Explainable AI in Hiring: Why It Matters

What the evidence says

• ✦Researchers have argued that interpretability should be defined and evaluated rigorously, according to who needs the explanation and for what purpose, rather than assumed. [1]
• ✦Surveys of explainable AI describe a range of techniques and stress that explanations are part of responsible AI practice, supporting trust, fairness assessment and accountability. [2]
• ✦The GDPR gives people rights concerning decisions based solely on automated processing that significantly affect them, and to meaningful information about the logic involved in such processing. [3]
• ✦The EU AI Act sets transparency requirements for high-risk AI systems, a category that includes many AI systems used in recruitment and selection. [4]

AuraSync's interpretation

For a hiring team, the explanation that matters most is the evidence: which answers and signals led to a result. We design for that kind of explanation, because it lets a recruiter check a result, disagree with it and explain the final decision to others.

What AuraSync claims

• ✦Every AuraSync score and recommendation is attached to the evidence behind it.
• ✦Recruiters can open a candidate's underlying answers and materials, and disregard or override any result.
• ✦Candidates can ask for a human to review any AI-generated score or recommendation.

Limitations

Showing evidence explains what a result was based on, not every internal step of a model. Explanations can also invite over-confidence if they are read as proof. Legal summaries here are orientation, not legal advice.

Sources

1. [1]Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv:1702.08608. https://arxiv.org/abs/1702.08608
2. [2]Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
3. [3]Regulation (EU) 2016/679 of the European Parliament and of the Council (General Data Protection Regulation). Official Journal of the European Union. https://eur-lex.europa.eu/eli/reg/2016/679/oj
4. [4]Regulation (EU) 2024/1689 of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union. https://eur-lex.europa.eu/eli/reg/2024/1689/oj

This page summarizes third-party research for orientation. It is not an evaluation of AuraSync, and any summary of law is not legal advice.