How to counter discrimination in AI-powered recruiting software.

Algorithmic discrimination in hiring is a lose-lose scenario for all parties involved

Introduction

Discrimination can occur at each stage of hiring processes

Sources of bias and other challenges

Algorithmic discrimination in hiring is real – and significantly affects real people

Experiences of navigating opaque hiring processes

EU Non-Discrimination law: important safeguards as a starting point

A long tradition that is relevant for algorithmic discrimination

Providers of high-risk AI hiring tools bear special responsibility

GDPR: Every processing of personal data needs careful legal assessment

The General Data Protection Regulation and personal data

Detecting discrimination in AI systems by using sensitive data – a necessity that poses a dilemma

Stakeholder mapping makes processes clear and straightforward

Defining roles, responsibilities and impact for better collaboration

UI/UX Design: Show less personal information on interfaces – and show it at later stages

Designing fair and inclusive interfaces

XAI: Explaining algorithmic suggestions is key to better recruitment

Guidelines for explainability and transparency

Do not rely on module-level tests only – consider real-world settings

The importance of end-to-end, socio-technical audits

Auditing: Thorough documentation and analysis across the AI lifecycle helps prevent bias

Fairness-enhancing interventions in the ranking pipeline can reduce ranking bias

Comprehensive evaluation and monitoring before and after deployment is key

Overview: FINDHR tools to counter discrimination in AI-assisted hiring

Glossary

Sources

Credits

FINDHR Mission Statement