AI in Recruiting: The Good, the Bad, and the Ugly
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The document discusses the role of AI in recruitment, highlighting both its beneficial and detrimental aspects. It notes that while many firms are optimistic about AI's potential in HR, issues such as bias in recruitment practices and the limitations of certain AI technologies remain concerning. The author emphasizes the importance of balancing intelligent HR tech with human insight to improve hiring outcomes and address discrimination.
AI in Recruiting: The Good, the Bad, and the Ugly
- 1. AI in Recruiting D r. D a n i e l M ü h l b a u e r T H E G O O D , T H E B A D A N D T H E U G L Y
- 2. Knowing vs. Doing currently use or implement AI projects in HR. think AI is a highly relevant technology. 79% of firms 8% of firms AI-study 2020 by Deloitte
- 4. Robot-Coworker = popular? of the surveyed people are optimistic and excited about robot-coworkers (n=8000) 53% 64% 36% AI@WorkReport by Oracle of the surveyed people would trust a robot more than their manager (n=8000) of the surveyed people think that robots are less biased than managers (n=8000)
- 5. MALE APPLICANTS RECEIVE SIGNIFICANTLY MORE RECRUITER CALLBACKS THAN FEMALE APPLICANTS. (Rivera/Tilcsik, 2016) MALE APPLICANTS ARE PREFERRED FOR "MALE OCCUPATIONS", BUT WOMEN ARE NOT PREFERRED FOR "FEMALE OCCUPATIONS". (Koch et al., 2015) NO REVERSAL OF DISCRIMINATION IN RECRUITING AFRICAN- AMERICAN APPLICANTS SINCE 1989. (Quillian et al., 2019) FEMALE APPLICANTS OVER 40 RECEIVE SIGNIFICANTLY FEWER RECRUITER CALLBACKS. (Carlsson et. al. 2019) APPLICANTS WITH A MIGRATION BACKGROUND RECEIVE SIGNIFICANTLY FEWER RECRUITER CALLBACKS. (Moa, 2017) FEMALE RECRUITERS PREFER WOMEN. MALE RECRUITERS DO NOT PREFER MEN. (Carlsson/Eriksson, 2019) WOMEN GENERALLY RECEIVE FEWER RECRUITER CALLBACKS, ESPECIALLY IF THEY HAVE CHILDREN. (Gonzales et al., 2019) SHOULD WE PERHAPS GIVE INTELLIGENT HR TECHNOLOGY A CHANCE AFTER ALL?
- 6. RECRUITING FAILS? 46% of all hires
- 7. The solution to the hiring puzzle is finding your firm’s happy place between HR Tech and HR Touch! DANIEL MÜHLBAUER
- 8. THE GOOD RECRUITING ANALYTICS & PROCESS MINING Intelligent technology analyzes your recruiting funnel and identifies errors, circularities, inefficiencies of your recruiting. INTELLIGENT STRING- BUILDER Intelligent technology analyzes your job posting and suggests the best Boolean string for active sourcing. JOB-ADS w/ COMMUTE- ANALYSIS For each job posting, candidates receive an analysis of the work route including seasonal weather effects, means of transport and rush-hours. TEXT-MINING OF COMPANY INFOS An intelligent technology analyzes all publicly available company information and derives various indicators of employer quality from the language used. Good AI-based REC technology focuses specific HR tasks and enables a linkage of artificial and human intelligence!
- 9. THE GOOD SKILL-BASED MATCHING Intelligent technology compares requirements and qualifications at the ski level and predicts a match quality. AUTOMATED APTITUDE TESTING Intelligent technology combines validated methods of aptitude diagnostics to predict aptitude in real time. PERSONALIZED CAREERPAGES Intelligent technology modifies career site content based on target group analysis and typical click behavior. AVATARS FOR RECRUITING TRAINING Intelligent virtual avatars enable the realistic simulation of interviews for a variant-rich training of recruiting trainees. Good AI-based REC technology focuses specific HR tasks and enables a linkage of artificial and human intelligence!
- 10. THE BAD EMOTION RECOGNITION Emotion recognition is a very serious research direction, but its results have not yet reached market maturity. FIT-PREDICITION BASED ON X The fit between people, jobs, teams, companies or cultures are very complex. The data models are often not reliable. AD TARGETING ON SOCIAL MEDIA Job posting algorithms are not transparent enough. They can subtly discriminate. Poor AI-based REC technology focuses on contexts that are too complex, are opaque, or are still in validation. As a result, their use should not yet be full- scale, but in pilots or validation studies.
- 11. THE UGLY INVALID SELECTIONMETHODS Poor selection processescannot be salvaged with AI use. Examples: Graphology, MBTI, DISG, profiling. DISCRIMINATION IN VIDEO RECRUITMENT Algorithms for image and video processing often work significantly worse for non-light-skinned people: PERSONALITY-ANALYSES -> PERFORMANCE Personality is not a stable predictor of subsequent job performance. AI- based technology does nothing to change that. Bad AI-based REC technology is based on dubious or even discriminatory procedures whose automation reinforces systematic disadvantage! FACE-ANALYSES -> ATTITUDES & BEHAVIOR Analyses of micro-expressionsto infer any traits or emotions of a person are not valid.
- 12. YOUR USE CASE Map the recruiting process including all important process steps (tasks)! PROCESS ANALYSIS Select the process steps that can be standardized! SELECT Focus on the processes with business relevance! FOCUS Translate the selected process steps into algorithms or out-of-the-box tools! TRANSLATE
- 13. Breaking down silos...the expedition into HR's technological future will not take place in a silo! DANIEL MÜHLBAUER
- 14. HR MUST HAVE A SOLID UNDERSTANDING OF HOW TO DESIGN AND SCALE INTELLIGENT TECHNOLOGY TECH MUST HAVE A SOLID UNDERSTANDING OF THE HUMAN DIMENSION OF WORK THE MISSING LINK?
- 15. COMPUTER LINGUISTICS EXPLAINABLE AI & FAIRNESS SOCIAL ROBOTICS & AFFECTIVE COMPUTING VIRTUAL & AUGMENTED REALITY THE BIG LEAPS?