RtllmWQ3fR9x9vKvZlEPf+380vmye9vboPuNZ9Po2Lu8UaSGTZIZRBEflyqsmAyLtXfF0kZF3MaIC9B9GzcbyOQjIv9NITJSJuxrq11T6L5X0i+79m+3zLfbUu3/Hv/taz6ZfTBEQ2ZM1DzG6+iIkDVRML7+6mu8OwmMyRJLviiRF6D6SGyUuymY5kX+kbN3SRK+jXWQ+kekfZ9F72f3/AH/9ItXoPvNZ9K+kVbgqLM2UaySO6aYhERSp5luhHsY5WTQiPRx7SxkIi9ORkJkTEiIv0TZORYshuskdh9ZvqukfZjP7/qrS9v8AeK+PRL/va36NiMaLonlpabHySTia9REITEzcQe7HKNSx+79qER6NCQvTZNEomOJFCF+iyS7Xb9lJ91kZyG83Flkn5ERH1iJ0J939T/fRr/va76JH3iu0iMXlyRShCcqWWbnIXSyzSz75V3QvZ+5D12NDiKNERfopSM07Ikh+/lsfkREfX3PfpLtN++j+91/0jBj3Pb2yI0+JY4SZqMu5+VsxT25J94oiS6Q/Aoo2iXSyy/Wz6lwyafNzR9ZsmyRFkmP00QGPpj91jRNbZS+Zs0b/AO3rvoRVvBjpOIsfds1GXzMZ/eGW7ERH0h5F6tdWWbhP1ZvbCU1M0sFGHqyZKY2SLG/Vh5I+6lZldyn8xo/utf8AR0sbcF2Y2Zsm1N7n5WPpo5dpe66MiIfRfgMkbiLF6msnUccd04qo+k2SyULMbyfR9x9vWh0fRM3UP3n8yRo/utd9HSey9pvvknRlnvl5n108tuSXsRJCEPpH8Bkkbe8UL1NVjnKekxu/QZuOQ32PuSibqNxfR+tA/piKIoS7z+aC+HRNeJzx3LDj2lkmZ09vnfVdnB7sZEfSPWP4LRQl6j9iPoyJ2VI3NHIN2TLNxY360fIuzv4rsm7n/Wh7a33k0SdEIWaiPwv39LSSuL911XRiYv1DkSZ/ZJIcBxon+EhdKJD7JdlLtKTZol/3f9pMjHfKu2VdsqqXpaWVZJdES6yZGXeP6d9KHE2jT65MY4/gxVldaKs9x95zfbQO9XI+ZwjtRNGox2NV6UO0l8UEIfWRfxY+lFFerRRRRRRRRRX4VDgbRoywJr14x3GLF2zR2iLHKz2H2HHuqNB95mdLTpbejJxMuMar0tNK8fsy+rGu+L2X5+6I8sEc8DxEBTixs3G4svzzRlj3l6uPHZjxUJGp9k+j7Fidjbs0X3mr+lo/o9WjJEyqvS0zpy8zRjYmWWWX+O2SzJHPK5ZmxyZZYyMnEjqGLJCZtizjYoiXlYzIrJrv6ePFZjx0JdNT8vSQ/b2T+X3P+P8Au9X9PR/Q8kkZMaZ4cemHp2PC0NV5EYvd94+Wj2N5yHIcgpm43G43G43G43G43G4ss3G43m83m83ksqiPUkszZuLL62WX1WRojqGcyOVCmmX1kiXYl7ZEPz0UKNmPCQhXk1Hylm4/tvvJ2ez0H3ur+TSfQ8tG02m0nAy4zabDYbSPYxu4f35WMXVdLLLLLLLGzcb2b2b2b2bmWzv1l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)
Crédito: Pixabay
A tecnologia de inteligaªncia artificial agora éusada por um número crescente de empresas que procuram contratar os melhores funciona¡rios, mas uma nova pesquisa da Universidade Rice adverte como ela pode incorporar vieses e ignorar caracteristicas importantes entre os candidatos a emprego.
O estudo explora as preocupações cientaficas, legais e anãticas levantadas por ferramentas de seleção de pessoal que dependem de tecnologias de IA e algoritmos de aprendizagem de ma¡quina. Autores Fred Oswald, professor do Departamento de Ciências Psicola³gicas da Universidade Rice; Nancy Tippins, do Grupo Nancy T. Tippins, LLC, e a pesquisadora independente S. Morton McPhail revisaram o uso dessa tecnologia.
Oswald diz que a tecnologia de IA — que inclui jogos, entrevistas por vadeo e ferramentas de mineração de dados — pode economizar tempo no processo de aplicação de emprego e na triagem de potenciais funciona¡rios. Mas ele acredita que a eficácia dessas ferramentas équestiona¡vel. Por exemplo, ele diz que a tecnologia de IA poderia ignorar traa§os de personalidade e habilidades relacionadas ao trabalho associadas ao desempenho bem-sucedido, trabalho em equipe e diversidade melhorada.
"Para usar os jogos como exemplo, lembra como as criana§as evitam testes e amam jogos?" Oswald disse. "A mesma ideia se aplica na contratação, onde a esperana§a éque os candidatos sejam atraados para jogar um jogo, e os dados do jogo sera£o pelo menos tão eficazes quanto um teste de emprego tradicional. Sem daºvida, os jogos são envolventes, mas precisamos de muito mais dados para defender a eficácia dos jogos como ferramentas de seleção em situações de contratação."
O uso de machine learning no processo de contratação também levanta preocupações sobre acessibilidade e diversidade.
"Tomemos um exemplo em que os candidatos a emprego passam por uma entrevista por vadeo, e seus dados são então pontuados por um algoritmo de aprendizado de ma¡quina", diz Oswald. "Pode pegar em caracteristicas relevantes para o trabalho, como respostas sobre conhecimento do trabalho ou consciência. Mas agora estamos altamente cientes de que os algoritmos de aprendizagem de ma¡quina também podem captar muitas caracteristicas incidentais irrelevantes para o trabalho, como tom de voz, gestos e expressaµes faciais."
Oswald ressalta que se um candidato estãoem um grupo minorita¡rio ou tem uma deficiência, os algoritmos podem não ter tantos dados sobre esses grupos para entender e julgar seus talentos aºnicos, o que poderia então limitar a diversidade no processo de contratação.
Finalmente, esta pesquisa expressa sanãrias preocupações anãticas sobre os empregadores revisarem informações que não fazia parte do pacote de aplicação do empregado. No passado, os candidatos a emprego podiam gerenciar com mais cuidado os materiais revisados por um potencial empregador, mas agora, a tecnologia de ma¡quinas pode minerar a internet para materiais não relacionados.
"Sa³ porque as organizações podem minerar a internet para obter informações dos candidatos não significa que elas devem", diz Oswald. "E, relacionado a essa preocupação, estamos vendo agora como as questões de privacidade e equidade dos candidatos estãocomea§ando a influenciar as políticas organizacionais, bem como as leis estaduais e federais."
Oswald e seus colegas autores esperam que a pesquisa sirva como um chamado a ação para aqueles que constroem e usam essa tecnologia para engajar pesquisadores a avaliar passivos, riscos e outros problemas associados.
"Preocupações cientaficas, legais e anãticas sobre ferramentas de seleção de pessoal baseadas em IA: um chamado a ação" apareceu em uma edição recente de Avaliação e Decisaµes de Pessoal.