Enabling diversity and inclusion (D&I) in the workplace should be a priority for all businesses today. There are many reasons and incentives for you to focus on this issue, including:
- Providing fair and equal opportunities to all staff and job candidates
- Building a stronger employer brand
- Accessing a broader range of skills, knowledge and experience
Purely from a commercial perspective, having a more eclectic and representative workforce can help you achieve financial success. Three separate studies by McKinsey conducted since 2015 have shown a consistently strengthening relationship between diversity on executive teams and financial outperformance.
Research by Boston Consulting Group has also highlighted the link between diversity and a company's capacity for innovation.
There’s no doubt about the ethical and business case for D&I, so it's important to ask what you can do to eliminate any biases in your hiring and HR practices that could be stopping you from building a truly inclusive workforce.
One area of innovation that could prove particularly significant on this front is artificial intelligence, which has the potential to remove human bias from your recruitment workflow and raise your efficiency levels.
Tackling human bias in recruitment
A significant challenge many businesses face in their mission to build truly diverse workforces is that human-led processes can be inherently biased. Biases are often unconscious and can be addressed through training and efforts to educate and raise awareness among your workforce, but while they exist, they'll hinder your attempts to promote D&I.
Common biases include:
- Confirmation bias: When people favor certain information or interpret data in a way that confirms what they already think.
- Stereotyping: Generalized beliefs or assumptions about certain groups of people - for example, that men are better suited to jobs in IT or technical sectors.
- Halo effect: Allowing positive impressions of a single aspect or characteristic of an individual - for example, their physical appearance - to inform your overall judgment about them and their ability to do a job.
- 'Like me' bias: When a hiring manager or interviewer looks favorably on an applicant with whom they share certain similarities.
It's easy for these biases to creep into human-led HR practices, and in many cases they could be occurring without the people on your team even being aware of them. One of the clearest and most powerful advantages of AI is that it has no inherent bias, and will operate in an entirely fair and impartial way, as long as it has been programmed to do so.
Improving efficiency and inclusion with AI
It's possible that your recruitment process isn't giving you a full and balanced view of the variety of talent available in the labor market, not necessarily due to biased practices, but simply because the people on your team don't have the time to evaluate the entire candidate pipeline.
When recruiters are struggling with heavy workloads or need to bring in staff at short notice to plug gaps in your workforce, they might cut corners and resort to the quickest (and not the most inclusive) ways to reduce large pools of applicants down to numbers they can manage.
AI and automation can give you more power to labor-intensive tasks with greater speed and efficiency than a human worker is capable of. As a result, you can feel confident that you're getting a full and uncompromised view of all the candidates in your pipeline, and the picture isn't being skewed by the time pressures affecting your team.
Furthermore, AI can raise your recruitment activities to a new level by analyzing individuals' skills, knowledge and experience, and matching this information with your organization's requirements. This can help to improve internal mobility and also give you access to a wider pool of jobseekers - for example, by recommending roles people might not have discovered through traditional channels.
Remember the human factor
AI-driven tools and technologies have a lot of potential to help you tackle bias and make your recruitment practices more inclusive, but it's important to note they're driven by human input.
If the data you use to train your AI applications and fuel your machine learning algorithms is inherently biased, the end results you’ll achieve will reflect these initial prejudices. It's crucial, therefore, to test and interrogate your fundamental processes and data to ensure they align with your wider commitment to D&I.
Make sure any variant of AI that you incorporate into your hiring activities can be regularly audited and updated. This will help you to keep it as fair and bias-free as possible, and will ensure that, when you see that changes and updates are necessary, you can follow up as quickly as possible.