1|Page
BIAS AND HEALTH EQUITY: THE NURSE’S
RESPONSIBILITY (2026/2027 EDITION)
RATED A+ | 150+ QUESTIONS & ANSWERS
WITH RATIONALES
SECTION 1: FOUNDATIONAL CONCEPTS – AI
& HEALTH EQUITY (12 Q&As)
Q1: What is artificial intelligence (AI) in
healthcare?
A1: Computer systems performing tasks that
normally require human intelligence, including
clinical decision support, predictive analytics,
natural language processing, and robotic
process automation.
Rationale: AI is a tool, not a replacement for
clinical judgment; nurses must understand its
limitations.
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Q2: What is algorithmic bias in healthcare AI?
A2: Systematic, unfair discrimination in AI
outputs due to flawed data collection,
development, or deployment that
disadvantages certain populations based on
race, ethnicity, gender, socioeconomic status, or
other protected characteristics.
Rationale: Bias can be introduced at any stage:
data, algorithm design, or implementation.
Q3: True or False: AI bias is always intentional.
A3: False. Most AI bias is unintentional, arising
from historical data reflecting existing societal
disparities or from technical blind spots in
algorithm design.
Rationale: Unintentional bias is still harmful
and requires mitigation.
Q4: What is the difference between AI bias and
AI error?
A4: Bias is systematic, patterned disadvantage
for specific groups. Error is random inaccuracy
affecting all groups proportionally.
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Rationale: Bias causes health disparities; error
causes general inaccuracy.
Q5: What is health equity according to the
WHO?
A5: Absence of unfair, avoidable, or remediable
differences in health among population groups
defined socially, economically, demographically,
or geographically.
Rationale: Equity ≠ equality (equal treatment
may perpetuate disparities).
Q6: What is the “digital divide” in healthcare AI?
A6: Disparities in access to digital health
technologies, internet connectivity, and digital
literacy that affect which populations benefit
from AI tools.
Rationale: Patients without smartphones or
broadband cannot use AI-powered patient
portals or remote monitoring.
Q7: True or False: AI bias only affects patients,
not healthcare workers.
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A7: False. AI bias can affect nurses (e.g., biased
scheduling algorithms, performance
evaluations) and other clinicians, perpetuating
workforce disparities.
Rationale: Nurses may be underrepresented in
AI training data or misrepresented by biased
algorithms.
Q8: What is “data colonialism” in healthcare AI?
A8: Extraction of health data from marginalized
populations without consent, benefit, or
compensation to train AI systems that primarily
serve privileged groups.
Rationale: Ethical concern raised by global
health scholars.
Q9: What are the three main sources of AI bias
in healthcare?
A9: 1) Data bias (unrepresentative or flawed
training data), 2) Algorithm bias (model design
choices), 3) Implementation bias (how AI is used
in clinical workflows).
Rationale: All three require nursing awareness.
BIAS AND HEALTH EQUITY: THE NURSE’S
RESPONSIBILITY (2026/2027 EDITION)
RATED A+ | 150+ QUESTIONS & ANSWERS
WITH RATIONALES
SECTION 1: FOUNDATIONAL CONCEPTS – AI
& HEALTH EQUITY (12 Q&As)
Q1: What is artificial intelligence (AI) in
healthcare?
A1: Computer systems performing tasks that
normally require human intelligence, including
clinical decision support, predictive analytics,
natural language processing, and robotic
process automation.
Rationale: AI is a tool, not a replacement for
clinical judgment; nurses must understand its
limitations.
,2|Page
Q2: What is algorithmic bias in healthcare AI?
A2: Systematic, unfair discrimination in AI
outputs due to flawed data collection,
development, or deployment that
disadvantages certain populations based on
race, ethnicity, gender, socioeconomic status, or
other protected characteristics.
Rationale: Bias can be introduced at any stage:
data, algorithm design, or implementation.
Q3: True or False: AI bias is always intentional.
A3: False. Most AI bias is unintentional, arising
from historical data reflecting existing societal
disparities or from technical blind spots in
algorithm design.
Rationale: Unintentional bias is still harmful
and requires mitigation.
Q4: What is the difference between AI bias and
AI error?
A4: Bias is systematic, patterned disadvantage
for specific groups. Error is random inaccuracy
affecting all groups proportionally.
,3|Page
Rationale: Bias causes health disparities; error
causes general inaccuracy.
Q5: What is health equity according to the
WHO?
A5: Absence of unfair, avoidable, or remediable
differences in health among population groups
defined socially, economically, demographically,
or geographically.
Rationale: Equity ≠ equality (equal treatment
may perpetuate disparities).
Q6: What is the “digital divide” in healthcare AI?
A6: Disparities in access to digital health
technologies, internet connectivity, and digital
literacy that affect which populations benefit
from AI tools.
Rationale: Patients without smartphones or
broadband cannot use AI-powered patient
portals or remote monitoring.
Q7: True or False: AI bias only affects patients,
not healthcare workers.
, 4|Page
A7: False. AI bias can affect nurses (e.g., biased
scheduling algorithms, performance
evaluations) and other clinicians, perpetuating
workforce disparities.
Rationale: Nurses may be underrepresented in
AI training data or misrepresented by biased
algorithms.
Q8: What is “data colonialism” in healthcare AI?
A8: Extraction of health data from marginalized
populations without consent, benefit, or
compensation to train AI systems that primarily
serve privileged groups.
Rationale: Ethical concern raised by global
health scholars.
Q9: What are the three main sources of AI bias
in healthcare?
A9: 1) Data bias (unrepresentative or flawed
training data), 2) Algorithm bias (model design
choices), 3) Implementation bias (how AI is used
in clinical workflows).
Rationale: All three require nursing awareness.
