0
Introducing unconscious bias
Unconscious bias is used to describe the innate prejudices that we hold, that could impact decision making when hiring and promoting people. We all hold some degree of subconscious attitude towards race, gender, education, age, wealth, appearance and many other characteristics. Because it’s unconscious, this type of bias can be hard to identify. Unconscious bias training can help, but another effective way to cut it out of your hiring process is to use artificial intelligence (AI).
Artificial intelligence and recruitment
AI can help recruiters in many ways. To reduce bias, an AI can help screen candidates based on ability (and not race, gender or other biased factors). It can also spot patterns in hiring processes that could be harming diversity.
When used in this way, however, the AI needs data. Plugging a CV into an algorithm isn’t enough, and neither is a LinkedIn profile. To identify the best person for the job, you need to consider all of their skills, experience, and most importantly, their potential. You’ll never get this wealth of data from a simple CV or LinkedIn profile.
Limited data feeds unconscious bias
Indeed, limited information on a candidate impacts your entire recruitment process – not just your AI’s efficiency. With limited data available, you’re more likely to be impacted by unconscious bias. You’ll be forced to make decisions based off of ‘gut instinct’ and not empirical data. In other words, you’re probably relying on your unconscious bias. Many might jump at that statement, after all, everyone understands not to hire based on gender, race, or any other common prejudice. However, education bias might pass under your radar. The same might go for affinity bias, confirmation bias, and past performance bias.
So, to avoid unconscious bias completely, you’ll need to ensure your AI has current data with a sensible aim to have over 100+ data points for all people in your candidate pool. This should include their skills, competencies, ambitions, experience, client relationships, volunteer work, and so on.
Importantly the AI (or machine intelligence) can then add further suggestions to this list based on similar candidates. Someone experienced in social media marketing, for example, might have campaign management, Buffer, and paid advertising as suggested skills.
Other uses for AI in recruitment
Predicting success: AI can help predict how successful a candidate might be in a role. Again, having a lot of data available is important for accuracy. AI can analyze an individual’s skills, along with cultural fit and other role-specific criteria, to understand who would be best placed for a job.
Creating job ads: It also has an impact early on in the hiring process. AI can use natural language processing to highlight where job descriptions might be biased. It can also analyze competitors’ job posts to give recruiters a benchmark for diverse job applications. Similarly, it can delve into hundreds of past job posts, to discover what attracts the most applicants. From these insights, recruiters can move closer to creating the perfect job ad.
Automating tasks: Of course, there’s also a plethora of tools that can automate much of the recruitment process. AI can schedule meetings, send emails, onboard new recruits, and even answer questions from candidate via a chatbot service. Taking over a lot of the mundane tasks in a recruiter’s day frees up time for hiring the right talent.
Getting talent acquisition ready for AI
Most AI tools available to recruiters are plug-and-play. That means they can get up and running with a little initial set-up.
Again, the availability and quality of your data must be emphasized. Without the right data, your AI is going to provide limited insights. Think of it like a car, without enough fuel you’re not going to get very far.
It’s worth consulting your IT department to understand how any new AI tool will integrate with the rest of your tech stack. You should also consider whether it requires any additional skills that require training or new team members.
Speaking of your team, there is a fear amongst some that AI will take their jobs. In contrast, a lot of AI tools are there to simply augment what they already know. It’s there to make their jobs easier and to move them on to more strategic activities. When implementing any kind of AI tool, communicating its benefits to your team is vital. As is alleviating any fears they may have.
Make recruitment a breeze with AI
AI technology is impacting all businesses, across all departments. Talent acquisition is no exception. There’s a lot of potentials to make the recruitment process ultra-efficient, truly unbiased and fully exploiting the internal candidate pool. By using AI in the right way, and giving it enough data, you can reach the Holy Grail of recruitment: finding and hiring the best candidate without bias.
About the author: Rob Hill is the CRO of ProFinda, a Team and Work Management platform that maps the skills, knowledge, and expertise available across an organization’s total talent supply chain of internal, contingent and alumni workers.
