AI has transformed the way we process data.
No longer is manual research required to filter through vast quantities of information, now it can be as simple as clicking a button. But how do we ensure that bias and discrimination aren’t magnified through use of Artificial Intelligence?
For processes like background screening, the subject of bias mitigation is becoming increasingly important. Recruiters and employers don’t want to introduce bias into the hiring process (in fact, on the grounds of the Equality Act, it’s a legal requirement to strive to eliminate discrimination during hiring), but if AI tools used by a hiring organisation have a flawed algorithm or magnify the human biases inherent in the data it processes, there is a risk that the result of a screening check could be discriminatory.
In reality, the use of AI has the potential to both add to the issue of hiring bias…and drastically improve it. It comes down to choosing AI tools which actively mitigate bias versus tools which are built on biased data or flawed programming.
Sounds confusing? Let’s bring some clarity to this hot tech topic, and suggest ways to mitigate bias in your hiring process.
What is AI bias, and how does this impact on hiring?
We won’t go into the full depth of how AI works from a tech perspective, as we’re here to see how AI bias affects the recruitment process specifically. But we could all do with knowing a little about the basics of this all-encompassing new technology.
Bias in AI refers to the perpetuation of bias, discrimination and unfairness in AI-processed data. Bias can be magnified by AI due to a number of reasons, including biased data sources or flawed algorithms. Bias can be magnified even further through biased decision making, made on top of already biased data results.
In terms of hiring, AI bias is now an extremely pressing issue. Traditionally, employers only had to be careful to keep human bias in check during the hiring process, but with the introduction of AI tools and processes throughout many stages of hiring, such as interview software and background screening tools, it’s all to easy to introduce bias unknowingly.
AI bias can lead to discriminatory hiring, and an erosion of fairness for job candidates. Some common examples might include:
An AI interviewee selection tool favours individuals with only college or university degrees, over those with apprenticeships or vocational qualifications.
An AI interview app selects questions that are designed to reward academic intelligence over often equally sought out emotional intelligence.
A background screening check duplicates instances of the same data, such as a negative press story relating to the candidate, giving a one-sided and over-arching impression of the person.
How can we mitigate bias in AI?
AI is built on human-created algorithms and programs, which naturally runs the risk of introducing any human bias into the resulting AI process. Most people have both conscious and unconscious biases (we’re all human), but some of these are more serious than others. Bias towards education, financial background, gender, ethnicity and age can all be introduced into the original data that AI screening software uses to assess if someone is suitable for a job role.
Luckily, as AI becomes more advanced, the impact of some of these biases can be reduced through more sophisticated data processing. AI technicians can also work to improve the fairness of AI tools through a series of ‘housekeeping’ methods, including:
Preprocessing data: By cleaning datasets and using diverse datasets that include a wider range of demographics, there is a reduced chance of bias existing within the source data.
Building fairer algorithms: Fairness constraints can be consciously introduced into an AI algorithm, and AI models can be trained to be more aware of biased inputs.
Following ethical AI frameworks: AI is a relatively unregulated technology, but policies and codes of conduct can help to keep AI bias in check, through introducing regulations that AI companies have to abide by. The UK government have started to make more steps towards regulating AI, with a white paper, ‘A pro-innovation approach to AI regulation’, released in March 2023. The Institute of Electrical and Electronic Engineers (IEEE) is also developing its IEEE P7000 series of standards relating to the ethical design of AI systems.
Conducting regular human audits: Impact assessments and audits conducted by human committees is an important step in monitoring AI bias and taking accountability for it.
How to mitigate bias in your recruitment process
Bias mitigation is an important practice in every stage of the hiring process, not just those which involve AI technology. You can also reduce human bias by introducing certain measures, such as:
When writing a job description, expand your requirements beyond degrees and qualifications, focussing instead on essential skills, competencies and experience. Avoid using gendered language, opting instead for inclusive language and tone.
Make candidate information anonymous. Blind CVs, where the candidate’s name and personal details are removed, are good practice and can help to sidestep unconscious bias.
Avoid hiring for culture fit, which tends to find people very like those already within the business. Instead, consider widening your reach to bring individuals who don’t fit the norm of your existing team, who will bring different skills and experience to the table.
Implement a structured interview process, with standardised questions and a standard way of evaluating responses. A diverse panel of interviewers can also help to mitigate interview bias.
Use a bias-free background screening tool like YOONO to assess the suitability of candidates for a role without introducing human bias. YOONO uses intelligent insights to present only the most relevant data about an individual, and the software also eliminates duplicated information, giving you a more balanced overview of the person.
With all of these steps in place, you are well on your way to mitigating bias effectively when you hire.
Put an end to unfair hiring practices
There’s a good reason to consciously mitigate bias in your hiring process, and why it is equally important to tackle AI bias head-on. Bringing more diversity into your business, and opening up your hiring to more inclusivity is not only fairer for everyone, but can have a huge positive impact on business success.
Many businesses fall into the trap of hiring for culture fit, which creates an inward-looking work culture and prevents fresh ideas and skills from coming into your company.
If you want to hire the people who will really be the right fit for your business, you need to overcome bias and cast your net wider. By using an AI-powered background screening tool like YOONO, which mitigates bias through advanced technology, you can get to the heart of who they really are, and what they can bring to your organisation.
Start a YOONO search to generate a bias-free report today, and see how YOONO can be the key tool in your arsenal for making hiring fairer.