Up tókànItọsọna atẹle
Awọn ofin Ofin EU AI fun AI ni igbanisise ati HR
Awujo
Awọn ohun elo Itọsọna
AI ninu HR
AI ni HR le ṣe iranlọwọ lati ṣeto awọn ohun elo, ṣeto awọn ibere ijomitoro, ṣe akopọ esi, tabi ṣe atilẹyin eto oṣiṣẹ.
Gbọ si itọsọna yiiAI-ṣe afihan · ~2 min
Lori iwe yi2 min ka
Akopọ
Employment decisions affect people’s opportunities and require careful attention to relevance, fairness, privacy, accessibility, and applicable law. A ranking is not a neutral fact about a person.
Awọn gbigba bọtini
- Define job-related outcomes.
- Test fairness, accessibility, and privacy.
- Keep accountable human review and recourse.
Jin Dive
Define the job-related outcome and the human decision-maker. Screening, performance support, scheduling, and workforce forecasting have different implications. Avoid labels that simply reproduce past hiring or promotion decisions without checking whether they reflect the qualifications and outcomes the organization actually needs. Evaluate error rates and opportunities across relevant groups and accommodations. A model can disadvantage people through proxies, inaccessible assessments, or data missing for a group. Test the complete application and review process, not only the model’s score. Inform candidates and employees appropriately, protect personal data, and provide a meaningful way to correct inaccurate records or request accommodation. Keep a trained human reviewer with authority to challenge the recommendation. Document vendor claims, model versions, data sources, thresholds, and decisions. Monitor outcomes after deployment and consult current employment law and qualified experts for the jurisdiction and specific practice.
04Apẹẹrẹ ṣiṣẹ
Question a historical hiring label
Imagine training on past hires where one department rarely interviewed career changers.
A model may learn that pattern and rank those applicants lower without measuring job capability.
Review the label, include relevant outcomes, and assess the complete process for unjustified disparities.
Ohun ti o fihan
The constructed example shows how historical decisions can become a misleading target.
Real-World imuse
Audit an automated screening recommendation against job-related criteria and human review.
Test an assessment with accessibility accommodations and missing-history cases.
Fifi o sinu iwa
Kini idi ti o ṣe pataki
- Kọ awọn yiyan. Apẹrẹ ipele-ohun elo pinnu boya AI ṣe ilọsiwaju awọn abajade gidi.
- Ẹgbẹ ati ṣiṣan iṣẹ. Ijọpọ iṣan-iṣẹ ti o dara ṣẹda awọn anfani iṣẹ-ṣiṣe ti awọn olumulo le gbẹkẹle.
- Ewu ati ailewu. Awọn ọran lilo ti iwọn daradara dinku rirẹ iyipada ati eewu imuse.
Ṣọra fun
- Ṣiṣẹda ilana fifọ le ṣe alekun awọn iṣoro to wa tẹlẹ.
- Awọn ẹgbẹ le ṣe adaṣe adaṣe ki o yọ idajọ eniyan ti o nilo kuro.
- Didara le fò ti awọn abajade ko ba ni iṣiro nigbagbogbo.
Next awọn igbesẹ
- Ṣe maapu iṣan-iṣẹ lọwọlọwọ ki o ṣe idanimọ igbesẹ ti o ga julọ.
- Ṣe alaye awọn aaye ayẹwo eniyan ṣaaju adaṣe ni kikun.
- Kọ awọn olumulo lori awọn itọsi, awọn ọna igbega, ati awọn iṣedede didara.
- Tọpinpin awọn abajade ipele-ṣiṣe lati jẹrisi iye idaduro.
Awọn orisun ati siwaju kika
Tesiwaju Ṣiṣawari
Ìwé ìròyìn ọfẹ
Gba ìkànìyàn AI ojoojúmọ́
Ìtàn AI mẹta tó jẹ́rìí ní gbogbo owurọ́ ọjọ́ iṣẹ́, kọ́wé ní Gẹ̀ẹ́sì tó rọrùn. Ọfẹ́ fún gbogbo àkókò, kò sí ìpolówó.
Ìmeèlì kan ní gbogbo ọjọ́ iṣẹ́. Yọkúrò ní ìkànìyàn ní ìkànìyàn kan. A kò ta tàbí pín àdírẹ́sì rẹ.
Ṣe idanwo fun ara rẹ
Take the AI in HR quiz
Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.
Ṣe àtìlẹ́yìn ẹ̀kọ́ AI ọfẹ́. AI Understanding jẹ́ àjọ àìní èrè 501(c)(3) — kò sí ìpolówó, kò sí àkúnya owó, ní gbogbo àkókò. Ṣe ẹbun kan
Awọn ibeere ti a beere nigbagbogbo
Does an AI hiring score objectively measure a candidate’s potential?
No. It reflects data, labels, features, and assumptions that need job-related validation and fairness review.
Tesiwaju kikọ
Jẹmọ awọn itọsọna
Awọn itọsọna diẹ sii ti a yan fun koko yii