It’s hard to refute the many benefits of combatting AI bias in RecTech such as increased efficiency, decreased bias, and improved talent acquisition – exactly what the recruitment space needs! However, as AI can’t always assess the necessary variables needed to make an objective decision (no one’s perfect), there are naturally some risks.
The good news is that there are ways for employers and recruiters to combat the biases present in today’s AI systems. This article will discuss how AI bias can affect the recruitment process and the steps taken to combat this.
The problem with AI bias in recruitment tech
Many companies and organisations are using AI to identify talent that would fit their organisation well but, AI is not agnostic – it has its own biases that can be difficult to identify.
AI bias in recruitment tech is a problem that will become more apparent in the coming years. There are already many cases of AI picking out specific people for jobs based on their race or gender, which can cause issues down the line.
Tay, Microsoft’s automated chatbot, is a perfect example of how AI bias can cause problematic outcomes with no human oversight (especially as Microsoft released the bot to the public with no provisions). Within a few days of interacting with humans on Twitter, Tay started using racial slurs and making offensive jokes about heinous dictators shortly after its release.
How does AI bias affect recruitment tech?
The use of artificial intelligence in recruitment technology has increased dramatically over the past few years. However, as AI becomes more prevalent in this sector, it is crucial to be aware of how bias can affect the outcome of hiring decisions.
AI is programmed to make decisions based on data input and algorithms developed by humans, so there are bound to be biases built into these programs..
How are biases formed through machine learning?
Many companies use AI in their hiring process without understanding the datasets it’s trained on or its algorithms; this means that AI might perpetuate discrimination by filtering out qualified candidates who belong to specific demographics.
This bias tends to form because datasets are predominantly populated by certain genders, races, or demographics, leading to the exclusion of underrepresented groups and more discrimination.
Why is it important to combat AI bias in recruitment tech?
AI bias in recruitment technology is significant to combat because it can considerably impact how people are treated. It poses a risk for companies, who may unknowingly hire someone an algorithm has discriminated against or, worst-case scenario, rejected due to an unidentified bias.
How to combat AI bias in recruitment tech
In addition to 68% of recruiters believing using AI will remove unconscious bias by handling candidate screening, AI can also help recruiters to make data-driven decisions by providing insights and analysis on the candidate and hiring data.
The problem is that humans train AI algorithms, and if those humans do not include enough diversity in their training data, then there can be an inherent bias in the algorithm’s results.
Solutions posed to counter AI bias in recruitment tech include
Recruiter Proactivity is Necessary
While leveraging data analytics to measure the diversity of a candidate pool, RecTech can currently only assist recruiters in creating more diverse and inclusive workforces.
From using AI-powered tools to eliminate bias in the recruitment process to using data analytics for measuring the diversity of a candidate pool, recruiters will need to be proactive in drawing insights from their data and making an active effort to invest in technologies that allow them to do so.
TL;DR Key Takeaways
- AI bias in recruitment tech is a problem that will become more apparent in the coming years.
- AI can perpetuate discrimination by filtering out qualified candidates who belong to specific demographics due to biased datasets.
- AI bias can considerably impact how people are treated and poses a risk for companies who may unknowingly hire someone an algorithm has discriminated against.
- To combat AI bias, recruiters need to review pre-existing data to check if it’s healthy and without bias, diversify the datasets used by the AIs during training, and use Machine Learning Models that are representative of an organisation’s hiring needs.
- Recruiters need to be proactive in drawing insights from their data and investing in technologies that allow them to eliminate bias in the recruitment process and create more diverse and inclusive workforces.