Events & Promotions
|
|
GMAT Club Daily Prep
Thank you for using the timer - this advanced tool can estimate your performance and suggest more practice questions. We have subscribed you to Daily Prep Questions via email.
Customized
for You
Track
Your Progress
Practice
Pays
Not interested in getting valuable practice questions and articles delivered to your email? No problem, unsubscribe here.
Apr 2212:30 AM EDT
-01:30 AM EDT
At one point, she believed GMAT wasn’t for her. After scoring 595, self-doubt crept in and she questioned her potential. But instead of quitting, she made the right strategic changes. The result? A remarkable comeback to 695. Check out how Saakshi did it.
Apr 1612:00 PM EDT
-11:59 PM EDT
You’re first to know: Save $200 on GMAT prep starting tomorrow, 4/13. Get 6 official practice tests and study with real questions. Be ready to save!
Apr 2301:30 AM EDT
-02:30 AM EDT
Struggling with GMAT Verbal as a non-native speaker? Harsh improved his score from 595 to 695 in just 45 days—and scored a 99 %ile in Verbal (V88)! Learn how smart strategy, clarity, and guided prep helped him gain 100 points.
Apr 2308:00 PM PDT
-09:00 PM PDT
Video explanations + diagnosis of 10 weakest areas + 150+ short videos + study plan!
Apr 2610:00 AM EDT
-11:00 AM EDT
The Target Test Prep course represents a quantum leap forward in GMAT preparation, a radical reinterpretation of the way that students should study. Try before you buy with a 5-day, full-access trial of the course for FREE!
Apr 2711:00 AM EDT
-12:00 PM EDT
Prefer video-based learning? The Target Test Prep OnDemand course is a one-of-a-kind video masterclass featuring 400 hours of lecture-style teaching by Scott Woodbury-Stewart, founder of Target Test Prep and one of the most accomplished GMAT instructors
Apr 2808:00 AM PDT
-11:00 AM PDT
Whether you are just beginning your MBA journey or fine-tuning your path to a 700+ score, GMAT Day is designed to give you a competitive edge.
29 Sep 2024, 20:05
Kudos
Bookmarks
D
Be sure to select an answer first to save it in the Error Log before revealing the correct answer (OA)!
Difficulty:
(N/A)
Question Stats:
50% (00:11) correct
50%
(03:23)
wrong
based on 2
sessions
History
Date
Time
Result
Not Attempted Yet
Artificial intelligence (AI) advancements are transforming society in ways previously unimaginable, affecting industries such as healthcare, education, and even job selection. As AI increasingly makes decisions once reserved for humans, ethical concerns arise, particularly around the notions of autonomy, explainability, and value alignment. Historically, ethics applied to technology were based on the idea that machines were mere tools for human use. However, as AI systems develop greater autonomy, the ethical framework surrounding these technologies requires reevaluation. Autonomy, which in AI refers to the ability to act independently, is often misunderstood as equivalent to human free will. Yet, AI systems operate within constraints determined by their programming and inputs, raising the question of whether they can ever be truly autonomous in the moral sense.
A second critical issue is the “right to explanation,” which refers to the need for AI systems to offer transparent reasoning behind their decisions. Machine learning models, in particular, often perform at high levels but lack explainability, causing distrust in their outcomes. Addressing this challenge, researchers are working on Explainable AI (XAI), aiming to balance performance with transparency.
Finally, value alignment is another pressing concern. AI systems need to align with human ethical standards, but achieving this is complex due to the potential biases in the data they learn from. Some researchers argue that inverse reinforcement learning, by learning ethical preferences directly from unbiased demonstrations, could counteract the biases that creep in through flawed data. Nevertheless, the question of whether AI systems can ever be true moral agents remains unresolved. While AI may never achieve human-like moral agency, its increasing role in decision-making necessitates the integration of ethical considerations into its design and operation.
Thus, as AI continues to evolve, so must our understanding of its ethical implications and the responsibility of ensuring its alignment with societal values.
If a company develops an AI system that shows biased decision-making due to flawed data inputs, which of the following strategies, inferred from the passage, would be most effective in addressing the issue?
A. Enhance the AI system’s ability to operate autonomously to reduce its dependence on flawed human data inputs.
B. Increase transparency through Explainable AI (XAI) to reveal how the system reaches its decisions and surface potential biases.
C. Prioritize performance optimization of the AI system to outweigh any biases that might arise in specific cases.
D. Apply inverse reinforcement learning to infer human ethical principles from unbiased examples and realign the AI’s decision rules.
E. Remove personally identifying features from all training records before retraining the AI to reduce bias.
A second critical issue is the “right to explanation,” which refers to the need for AI systems to offer transparent reasoning behind their decisions. Machine learning models, in particular, often perform at high levels but lack explainability, causing distrust in their outcomes. Addressing this challenge, researchers are working on Explainable AI (XAI), aiming to balance performance with transparency.
