5 Common AI Phone Screening Mistakes That Hurt Diversity

As of February 2026, a staggering 68% of organizations report struggling to meet their diversity hiring goals. Despite the promises of artificial intelligence (AI) in streamlining recruitment processes, many companies inadvertently create barriers to diverse talent during the phone screening stage. The stakes are high; the implications of these mistakes can hinder not only candidate diversity but also overall organizational performance and innovation. Here, we delve into the five most common AI phone screening mistakes that can negatively impact diversity efforts and provide actionable insights to avoid them.

1. Over-Reliance on Historical Data

AI systems learn from historical data, but when that data reflects past biases, the outcome is perpetuated discrimination. For instance, if a company has historically favored candidates from specific universities or demographics, the AI will likely prioritize similar profiles, sidelining diverse candidates.

Solution: Regularly audit the data fed into your AI systems to ensure it is reflective of your diversity goals. Incorporating data from a wider array of universities and backgrounds can help mitigate this risk.

2. Lack of Multilingual Support

In a globalized job market, organizations often overlook the importance of multilingual capabilities in AI phone screening. Failing to provide language options can alienate non-native speakers, reducing their chances of success.

Solution: Choose an AI phone screening solution like NTRVSTA, which offers support in 9+ languages, ensuring inclusivity for candidates from different linguistic backgrounds. This can significantly increase candidate completion rates, which currently average 95% for NTRVSTA, compared to 40-60% for video interviews.

3. Ignoring Candidate Experience

Many AI systems prioritize efficiency over candidate experience. A rigid, impersonal phone screening process can discourage diverse candidates who may already feel marginalized in a competitive job market.

Solution: Design your screening process to be candidate-friendly. Use AI to personalize interactions, such as addressing candidates by name and providing constructive feedback. This human touch can improve engagement and completion rates.

4. Inadequate Bias Detection Measures

AI systems can inadvertently reinforce bias if they lack robust fraud detection and bias mitigation algorithms. For example, if an AI system scores resumes based on biased criteria, it may unintentionally favor certain demographics over others.

Solution: Implement AI solutions with built-in bias detection capabilities. NTRVSTA's AI resume scoring includes fraud detection features that help identify fake credentials, ensuring a fairer assessment of all candidates.

5. Not Aligning with Compliance Standards

Diversity hiring practices must also align with legal standards and regulations. Companies often overlook compliance with local laws or industry standards, which can lead to discriminatory practices during the screening process.

Solution: Ensure your AI phone screening tool is compliant with regulations such as GDPR and NYC Local Law 144. This compliance not only protects your organization but also demonstrates your commitment to fair hiring practices.

Conclusion

Addressing these five common AI phone screening mistakes is crucial for organizations aiming to enhance diversity in their hiring processes. Here are three actionable takeaways:

1. Audit Your Data: Regularly review the data feeding into your AI systems to eliminate biases and reflect your diversity goals.
2. Enhance Candidate Experience: Opt for AI solutions that personalize candidate interactions and support multiple languages to improve engagement.
3. Implement Robust Bias Detection: Choose AI tools that offer advanced bias detection and fraud prevention to ensure equitable candidate assessment.

By taking proactive steps to rectify these mistakes, organizations can foster a more inclusive hiring environment, ultimately driving better business outcomes.