Introducing unconscious bias
Unconscious bias is used to describe the innate prejudices that we hold, that could impact decision making when hiring and promoting people. We all hold some degree of subconscious attitude towards race, gender, education, age, wealth, appearance and many other characteristics. Because it’s unconscious, this type of bias can be hard to identify. Unconscious bias training can help, but another effective way to cut it out of your hiring process is to use artificial intelligence (AI).
Artificial intelligence and recruitment
AI can help recruiters in many ways. To reduce bias, an AI can help screen candidates based on ability (and not race, gender or other biased factors). It can also spot patterns in hiring processes that could be harming diversity.
When used in this way, however, the AI needs data. Plugging a CV into an algorithm isn’t enough, and neither is a LinkedIn profile. To identify the best person for the job, you need to consider all of their skills, experience, and most importantly, their potential. You’ll never get this wealth of data from a simple CV or LinkedIn profile.
Limited data feeds unconscious bias
Indeed, limited information on a candidate impacts your entire recruitment process – not just your AI’s efficiency. With limited data available, you’re more likely to be impacted by unconscious bias. You’ll be forced to make decisions based off of ‘gut instinct’ and not empirical data. In other words, you’re probably relying on your unconscious bias. Many might jump at that statement, after all, everyone understands not to hire based on gender, race, or any other common prejudice. However, education bias might pass under your radar. The same might go for affinity bias, confirmation bias, and past performance bias.
So, to avoid unconscious bias completely, you’ll need to ensure your AI has current data with a sensible aim to have over 100+ data points for all people in your candidate pool. This should include their skills, competencies, ambitions, experience, client relationships, volunteer work, and so on.
Importantly the AI (or machine intelligence) can then add further suggestions to this list based on similar candidates. Someone experienced in social media marketing, for example, might have campaign management, Buffer, and paid advertising as suggested skills.
Other uses for AI in recruitment
Predicting success: AI can help predict how successful a candidate might be in a role. Again, having a lot of data available is important for accuracy. AI can analyze an individual’s skills, along with cultural fit and other role-specific criteria, to understand who would be best placed for a job.
Creating job ads: It also has an impact early on in the hiring process. AI can use natural language processing to highlight where job descriptions might be biased. It can also analyze competitors’ job posts to give recruiters a benchmark for diverse job applications. Similarly, it can delve into hundreds of past job posts, to discover what attracts the most applicants. From these insights, recruiters can move closer to creating the perfect job ad.
Automating tasks: Of course, there’s also a plethora of tools that can automate much of the recruitment process. AI can schedule meetings, send emails, onboard new recruits, and even answer questions from candidate via a chatbot service. Taking over a lot of the mundane tasks in a recruiter’s day frees up time for hiring the right talent.
Getting talent acquisition ready for AI
Most AI tools available to recruiters are plug-and-play. That means they can get up and running with a little initial set-up.
Again, the availability and quality of your data must be emphasized. Without the right data, your AI is going to provide limited insights. Think of it like a car, without enough fuel you’re not going to get very far.
It’s worth consulting your IT department to understand how any new AI tool will integrate with the rest of your tech stack. You should also consider whether it requires any additional skills that require training or new team members.
Speaking of your team, there is a fear amongst some that AI will take their jobs. In contrast, a lot of AI tools are there to simply augment what they already know. It’s there to make their jobs easier and to move them on to more strategic activities. When implementing any kind of AI tool, communicating its benefits to your team is vital. As is alleviating any fears they may have.
Make recruitment a breeze with AI
AI technology is impacting all businesses, across all departments. Talent acquisition is no exception. There’s a lot of potentials to make the recruitment process ultra-efficient, truly unbiased and fully exploiting the internal candidate pool. By using AI in the right way, and giving it enough data, you can reach the Holy Grail of recruitment: finding and hiring the best candidate without bias.
About the author: Rob Hill is the CRO of ProFinda, a Team and Work Management platform that maps the skills, knowledge, and expertise available across an organization’s total talent supply chain of internal, contingent and alumni workers.
