Reimagining the Interview: AI and Video Technology

The traditional recruitment process, while fundamental, often falls short of delivering optimal results. Studies reveal a stark reality: unconscious bias significantly impacts hiring decisions. Furthermore, traditional interviews are characterised by manual processes, reliance on intuition, and a heavy emphasis on resume screening, and are plagued by inefficiencies and biases. Challenges such as the high volume of applications, time-consuming interviews, and difficulty assessing soft skills have long been prevalent.

Organisations are increasingly turning to technology to bridge this divide and foster genuinely inclusive workplaces. AI and video technology offer a promising avenue to mitigate biases, streamline operations, and enhance the overall candidate experience.

Video Interviewing: The Future of Recruitment

AI in recruitment encompasses a variety of applications designed to streamline the hiring process. For instance, AI-powered resume screening tools can rapidly sift through thousands of applications, identifying candidates with specific skills or experience, such as Python programming or five years of sales experience. Candidate matching algorithms can then compare candidate profiles against job descriptions to suggest the most qualified individuals for a role, like recommending a data scientist with a PhD for a machine learning position.

Finally, interview analysis tools leverage AI to evaluate candidate responses in video interviews, assessing factors like communication skills, problem-solving abilities, and cultural fit.

AI-powered video interviewing is set to redefine the future of recruitment by automating routine tasks, providing data-driven insights, and enhancing the candidate experience.

Platforms like HireVue, Modern Hire (formerly Montage), and Talview have emerged as leaders in this space, captivating the attention of global corporations. Industry giants such as Unilever, IBM, Mercedes Benz, Walmart, L'Oréal, and Hilton have already embraced AI-driven AVI and assessment solutions, recognising their potential to streamline hiring processes, enhance candidate experiences, and improve decision-making. Video interviewing and recruiting automation platforms process millions of interviews, leveraging AI to analyse candidate responses and predict job fit. This data-driven approach has transformed the way organisations evaluate talent.

Types of AI Interviews

1. AI-assessed asynchronous video interview: Candidates record responses to preset questions. AI analyses and scores responses. Caution is advised against unchecked AI decision-making due to potential bias.
2. AI-assessed transcribed interviews: Similar to AI-assessed video interviews, but use transcription and semantic analysis. Challenges include contextual misinterpretations and transcription accuracy (WER).
3. AI-led telephone interview: AI system conducts interviews, asks questions, and evaluates responses using NLP.
4. AI Chatbot or Avatar-led interview: Candidates interact with AI chatbots or avatars, responding to human review.
5. Real-time AI analysis during live video interviews: AI analyses tone of voice, language, and potentially facial expressions (though this practice is controversial). Focus on interviewer analysis for improvement is recommended.

Ethical Implications of AI Interviewing and Video Technology

While promising, integrating AI and video technology in recruitment raises significant ethical concerns like data privacy. For instance, collecting and storing extensive candidate data, including personal information, requires stringent measures to prevent breaches. Adherence to regulations like GDPR and CCPA is essential to protecting sensitive information such as social security numbers, medical conditions, or biometric data.

Algorithmic bias also poses a significant challenge. If trained on biased data, AI algorithms can perpetuate discriminatory practices. Artificial intelligence (AI) tools used in employment decision-making cut across the multiple stages of recruitment, and actual and potential bias can arise in each of these stages.

A study found that AI used in asynchronous video interviews (AI-AVI) increased applicants' cognitive trust compared to the non-AI condition. Moreover, when the AI-AVI had features of tangibility and transparency, the applicants’ cognitive and affective trust increased.

For example, Amazon faced criticism for using an AI recruiting tool that showed bias against female candidates. This may be because an algorithm trained on historical hiring data that favours male candidates might continue to do so, hindering diversity efforts. To mitigate this, organisations must ensure their training data is representative and diverse and regularly audit AI systems for biases. This incident also highlights the need for rigorous testing and monitoring of AI systems.

Job displacement is another growing concern. As AI takes over tasks like resume screening and initial candidate selection, human recruiters risk job losses. To address this, organisations should focus on upskilling and reskilling their workforce, enabling them to take on higher-value roles like strategic talent acquisition, candidate experience management, and employer branding.

The Human Touch in the Age of AI Recruiting

While AI offers significant potential to streamline recruitment, it is crucial to recognise its limitations and the ongoing importance of human judgement. Unlike humans, AI relies on set rules and data patterns, often missing subtle nuances in human behaviour. The tone of voice, body language, and emotional cues, essential for assessing candidates, can be overlooked by AI systems.