Finally, value alignment is another pressing concern. AI systems need to align with human ethical standards, but achieving this is complex due to the potential biases in the data they learn from. Some researchers argue that inverse reinforcement learning, by learning ethical preferences directly from unbiased demonstrations, could counteract the biases that creep in through flawed data. Nevertheless, the question of whether AI systems can ever be true moral agents remains unresolved. While AI may never achieve human-like moral agency, its increasing role in decision-making necessitates the integration of ethical considerations into its design and operation.
Thus, as AI continues to evolve, so must our understanding of its ethical implications and the responsibility of ensuring its alignment with societal values.
If a company develops an AI system that shows biased decision-making due to flawed data inputs, which of the following strategies, inferred from the passage, would be most effective in addressing the issue?
A. Enhance the AI system’s ability to operate autonomously to reduce its dependence on flawed human data inputs.
B. Increase transparency through Explainable AI (XAI) to reveal how the system reaches its decisions and surface potential biases.
C. Prioritize performance optimization of the AI system to outweigh any biases that might arise in specific cases.
D. Apply inverse reinforcement learning to infer human ethical principles from unbiased examples and realign the AI’s decision rules.
E. Remove personally identifying features from all training records before retraining the AI to reduce bias.
29 Sep 2024, 20:05
Kudos
Bookmarks
Official Solution:
Artificial intelligence (AI) advancements are transforming society in ways previously unimaginable, affecting industries such as healthcare, education, and even job selection. As AI increasingly makes decisions once reserved for humans, ethical concerns arise, particularly around the notions of autonomy, explainability, and value alignment. Historically, ethics applied to technology were based on the idea that machines were mere tools for human use. However, as AI systems develop greater autonomy, the ethical framework surrounding these technologies requires reevaluation. Autonomy, which in AI refers to the ability to act independently, is often misunderstood as equivalent to human free will. Yet, AI systems operate within constraints determined by their programming and inputs, raising the question of whether they can ever be truly autonomous in the moral sense.
A second critical issue is the “right to explanation,” which refers to the need for AI systems to offer transparent reasoning behind their decisions. Machine learning models, in particular, often perform at high levels but lack explainability, causing distrust in their outcomes. Addressing this challenge, researchers are working on Explainable AI (XAI), aiming to balance performance with transparency.
Finally, value alignment is another pressing concern. AI systems need to align with human ethical standards, but achieving this is complex due to the potential biases in the data they learn from. Some researchers argue that inverse reinforcement learning, by learning ethical preferences directly from unbiased demonstrations, could counteract the biases that creep in through flawed data. Nevertheless, the question of whether AI systems can ever be true moral agents remains unresolved. While AI may never achieve human-like moral agency, its increasing role in decision-making necessitates the integration of ethical considerations into its design and operation.
Thus, as AI continues to evolve, so must our understanding of its ethical implications and the responsibility of ensuring its alignment with societal values.
If a company develops an AI system that shows biased decision-making due to flawed data inputs, which of the following strategies, inferred from the passage, would be most effective in addressing the issue?
A. Enhance the AI system’s ability to operate autonomously to reduce its dependence on flawed human data inputs.
B. Increase transparency through Explainable AI (XAI) to reveal how the system reaches its decisions and surface potential biases.
C. Prioritize performance optimization of the AI system to outweigh any biases that might arise in specific cases.
D. Apply inverse reinforcement learning to infer human ethical principles from unbiased examples and realign the AI’s decision rules.
E. Remove personally identifying features from all training records before retraining the AI to reduce bias.
A. Incorrect. The passage never links autonomy to removing bias in flawed inputs. The author’s discussion of autonomy centers on moral agency, not data quality.
B. Incorrect. The passage presents Explainable AI as a way to reveal how decisions are made and surface potential biases, but it does not claim that transparency alone eliminates or corrects biased data sources—leaving the core problem unresolved.
C. Incorrect. Optimizing performance while ignoring ethical concerns is actually cautioned against in the passage; prioritizing accuracy “to outweigh” bias contradicts the author’s call for balancing performance with ethical safeguards.
D. Correct. The passage in fact proposes inverse reinforcement learning as a means to learn human ethical preferences from unbiased demonstrations, thereby realigning the AI’s decision rules and counteracting bias introduced by flawed data.
E. Incorrect. Removing personally identifying features is a common technical mitigation, but the passage never discusses data anonymization as a bias-reduction strategy. Because it is not advocated in the text, it cannot be the most effective passage-based remedy.
