Mary Jane O.
Reyes
BSN 2-1
Title: Ethical Implications of AI in Hiring: A Case Study
1. What is the case about?
This case study explores the ethical complexities that arise when Artificial
Intelligence (AI) technologies are integrated into recruitment practices. It draws upon
findings by Dr. Natalie Sheard of the University of Melbourne, whose research
demonstrates how AI-powered hiring systems can unintentionally discriminate
against candidates with accents or speech-related impairments. Many of these tools
are trained using homogenous datasets, often heavily centered around American
English. As a result, they struggle with speech variations, producing high
transcription error rates up to 22% in some cases particularly affecting non-native
speakers. These inaccuracies can misrepresent applicants' communication skills or
qualifications, leading to unfair rejection from job opportunities. The case highlights
the critical gap between technological capability and ethical responsibility in modern
hiring.
2. What positive and negative societal impacts are mentioned?
Positive Impacts:
Improved Hiring Efficiency - AI can expedite the recruitment process by evaluating
numerous applications quickly, reducing the time and costs associated with
traditional hiring methods.
Negative Impacts:
Unfair Biasb- Applicants with foreign accents or speech impairments may be judged
inaccurately due to the AI's misinterpretations, which can lead to unfair exclusion.
Opacity in Decision-Making - The internal processes of many AI tools remain
unclear, making it hard for employers and applicants to understand how hiring
decisions are made.
Legal and Moral Issues - When AI systems produce biased outcomes, they can
lead to lawsuits and moral scrutiny, especially if deserving candidates are wrongly
dismissed.
3. Were ethical issues addressed?
Yes, several ethical concerns are highlighted:
Algorithmic Bias: AI systems trained on narrow datasets may continue or even
deepen existing social biases.
Lack of Clarity and Accountability: Because AI decision-making is often opaque,
it's difficult to review or audit hiring outcomes for fairness.
Need for Regulation: The study recommends the development of targeted AI
regulations and enhanced anti-discrimination protections to prevent systemic bias in
recruitment.
4. Summary of Findings and Key Insights
While AI offers efficiency benefits in hiring, it also introduces ethical risks that must
not be overlooked.
The study emphasizes the importance of
Inclusive Training Data: AI models should be developed using speech and
language samples from diverse populations.
Transparent Algorithms: Systems must be designed so their decision-making
processes can be understood and evaluated by humans.
Robust Legal Guidelines: Establishing clear legislation to govern the responsible
use of AI in recruitment is essential to prevent unfair treatment of candidates.
Reflection:
The case brings to light the paradox of using AI in hiring: while it promises speed and
consistency, it can also deepen existing inequities if not carefully designed and
managed. Technologies that aim to reduce bias can inadvertently reinforce it when
built on narrow datasets or without sufficient human oversight. As we move toward
increasing digitalization of workplace functions, it becomes essential to embed ethics
at every stage of AI development and deployment. Ensuring equity in hiring is not
just a technical challenge, it is a societal responsibility that demands input from
technologists, ethicists, lawmakers, and employers alike. The future of work must
prioritize inclusion and fairness, ensuring technology serves to uplift rather than
exclude.
Reference
The Guardian. (2025, May 14). People interviewed by AI for jobs face discrimination
risks, Australian study warns.Retrieved from The Guardian
