The Rise and Risks of Using Emotion Detection in Interviews

Human resource functions are rapidly turning to advanced technologies to boost efficiency. This shift has led to the adoption of software and tools in every sphere of HR management, from recruitment to retention. Emotion recognition technology is an innovative and novel AI-driven tool that aims to decode human expressiveness, body language, tone of voice, and even microexpressions to evaluate candidates' soft skills and personality traits more effectively during job interviews.

Emotion detection applications are powered by facial recognition AI and leveraged in remote recruitments where digital hiring and video interviews are the norm. They are primarily designed to reduce and eliminate implicit biases and ensure fairness in the hiring process.

However, as biometric surveillance gains traction across the hiring process, especially in virtual interviews, its accuracy, potential for bias, lack of consent, and legal risks have sparked conversations. Its applications and challenges require a closer look by HR professionals and corporate leaders, which this article intends to provide.

What is Emotion Detection in Interviews?

Emotion detection in interviews involves using AI-powered tools to analyze facial expressions, eye movement, tone of voice, and microexpressions. The aim is to identify emotional states like anxiety, enthusiasm, and stress during interviews, both in person and otherwise.

Emotion recognition technology highlights the broader shift toward artificial intelligence across the HR decision-making process, whether it involves incorporating AI technology to predict employability scores, predictive analytics to reveal turnover risk, artificial intelligence (AI) in HR workplace surveillance, or, in this case, facial recognition AI to assess candidature.

Benefits of Emotion Detection in Interviews

Emotion detection during the virtual hiring process can reimagine how hiring managers assess candidate capability.

     1. Efficient use of time and resources

Emotion recognition software can automate the pre-filtering process of qualified candidates. Traditional screening methods may be time-consuming and often ineffective for roles that attract applicants in substantial volumes. However, AI-powered emotion detection technology can be particularly advantageous in performing administrative tasks common in the initial sourcing to the screening process. It can help recruiters to accelerate the hiring process and focus on the high-potential applicants who fit the job description.

    2. Mitigation of internal bias

Emotion recognition tools, structured interview questions, and predefined hiring benchmarks can reduce implicit human biases, such as first impression bias or halo effects.

Many business leaders and human resource professionals believe that AI-supported tools are useful in improving objective analysis when shortlisting candidates.

    3. Standardization of interviews

Employee monitoring tools and analytics allow companies to ensure that hiring standards are uniformly applied. They consistently evaluate every applicant, supporting fairness and compliance in regulated industries.

4. Enhanced candidate assessment

Emotion recognition technology offers a more holistic view of the candidate. It may allow HR to uncover inconsistencies between verbal statements and non-verbal cues. For instance, during follow-ups in behavioral interviews where complex questions surface, recruiters may be able to evaluate how well a candidate holds up under stress by analyzing facial expressions, gestures, signs of nervousness, etc.

Challenges in Using Emotion Recognition Technology

Since emotions are complex and context-dependent, it may be difficult to quantify them into algorithms, posing challenges.

1. Cultural and personal variables

Cultural norms, personal values, and identities may influence how individuals express emotions. Introverted applicants may naturally exhibit fewer facial expressions and non-verbal cues than extroverted individuals. Similarly, people's emotional expression across different ages, genders, and geographical demographics can drastically vary, making facial and emotion detection software unreliable. These tools may misinterpret emotions and traits and often misidentify individuals, leading to ethical concerns and algorithmic biases towards certain demographics.

    2. Overreliance on technology

Overusing technology poses a risk of employers excessively relying on or trusting automated evaluations. This is especially true in larger workplace surveillance systems, where it may be easy to overlook the faults of AI evaluations, leading to oversights, particularly if hiring managers are not trained in interpreting AI-generated emotional data.

    3. Potential risks of emotion detection

There may be potential privacy, consent-related, and legal risks of using emotion detection in interviews:

- Privacy and consent: The most concerning aspect of emotion recognition is the privacy intrusion into the candidate's biometric data. The technology captures voice, facial expressions, and nonverbal cues as part of the biometric surveillance. In many jurisdictions, these are subject to strict regulations and requirements. For instance, the European Union's general data protection regulations (GDPR) classify biometric data as sensitive personal data, which requires explicit consent and data protection protocols. 

- Legal risks: Using emotion recognition technology without clear consent and disclosure puts the company's reputation at risk of facing legal challenges. In 2021, Meta was ordered to pay $650 million to settle a class-action lawsuit for violating the Illinois Biometric Information Privacy Act (BIPA) by using facial recognition without proper consent. Companies must consider the legal risks of implementing emotion recognition technology to prevent damage to their reputation or employer brand.

    4. Discrimination and algorithmic bias

Facial recognition AI can produce skewed and incorrect outcomes if algorithms are not trained on diverse data sets, for instance, in people of color or women. Research by MIT and Stanford University shows evidence of gender and skin-type bias in facial recognition systems, with error rates for darker-skinned women exceeding 34.7% compared to 0.8% for light-skinned men.

Understandably, AI-driven facial tools carry the risk of misidentification or racial profiling in minority communities and underrepresented groups. This failure to handle the diversities of human facial features could lead to rejections due to unconscious bias, further exacerbating existing inequalities in the hiring process.

    4. Misinterpretation of data

Emotional data is interpretive. For example, a furrowed brow might express confusion, but AI might interpret it as disagreement. Relying on data provided by emotion detection technologies alone without human input can lead to flaws and inaccuracies in judgments regarding candidates’ motivations and feelings.

How to Effectively and Ethically Use AI Emotion Detection in Interviews?

Here's how hiring managers and recruiters may leverage emotion detection effectively:

1. Transparent communication

Companies that implement emotion detection software during interviews should ensure they inform candidates and seek consent to do so. They should proactively share details about what kind of data will be collected, how it will be analyzed, and how long it will be retained. Company leaders ensuring transparency about their decision to leverage emotion recognition technology can help build trust and protect them from legal repercussions.

    2. Ethical audits of AI

Organizations may conduct periodic audits to evaluate the effectiveness and fairness of their biometric surveillance systems. Outsourcing this to an independent third party skilled at auditing AI algorithms may be a strategic decision to mitigate algorithmic bias.

    3. Human-in-the-loop systems

AI technologies should complement human decision-making, not replace it. Hiring managers should be given in-depth training to contextualize and interpret emotional data so they know when to rely on it and when not to consider it.

AI should be tactically used as a supportive tool for making decisions rather than to make decisions.

    4. Compliance with data protection laws

Organizations that use emotion detection technology should ensure that they align with emerging data privacy laws, such as India’s Digital Personal Data Protection Act (DPDP Act), 2023, and similar regional frameworks like GDPR and CCPA. Legal and compliance teams may be implemented to prevent penalties and errors.

    5. Limiting the scope of AI

Strict boundaries should be established to define what data is collected and why. Limiting the analysis to general engagement levels rather than in-depth emotional analysis can help reduce invasiveness.

Conclusion

Integrating emotion regulation technology into the hiring process, especially when deployed responsibly, can serve as a strategic tool to enhance hiring practices. It can be used for candidate evaluation, streamlining recruitment, and finding the right cultural fit. 

The success of emotion detection technology ultimately lies in the integrity of the application. Employers should proactively prioritize fairness and respect in their usage to ensure privacy and security standards are upheld.