As businesses adopt Artificial Intelligence in recruiting at an increasing rate, it’s critical that they know how to leverage the technology to mitigate bias rather than worsen it.
Despite signs of a recession, hiring is a key concern and a challenge for many industries, including hospitality, healthcare, transportation, and manufacturing. According to the US Bureau of Labor Statistics report, there are currently 10.7 million vacant jobs in the US. Talent teams must find ways to be more successful and efficient in the current environment.
Using Artificial Intelligence can help make the hiring process swift and efficient. Additionally,
Because bias is the process of identifying patterns in data, data scientists will claim that all data is biased. Some bias is needed, such as the bias used to recommend job roles to individuals based on their desired location, interests, skills, and job title. When building algorithmic models, it is helpful to encourage biases that fulfill these preferences to match candidates with positions they are interested in and recruiters with the best candidates to fill open positions. Bias is advantageous here for both employers and job applicants.
However, social bias worries regulatory organizations and hiring teams. On the basis of age, gender, or other demographic characteristics, this bias rejects job candidates. Another example of social bias is a discriminating preference for individuals who attended particular institutions or listed certain former companies on their resumes.
Continue reading: https://enterprisetalk.com/featured/how-companies-can-achieve-success-with-artificial-intelligence-in-hiring/
Despite signs of a recession, hiring is a key concern and a challenge for many industries, including hospitality, healthcare, transportation, and manufacturing. According to the US Bureau of Labor Statistics report, there are currently 10.7 million vacant jobs in the US. Talent teams must find ways to be more successful and efficient in the current environment.
Using Artificial Intelligence can help make the hiring process swift and efficient. Additionally,
Biased Hiring Trends
Because bias is the process of identifying patterns in data, data scientists will claim that all data is biased. Some bias is needed, such as the bias used to recommend job roles to individuals based on their desired location, interests, skills, and job title. When building algorithmic models, it is helpful to encourage biases that fulfill these preferences to match candidates with positions they are interested in and recruiters with the best candidates to fill open positions. Bias is advantageous here for both employers and job applicants.
However, social bias worries regulatory organizations and hiring teams. On the basis of age, gender, or other demographic characteristics, this bias rejects job candidates. Another example of social bias is a discriminating preference for individuals who attended particular institutions or listed certain former companies on their resumes.
Continue reading: https://enterprisetalk.com/featured/how-companies-can-achieve-success-with-artificial-intelligence-in-hiring/
