Using Algorithms for Hiring
Could an Algorithm Hire Your Next Team?
What if you could predict the success or failure of your next hire without even interviewing them? According to the New York Times, companies like Workday (a company offering cloud based personnel software) are using algorithms to help us make important decisions about hiring and managing teams.
Quentin Hardy writes,
Workday has released a product that looks at 45 employee performance factors, including how long a person has held a position and how well the person has done. It predicts whether a person is likely to quit and suggests appropriate things, like a new job or a transfer that could make this kind of person stay.
Using data-based character judgments to inform our decision making is interesting, and the possibility of relying on objective data to make important hiring decisions sounds compelling until we remember that algorithms are built by people, and bias is human. According to Warren Buffet, “you only have to do a very few things right in your life so long as you don’t do too many things wrong.”
How to Minimize Bias:
- Search relentlessly for potentially relevant or new dis-confirming evidence
- Seek diverse outside opinion to counter overconfidence
- Flip the problem on its head to see if we are viewing the situation in either a positive or negative way
- Redefine the problem from here on out and ignore the old problem to avoid escalation of unnecessary commitment
Since it is impossible to eliminate bias all together, or to achieve pure objectivity, one thing we can all work on is minimizing the impact bias has on our decision making, and be mindful not to do too many things wrong.
Jennifer Albrecht, Vice President of Professional Development, has been teaching and consulting with Learn iT! since 1997. Since joining Learn iT!, Jennifer has built and facilitated all of Learn iT!’s Professional Development classes including Communication, Leadership, Negotiating and Decision Making.
Jennifer strongly believes in Learn iT!’s 8 Step Model for Learning and applies it in all of the classes she builds and facilitates. Further information on the 8 Step Model can be found here.