For instance, imagine an AI system analysing a candidate's interview. While it accurately transcribes the conversation and identifies keywords, it might struggle to differentiate between genuine enthusiasm and excessive flattery or a confident demeanour and arrogance. Additionally, candidates from diverse backgrounds may face challenges in AI interviews due to accent recognition limitations or cultural misunderstandings. On the other hand, humans excel at reading between the lines, picking up on subtle cues, and assessing cultural fit. In short, AI is a tool, not a replacement for human judgement.

To mitigate these risks, a human-in-the-loop approach is essential. Humans should oversee AI-generated insights, ensuring they align with company values and legal requirements. For example, a recruiter could use AI to identify potential candidates and conduct in-depth interviews to assess cultural fit and personality.

Moreover, transparency is vital. Candidates should be informed about the use of AI in the recruitment process. This builds trust and allows candidates to prepare accordingly. For instance, if candidates know an AI system will be analysing their video interview, they can focus on clearly articulating their skills and experience.

A recent study indicating that 49% of job seekers perceive AI recruiting tools as more biased than human interviewers highlights the importance of transparency and trust.

By combining AI's efficiency with humans' empathy and judgement, organisations can create a more equitable and effective recruitment process. It's about finding the right balance between technology and human expertise to build high-performing teams.

Transforming Recruitment with AI: A Focus on AI Interviews

Advanced AI Capabilities

Beyond the foundational applications of AI, we can expect breakthroughs in:

The immersive capabilities of VR and AR are poised to revolutionise recruitment. Imagine candidates taking virtual tours of offices, experiencing simulated job scenarios, or collaborating with remote team members in a shared digital space. For instance, a hospital could use VR to simulate operating room conditions and assess a nurse's performance under pressure.

Beyond recruitment, VR/AR can also enhance training and development. Employees can learn new skills in immersive environments, such as customer service scenarios or complex machinery operations. A study by PwC found that 51% of companies are either in the process of integrating VR into strategy or have already built VR into at least one dedicated line of business.

Other areas include:

• Behavioural and Sentiment Analysis: AI can delve deeper into understanding candidate behaviour and emotions by analysing facial expressions, tone of voice, and language patterns. This can provide insights into a candidate's personality, motivation, and cultural fit. For instance, AI could detect signs of stress or enthusiasm during an interview, providing valuable information for recruiters.
• Improved Natural Language Processing (NLP): Enhanced NLP capabilities will allow AI to understand and interpret human language better, leading to more accurate resume parsing, improved chatbots, and more effective interview analysis. For example, AI could accurately identify skills and experiences mentioned in free-form text resumes.
• Advanced Level Personalisation: AI will tailor the candidate experience to individual preferences and behaviours. This could involve personalised job recommendations, targeted communication, and customised assessment methods. For instance, AI could suggest additional interview questions based on a candidate's previous responses.

AI-Driven Interview Innovations

• Simulated Work Environments: Immersive AI-driven simulations can recreate real-world work scenarios, allowing candidates to demonstrate their problem-solving, decision-making, and teamwork abilities in a controlled setting.
• Data-Driven Insights: By analysing vast amounts of interview data, AI can uncover patterns and trends in candidate performance. This enables recruiters to identify top performers, assess the effectiveness of interview questions, and optimise the hiring process.
• Intelligent Scheduling: AI-powered scheduling tools can optimise interview time slots for candidates and interviewers, considering time zones, candidate availability, and interviewer workload.
• Proactive Bias Mitigation: AI can be employed to identify and address potential biases in interview questions, candidate evaluations, and hiring decisions. For example, AI can flag gendered language in job descriptions or inconsistencies in candidate scoring.
• Interactive Assessments: Gamified assessments and interactive challenges can make the interview more engaging and informative. AI can tailor these experiences to individual candidates based on their skills and interests, providing a more personalised assessment.

The Road Ahead

Developing robust ethical frameworks is essential as AI becomes increasingly integrated into recruitment. This includes establishing guidelines for data privacy, algorithmic fairness, and transparency. Organisations like the Society for Human Resource Management (SHRM) are actively working on developing AI ethics standards.

As AI evolves, it's crucial to balance technological advancements with human judgement and ethical considerations. By combining the strengths of humans and machines, organisations can create a more efficient, fair, and engaging recruitment process.

By embracing these future trends and prioritising ethical considerations, organisations can harness the power of AI to create a more efficient, equitable, and engaging recruitment process. By shifting focus from intuition to data-driven strategies, automating tasks, analysing vast candidate data, and predicting performance, AI enhances efficiency, reduces bias, and optimises talent acquisition. This data-centric approach empowers organisations to make informed decisions, build more robust talent pipelines, and ultimately drive business success.