AI for smart and efficient recruitment
Enhancing recruitment capabilities with AI-powered intelligent assistance.
Human resource (HR) executives are actively embracing video technology, new screening methods, data science, mobile technology, and other advancements powered by artificial intelligence (AI) to recruit workplace talent. Attempts are in process to automate the end-to-end recruitment activities. Fundamental recruitment tasks such as interviews, however, largely continue to be manual, requiring human skill. That’s because recruiters increasingly value soft skills such as listening, interpersonal skills, and effective communication.
This paper is an exploration of how AI-powered intelligent assistance augments technical recruitment capabilities by improving efficiencies, personalization, and data-backed decision-making. The intelligent assistant helps streamline and enhance various recruitment tasks, retaining a recruiter’s control to make prompt and wise decisions.
The absence of digital interview records makes candidate evaluations complex.
One key challenge for most organizations is that the number of recruiters qualified to assess candidates has not kept pace with the developing recruitment landscape. As a result, recruiters face an influx of applications, leading to suitable candidates never being considered or rejected unfairly. The dearth of expert interviewers translates into sub-standard interviews and poor hiring decisions. The absence of digital interview records makes it hard for organizations to evaluate the efficacy of their interviews.
The outcomes are no better for candidates. Besides enduring a poor interview experience marked by slow turn-around times, candidates often find themselves in roles that do not fit their qualifications and aspirations.
The recruitment industry is responding to these challenges. There are start-ups whose technology focuses on innovation in the recruitment space in the areas of focused screening and behavioral assessment, with a few focusing on technical assessments. Notably, almost all focus on automation, powered by machine learning (ML) and AI. While the progress is visible, there continue to be a few challenges as well.
First, many AI-based solutions are driven by supervised ML, leveraging historical recruitment data that can lead to biased decisions, especially when behavioral cues are used in decision-making. A related issue is the inability to justify automated decisions. For instance:
Second, while behavioral fitness is an important assessment factor, the hours spent on technical assessment far surpass it. Technical assessment requires interviewers to possess deep technical domain knowledge. Also, technical experts often don’t find time to conduct interviews that involve free-form technical conversations. In reality, such conversations are critical to assess a candidate’s expertise in relevant skills and identify gaps in their knowledge or reasoning. Further, a candidate’s response to criticism or a hint in an interview provides insights into their personality.
These complexities make technical interviews difficult to automate. While there has been progress around automating question-answering systems and conversational assistants using natural language processing, they are still largely focused on reading comprehension rather than technical assessment. The existing open-domain knowledge graphs are also not robust enough for assessing candidate knowledge of technical domains such as ML.
An AI-powered intelligent assistant
Powering decision-making from the time a resume hits a human recruiter’s inbox.
The AI- and ML-enabled intelligent assistants equip recruiters with enhanced capabilities to conduct technical interviews. Here’s how it works.
An AI-powered intelligent assistant helps extract information from free-form resumes and create candidate profiles for screening, shortlisting, prepping, interviewing, and overall decision-making.
The process begins with an AI-powered intelligent assistant populating candidate resumes into a repository. Next, the assistant works with human interviewers at every stage, offering suggestions and insights to make prompt and wise decisions.
During the interview, the recruiter, the candidate, and the intelligent assistant form a triangular formation, facilitating a three-way interaction. The human interviewer remains the face and conducts the interview. In the background, the intelligent assistant listens to both parties and makes dynamic recommendations to the interviewer (via the user interface) based on the conversation. In addition to recommending the topic, the degree of difficulty, and the questions to ask, the AI tool suggests assessment scores for the candidate’s answers and the reasoning behind the score. It also highlights the current progress of the interview and assesses the candidate’s different skills and expertise.
The interviewer considers these suggestions and accepts the appropriate ones. Alternatively, the interviewer can provide feedback on the recommendations, such as suggesting a different topic or difficulty level for the next question and overriding the answer-level or skill-level assessment. The assistant acts on such feedback and updates its recommendations in real time.
Multi-pronged benefits of collaborative recruitment
The human-AI collaboration reduces decision-making bias.
Collaborative recruitment is a spectrum, with complete manual recruitment at one end and fully automated recruitment at the other. The technology empowers recruiters to operate in the middle of the spectrum and offers several benefits.
It reduces the cognitive load on expert interviewers, helping them move from making decisions to verifying decisions made by the assistant. It also ensures that fundamental interviewing principles are followed, allowing interviewers with lower expertise or experience to conduct interviews and enabling them to improve their interviewing skills over time. The collaborative approach between a human recruiter and an AI-powered assistant reduces the overall bias of decision-making, as the conscious and unconscious biases of the assistant and the human tendency negate each other.
The process creates a digital repository of recruitment actions, including all interviewing activities. This opens the opportunity to measure quantitatively and track the interview quality, identify deficiencies, and improve upon them.
From a candidate’s perspective, the chances of being assessed fairly and matched to a suitable role improve significantly. The approach elevates the candidate’s experience, too, even as they continue to interact with a human, but their assessment happens using standard, measurable criteria.
The future is AI
AI-led tools will ease and refine recruitment processes.
There is wide scope to use technology-led assistance in the recruitment industry, given the need to find solutions to various complex technical issues, including candidate diversity, skill, domain adaptation, improved behavioral trait evaluation, and organizational or profile fit.
Human resource management, however, has made great strides within the past two years, as modern companies grasp the true power of AI. With the talent market continuing to tighten, integrating AI assistance with recruitment will ensure a competitive edge, as organizations learn to use smarter tools to find the talent that can get the job done.