D333 Task 1: Ethical Implications of AI in Hiring Practices
William Sanders
Professor Rob Przygrodzki
D333 Ethics in Technology
July 7, 2026
A.
1. A major corporation implemented an AI-driven system to streamline its hiring process.
The AI was programmed to analyze resumes and assess candidate suitability based on
criteria like education, experience, and skill set. However, the system consistently failed
to provide a fair and equal opportunity to all candidates. It couldn't properly
accommodate individuals with disabilities who required assistive technology or
alternative resume formats. As a result, the AI automatically filtered out these candidates,
even if they were highly qualified for the position. The company received numerous
complaints from advocacy groups highlighting this lack of fairness, yet the HR team
continued to rely on the AI system without any adjustments, effectively denying these
individuals an equitable chance at employment (Burton et al., 2023).
2. The company faces significant legal repercussions, including lawsuits, fines, and
compliance orders, particularly under anti-discrimination laws like the Americans with
Disabilities Act (ADA) in the U.S. These legal challenges lead to costly legal fees,
settlements, and lost productivity. Beyond the financial strain, the company misses out on
a diverse pool of highly qualified candidates who could offer valuable skills,
D333 Task 1: Ethical Implications of AI in Hiring Practices
, D333 Task 1: Ethical Implications of AI in Hiring Practices
perspectives, and innovation, ultimately limiting its potential for growth and problem-
solving. For individuals, inaccessible systems unfairly filter out qualified candidates,
preventing them from even being considered for jobs they could excel at. This directly
limits their career progression and economic opportunities. Being denied employment
due to such a system can result in financial instability and hardship for individuals and
their families. Broadly, this contributes to increased inequality, reduced economic
participation, and a growing distrust in AI technologies (Burton et al., 2023).
3. Because the AI is likely trained on a narrow set of standard resume formats, it lacks the
data and programming to accurately parse and understand information presented through
assistive technology or in alternative layouts. It might see these formats as unreadable or
incomplete data, leading it to automatically filter out qualified candidates. The criteria
like education, experience, and skill set are not being properly extracted for these
individuals, even if they possess them. The AI's definition of suitability is directly derived
from its training data. If that data doesn't include examples of successful employees who
also use assistive technology or alternative resume formats, the AI will inaccurately deem
candidates with these needs as unsuitable, regardless of their actual qualifications. The
HR team's continued reliance on the flawed AI without adjustments demonstrates a
severe lack of a feedback mechanism to correct data inaccuracies or algorithmic biases.
They are not incorporating the complaints from advocacy groups into the AI's learning or
adjustment process, which would be crucial for improving data accuracy and fairness
(Burton et al., 2023).
D333 Task 1: Ethical Implications of AI in Hiring Practices
William Sanders
Professor Rob Przygrodzki
D333 Ethics in Technology
July 7, 2026
A.
1. A major corporation implemented an AI-driven system to streamline its hiring process.
The AI was programmed to analyze resumes and assess candidate suitability based on
criteria like education, experience, and skill set. However, the system consistently failed
to provide a fair and equal opportunity to all candidates. It couldn't properly
accommodate individuals with disabilities who required assistive technology or
alternative resume formats. As a result, the AI automatically filtered out these candidates,
even if they were highly qualified for the position. The company received numerous
complaints from advocacy groups highlighting this lack of fairness, yet the HR team
continued to rely on the AI system without any adjustments, effectively denying these
individuals an equitable chance at employment (Burton et al., 2023).
2. The company faces significant legal repercussions, including lawsuits, fines, and
compliance orders, particularly under anti-discrimination laws like the Americans with
Disabilities Act (ADA) in the U.S. These legal challenges lead to costly legal fees,
settlements, and lost productivity. Beyond the financial strain, the company misses out on
a diverse pool of highly qualified candidates who could offer valuable skills,
D333 Task 1: Ethical Implications of AI in Hiring Practices
, D333 Task 1: Ethical Implications of AI in Hiring Practices
perspectives, and innovation, ultimately limiting its potential for growth and problem-
solving. For individuals, inaccessible systems unfairly filter out qualified candidates,
preventing them from even being considered for jobs they could excel at. This directly
limits their career progression and economic opportunities. Being denied employment
due to such a system can result in financial instability and hardship for individuals and
their families. Broadly, this contributes to increased inequality, reduced economic
participation, and a growing distrust in AI technologies (Burton et al., 2023).
3. Because the AI is likely trained on a narrow set of standard resume formats, it lacks the
data and programming to accurately parse and understand information presented through
assistive technology or in alternative layouts. It might see these formats as unreadable or
incomplete data, leading it to automatically filter out qualified candidates. The criteria
like education, experience, and skill set are not being properly extracted for these
individuals, even if they possess them. The AI's definition of suitability is directly derived
from its training data. If that data doesn't include examples of successful employees who
also use assistive technology or alternative resume formats, the AI will inaccurately deem
candidates with these needs as unsuitable, regardless of their actual qualifications. The
HR team's continued reliance on the flawed AI without adjustments demonstrates a
severe lack of a feedback mechanism to correct data inaccuracies or algorithmic biases.
They are not incorporating the complaints from advocacy groups into the AI's learning or
adjustment process, which would be crucial for improving data accuracy and fairness
(Burton et al., 2023).
D333 Task 1: Ethical Implications of AI in Hiring Practices
