How To Improve HR Team’s Recruitment Efforts With Ethical AI

Misconceptions about AI in HR have trumped its adoption in the field

The ongoing effects of the great resignation have put talent acquisition teams to the test by highlighting the need for smarter recruiting efforts. Some may say the answer lies in using artificial intelligence (AI) with the promise of streamlining tasks and reducing workload. But is widespread adoption plausible when many are still hesitant to adopt it in their processes?
 

One of the main challenges lies in recognizing misconceptions, as misinformation can often mean many talent teams believe tech software is infused with biases. In reality, the main focus of ethical AI is objectively and accurately making assessments to erase any anomalies in the process.

The AI used for HR is often called ethical AI, as it follows rigorous testing and receives input through feedback loops that refine its inferences. The software does not have the final say. Human professionals evaluate the results and ultimately make decisions in the recruiting process.

Dispelling such misconceptions about the tool can change the perception of AI and increase subsequent adoption. So, let us look at how ethical AI helps HR teams work at scale, solving their unique difficulties in producing quality recruitment decisions.

Ongoing Testing for Prevention of Bias

Although many HR teams are already using automation to take care of repetitive administration tasks, helping scale efforts and speed up processes, AI adoption for more complex tasks has been slower. The key difference between the two tools is that automation only completes tasks via human input, while AI analyzes data and autonomously makes inferences to filter the best options for a recruiter to consider.

On average it takes only 60 seconds for a recruiter to read a resume and decide whether the candidate is suitable. While ethical AI can take less time to go through more CVs, its brightest advantage over human capabilities is its ability to continually remove bias from its processes. Constant testing, such as t-tests and effect size tests, update ongoing feedback loops in AI models to refine its predictions.

With machine learning (ML), algorithms used to assess applicants are fed massive amounts of job-related data gathered from successful recruitment results to significantly cut bias every time the model receives new input. Every time the model gets further information, it is examined to spot any inequitable results and retrained in the case of irregularities until these are eliminated. The more job-specific data it receives, the more unbiased it becomes.

As a result, feedback loops help models be fairer to all applicants, regardless of race, ethnicity, gender, and disability traits.

Becoming More Reliable with Psycholinguistics

Psycholinguistics studies how the human brain processes speech and language development. AI software leverages psycholinguistics to analyze candidates’ potential based on their speech, with recorded interviews to recognize behavior, personality traits, and job competency, both reliably and efficiently.

Data given to the system can help examine the diversity of candidates. Companies have increasingly realized that diversifying their teams means a more prosperous work environment with proactive solutions, and many are now actively looking to be more inclusive.

Proactive software leverages psycholinguistics data to feed the model’s ML. Aided by an industrial-organizational (I/O) psychologist, inputs such as certain inflections and tones of voice are evaluated to create accurate outputs, like the correct inference on which traits are desired for a position. Data is then used to train the software in determining the best candidates based on their discipline’s study of behavior at the workplace.

The moment an applicant sits down for an interview, at least 180 biases have already played a role in how a recruiter perceives them. Using psycholinguistics helps level the playing field, as it eliminates these human preconceptions, and making the AI more ethical. The software assesses candidates by their manner of speech to find the correct behavioral tendencies needed for a position, such as working in teams, accepting feedback, and problem-solving.

Instead of advancing misconceptions, software that leverages this discipline will allow companies to study candidate metrics so HR can ensure diversity, equity and inclusion goals are being met.

Efficiently Lowering Workload for Recruiters

One of the most discussed advantages of AI is how it streamlines processes for HR departments. However, this notion is often mixed up with automation, when in reality, AI takes automation a step further by making inferences based on cues given. One example is where conversational AI chatbots are used to create deeper connections with applicants by accurately answering their questions after recognizing the text, tone, and motive of messages.

Ethical AI also cuts down efforts massively and accurately for recruiters to focus on more targeted tasks. Whether answering emails, chats, texts, or reading resumes, HR teams should rest assured that properly trained AI will help communicate with candidates and filter the best ones for the department to focus on.

In the same way humans evaluate subjects depending on the attributes they are looking for, AI is trained to turn qualitative information into quantitative data. The only difference is that software will take significantly less than the human brain to perform these actions. Hence, engines gather, scrutinize, filter, and deliver a selection of appropriate resumes to HR teams from hundreds of applications.

Moving Away from Blind Hiring

The usage of psycholinguistics in AI for hiring processes is only done through voice recordings. It is key to shed light on the exclusion of biometrics in HR, which measures facial and body characteristics to make predictions. Although technology is quite advanced in AI software, the field is not quite ready to apply biometrics to assert a candidate’s competencies for a position, given its current inaccuracies.

HR teams should stay wary of what different vendors sell because if it sounds too good to be true, it most likely is. Biometrics and face recognition tools, as far as current technology goes, might only disrupt the opportunities of applicants instead of improving them.

Despite not using physical characteristics to evaluate candidates the same way blind hiring does, to strip candidates of certain personal traits, ethical AI does not have the same goal in mind. Applying psycholinguistics in the field is the opposite of erasing applicants’ backgrounds. AI engines make a well-rounded decision on candidates, based on job-specific aspects, such as behavioral tendencies to fit a position without needing to erase any information and become ‘blind’ to certain aspects. The software joins data with additional information from the candidate’s resume to give recruiters a complete assessment of who they are.

The ethical use of AI ensures the consistent gathering and unbiased analysis of candidate data, while decision-making is still left to humans, not the software itself. Addressing this misconception can help HR teams trust the software to do large-scale jobs ethically. Its design helps teams focus on delivering a pleasant recruiting process—where candidates stay informed, and doubts are answered in a timely fashion with the help of AI.

Author Bio

Fred Rafilson is Chief I/O Psychologist at Talview.