Answer: D
Artificial intelligence (AI) advancements are transforming society in ways previously unimaginable, affecting industries such as healthcare, education, and even job selection. As AI increasingly makes decisions once reserved for humans, ethical concerns arise, particularly around the notions of autonomy, explainability, and value alignment. Historically, ethics applied to technology were based on the idea that machines were mere tools for human use. However, as AI systems develop greater autonomy, the ethical framework surrounding these technologies requires reevaluation. Autonomy, which in AI refers to the ability to act independently, is often misunderstood as equivalent to human free will. Yet, AI systems operate within constraints determined by their programming and inputs, raising the question of whether they can ever be truly autonomous in the moral sense.
A second critical issue is the “right to explanation,” which refers to the need for AI systems to offer transparent reasoning behind their decisions. Machine learning models, in particular, often perform at high levels but lack explainability, causing distrust in their outcomes. Addressing this challenge, researchers are working on Explainable AI (XAI), aiming to balance performance with transparency.
Finally, value alignment is another pressing concern. AI systems need to align with human ethical standards, but achieving this is complex due to the potential biases in the data they learn from. Some researchers argue that inverse reinforcement learning, by learning ethical preferences directly from unbiased demonstrations, could counteract the biases that creep in through flawed data. Nevertheless, the question of whether AI systems can ever be true moral agents remains unresolved. While AI may never achieve human-like moral agency, its increasing role in decision-making necessitates the integration of ethical considerations into its design and operation.
Thus, as AI continues to evolve, so must our understanding of its ethical implications and the responsibility of ensuring its alignment with societal values.
If a company develops an AI system that shows biased decision-making due to flawed data inputs, which of the following strategies, inferred from the passage, would be most effective in addressing the issue?
A. Enhance the AI system’s ability to operate autonomously to reduce its dependence on flawed human data inputs.
B. Increase transparency through Explainable AI (XAI) to reveal how the system reaches its decisions and surface potential biases.
C. Prioritize performance optimization of the AI system to outweigh any biases that might arise in specific cases.
D. Apply inverse reinforcement learning to infer human ethical principles from unbiased examples and realign the AI’s decision rules.
E. Remove personally identifying features from all training records before retraining the AI to reduce bias.
A. Incorrect. The passage never links autonomy to removing bias in flawed inputs. The author’s discussion of autonomy centers on moral agency, not data quality.
B. Incorrect. The passage presents Explainable AI as a way to reveal how decisions are made and surface potential biases, but it does not claim that transparency alone eliminates or corrects biased data sources—leaving the core problem unresolved.
C. Incorrect. Optimizing performance while ignoring ethical concerns is actually cautioned against in the passage; prioritizing accuracy “to outweigh” bias contradicts the author’s call for balancing performance with ethical safeguards.
D. Correct. The passage in fact proposes inverse reinforcement learning as a means to learn human ethical preferences from unbiased demonstrations, thereby realigning the AI’s decision rules and counteracting bias introduced by flawed data.
E. Incorrect. Removing personally identifying features is a common technical mitigation, but the passage never discusses data anonymization as a bias-reduction strategy. Because it is not advocated in the text, it cannot be the most effective passage-based remedy.
Answer: D
24 Jun 2025, 23:01
Kudos
Bookmarks
Hi!
I stuck on this for a long time and answer D feels a little bit odd.
I see two problems:
1) The passage discusses inverse reinforcement learning (IRL) in the context of value alignment, not as a method for correcting flawed data inputs. Value alignment refers to aligning AI systems with human ethical standards, whereas the question focuses specifically on biased decision-making resulting from problematic data. To me these are related but distinct concepts, and the passage does not suggest that IRL is used to correct or clean biased data.
2) The passage presents IRL as a theoretical possibility rather than an established solution. It says "some researchers argue... could be a solution" and immediately follows with "the question of whether AI systems can ever be true moral agents remains unresolved." To me that conveys a tentative and speculative view on IRL. As such, IRL is presented as a potential approach under investigation, not as a proven or reliable fix for current problems like biased inputs. Choosing it as the most effective solution over more grounded options seems not justified
I stuck on this for a long time and answer D feels a little bit odd.
I see two problems:
1) The passage discusses inverse reinforcement learning (IRL) in the context of value alignment, not as a method for correcting flawed data inputs. Value alignment refers to aligning AI systems with human ethical standards, whereas the question focuses specifically on biased decision-making resulting from problematic data. To me these are related but distinct concepts, and the passage does not suggest that IRL is used to correct or clean biased data.
2) The passage presents IRL as a theoretical possibility rather than an established solution. It says "some researchers argue... could be a solution" and immediately follows with "the question of whether AI systems can ever be true moral agents remains unresolved." To me that conveys a tentative and speculative view on IRL. As such, IRL is presented as a potential approach under investigation, not as a proven or reliable fix for current problems like biased inputs. Choosing it as the most effective solution over more grounded options seems not justified
Thank you and I have added 2 weeks of 
