AI and Human Resources Report
A closed roundtable held on 5 June 2026 with more than 20 participants examines, through examples from the field, the questions AI raises in human resources: from algorithmic bias to shadow AI and from entry-level jobs to the digital workforce. Three core risks stood out in the session: data leakage through shadow AI, algorithmic bias rooted in historical data, and a talent pipeline weakened as entry-level roles are automated.
What is in the report?
Algorithmic Bias and Male-Skewed Datasets
Trained on historical data, AI can copy and amplify past discrimination and biases in hiring interviews and candidate screening; participants described algorithmic discrimination based on name or ethnicity as a growing problem in the sector.
Data Privacy, Shadow AI and Cost
Employees copying company and personnel data into public language models was rated one of the most important cybersecurity and KVKK risks. In-house models that keep data in Türkiye ease compliance, but GPU rental costs are a heavy burden.
Entry-Level Jobs and Education
As AI begins to take over entry- and mid-level work such as reporting, data analysis and coding, the “apprenticeship” ground on which new graduates develop is narrowing. The concern raised most at the table: if AI does this work, how will tomorrow's managers be developed?
Human and Digital Workforce
Participants agreed that organizations have begun moving from transactional HR management toward “Workforce Architecture”. Yet time saved through AI does not always become added value; this “Productivity Trap” can lead to demotivation and aimlessness.