Indeed, these risks and learnings extend beyond self-driving cars. Big data startups are making big promises in the talent acquisition space. Machine learning and artificial intelligence can lead to similar crashes and other unintended mishaps when it comes to making hiring decisions. Discrimination, adverse impact, and lack of reliability and job relevance are just a few of the accidents talent acquisition professionals must take care to avoid when relying on insights drawn from data mining.
There are already clear rules for:
- Fairness
The data must not treat different groups differently.
- Reliability
The outcome should perform similarly with new data.
- Job-relevance
The data used must relate to the demands of the job.
Just like cars must learn to detect the difference between a truck and the sky, HR professionals must learn the difference between hiring with speculative versus predictive analysis. Because it’s the same road to legal action when mistakes are made.
The companies engaged in developing autonomous cars have sought a broad range of expert engineers and scientist to get it right. Organizations that want to make use of data to develop smart hiring systems engage industrial-organizational (I-O) psychologists. These are the data scientists and selection system engineers who design candidate evaluation methods that evade wrong turns, accidents, and outright breakdowns when making hiring decisions.
Want to speak with an I-O expert? Contact us. We are one of the largest employers of PhD-level I-O psychologists exclusively dedicated to selection science and hiring system design, validation, and implementation.
Hiring may always be an act of human judgment, but we will help you steer clear of obstacles and augment your decision making to maintain your safe hiring record.
