A businessman holds a briefcase behind his back.

The Ethics of AI in Hiring Decisions

1. Warm-Up Questions

Should companies use AI to screen job applicants? Why or why not?
Is AI more objective than human recruiters, or can it reinforce bias?
Should candidates have the right to know if AI was used to evaluate their application?
Can AI ever truly assess soft skills and cultural fit?

2. Vocabulary Preparation

algorithmic bias
predictive hiring
candidate profiling
ethics compliance
data-driven recruitment
disparate impact
human oversight
automated assessment

A. Evaluation of applicants using statistical models
B. Discrimination that results unintentionally from a policy or algorithm
C. Monitoring AI systems to ensure fairness
D. Ethical rules governing business practices
E. Use of automated systems to assess skills and performance
F. Creation of applicant profiles based on data patterns
G. Predicting job success using AI models
H. Unfair patterns emerging from biased algorithms

Fun Vocabulary Game: "Fair or Foul?"

Choose the correct term:

If an AI rejects applicants from certain groups more often, it shows (algorithmic bias / algorithmic bliss).
Evaluating resumes purely using AI is an example of (data-driven recruitment / emotion-driven recruitment).
Ensuring that AI follows legal and ethical standards is (ethics compliance / ethics defiance).

3. Reading Article (≈650 words)

Hiring in the Age of AI – Ethics, Efficiency, and Equity

In the last decade, artificial intelligence has emerged as a central tool in recruitment, promising efficiency, objectivity, and predictive accuracy. Companies worldwide are adopting AI-driven platforms to scan resumes, assess candidates’ skills, and even evaluate personality traits through video interviews. Proponents argue that AI reduces human error and unconscious bias, yet critics warn that it can also perpetuate and amplify inequities.

A 2025 survey by the Society for Human Resource Management found that 62% of Fortune 500 companies now use some form of AI in hiring, ranging from resume screening to automated skill assessments. Supporters claim AI allows organizations to process large volumes of applications more efficiently, eliminating bottlenecks and increasing speed-to-hire. They cite examples where AI successfully identified highly qualified candidates who might have been overlooked by traditional recruiters.

However, numerous studies highlight ethical challenges. Algorithmic bias remains a significant concern. If AI is trained on historical hiring data, it may reflect and reinforce existing workplace inequalities. Research from MIT in 2024 revealed that certain AI recruitment tools disproportionately flagged women and minority candidates as less suitable, even when their qualifications were equivalent to other applicants. The risk of disparate impact—where policies or algorithms unintentionally disadvantage specific groups—has become a focal point of legal and academic debates.

Human oversight is often recommended as a safeguard. Companies are encouraged to combine AI assessment with human review to ensure fairness, transparency, and accountability. Yet, even when humans are involved, the opacity of algorithms can make it difficult to understand how decisions are made. Candidates may not know why they were rejected or what data influenced the outcome, raising questions about procedural fairness.

Ethics compliance is another emerging priority. Regulatory frameworks in the EU, the US, and other regions are evolving to ensure that AI in hiring adheres to anti-discrimination laws. The European Commission has proposed mandatory audits of AI recruitment tools, requiring companies to document how decisions are made and to demonstrate that algorithms do not produce biased outcomes.

Despite these measures, the debate continues. Critics argue that over-reliance on AI can reduce opportunities for human judgment, empathy, and nuanced evaluation of cultural fit. Others counter that AI can identify patterns invisible to humans and make hiring more meritocratic. Companies are thus challenged to balance efficiency with fairness, ensuring that AI serves as a tool for inclusion rather than exclusion.

Ultimately, the ethical use of AI in hiring depends on conscious design, continuous monitoring, and transparency. Organizations that proactively audit their algorithms, involve diverse teams in model development, and disclose assessment criteria are better positioned to gain public trust and attract top talent. As AI technology evolves, the conversation about fairness, equity, and efficiency in recruitment will remain both urgent and unresolved.

4. Grammar Focus (Advanced)

A. Nominalisation

Rewrite the sentences using nominalisation:

Companies often reject applicants unfairly.
AI systems process resumes rapidly.
Managers monitor algorithm performance.
HR departments conduct interviews.
Candidates submit applications online.
Employees provide feedback about hiring tools.

B. Mixed Conditionals

Complete the sentences:

If companies had audited their AI tools, bias ___ (be) reduced.
Had applicants been informed about AI screening, they ___ (prepare) differently.
If HR teams monitored algorithms closely, fairness ___ (improve).
Should regulations require transparency, trust ___ (increase).
If training data were unbiased, hiring outcomes ___ (be) more equitable.
Were AI completely unregulated, legal challenges ___ (multiply).