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On Wed, 2 Apr, 8:02 AM UTC
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[1]
AI Thinks Like Us: Flaws, Biases, and All, Study Finds - Neuroscience News
Summary: A new study finds that ChatGPT, while excellent at logic and math, exhibits many of the same cognitive biases as humans when making subjective decisions. In tests for common judgment errors, the AI showed overconfidence, risk aversion, and even the classic gambler's fallacy, though it avoided other typical human mistakes like base-rate neglect. Interestingly, newer versions of the AI were more analytically accurate but also displayed stronger judgment-based biases in some cases. These findings raise concerns about relying on AI for high-stakes decisions, as it may not eliminate human error but instead automate it. Can we really trust AI to make better decisions than humans? A new study says ... not always. Researchers have discovered that OpenAI's ChatGPT, one of the most advanced and popular AI models, makes the same kinds of decision-making mistakes as humans in some situations - showing biases like overconfidence of hot-hand (gambler's) fallacy - yet acting inhuman in others (e.g., not suffering from base-rate neglect or sunk cost fallacies). Published in the INFORMS journal Manufacturing & Service Operations Management, the study reveals that ChatGPT doesn't just crunch numbers - it "thinks" in ways eerily similar to humans, including mental shortcuts and blind spots. These biases remain rather stable across different business situations but may change as AI evolves from one version to the next. AI: A Smart Assistant with Human-Like Flaws The study, "A Manager and an AI Walk into a Bar: Does ChatGPT Make Biased Decisions Like We Do?," put ChatGPT through 18 different bias tests. The results? Why This Matters From job hiring to loan approvals, AI is already shaping major decisions in business and government. But if AI mimics human biases, could it be reinforcing bad decisions instead of fixing them? "As AI learns from human data, it may also think like a human - biases and all," says Yang Chen, lead author and assistant professor at Western University. "Our research shows when AI is used to make judgment calls, it sometimes employs the same mental shortcuts as people." The study found that ChatGPT tends to: "When a decision has a clear right answer, AI nails it - it is better at finding the right formula than most people are," says Anton Ovchinnikov of Queen's University. "But when judgment is involved, AI may fall into the same cognitive traps as people." So, Can We Trust AI to Make Big Decisions? With governments worldwide working on AI regulations, the study raises an urgent question: Should we rely on AI to make important calls when it can be just as biased as humans? "AI isn't a neutral referee," says Samuel Kirshner of UNSW Business School. "If left unchecked, it might not fix decision-making problems - it could actually make them worse." The researchers say that's why businesses and policymakers need to monitor AI's decisions as closely as they would a human decision-maker. "AI should be treated like an employee who makes important decisions - it needs oversight and ethical guidelines," says Meena Andiappan of McMaster University. "Otherwise, we risk automating flawed thinking instead of improving it." What's Next? The study's authors recommend regular audits of AI-driven decisions and refining AI systems to reduce biases. With AI's influence growing, making sure it improves decision-making - rather than just replicating human flaws - will be key. "The evolution from GPT-3.5 to 4.0 suggests the latest models are becoming more human in some areas, yet less human but more accurate in others," says Tracy Jenkin of Queen's University. "Managers must evaluate how different models perform on their decision-making use cases and regularly re-evaluate to avoid surprises. Some use cases will need significant model refinement." A Manager and an AI Walk into a Bar: Does ChatGPT Make Biased Decisions Like We Do? Problem definition: Large language models (LLMs) are being increasingly leveraged in business and consumer decision-making processes. Because LLMs learn from human data and feedback, which can be biased, determining whether LLMs exhibit human-like behavioral decision biases (e.g., base-rate neglect, risk aversion, confirmation bias, etc.) is crucial prior to implementing LLMs into decision-making contexts and workflows. To understand this, we examine 18 common human biases that are important in operations management (OM) using the dominant LLM, ChatGPT. Methodology/results: We perform experiments where GPT-3.5 and GPT-4 act as participants to test these biases using vignettes adapted from the literature ("standard context") and variants reframed in inventory and general OM contexts. In almost half of the experiments, Generative Pre-trained Transformer (GPT) mirrors human biases, diverging from prototypical human responses in the remaining experiments. We also observe that GPT models have a notable level of consistency between the standard and OM-specific experiments as well as across temporal versions of the GPT-3.5 model. Our comparative analysis between GPT-3.5 and GPT-4 reveals a dual-edged progression of GPT's decision making, wherein GPT-4 advances in decision-making accuracy for problems with well-defined mathematical solutions while simultaneously displaying increased behavioral biases for preference-based problems. Managerial implications: First, our results highlight that managers will obtain the greatest benefits from deploying GPT to workflows leveraging established formulas. Second, that GPT displayed a high level of response consistency across the standard, inventory, and non-inventory operational contexts provides optimism that LLMs can offer reliable support even when details of the decision and problem contexts change. Third, although selecting between models, like GPT-3.5 and GPT-4, represents a trade-off in cost and performance, our results suggest that managers should invest in higher-performing models, particularly for solving problems with objective solutions. Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada [Grant SSHRC 430-2019-00505]. The authors also gratefully acknowledge the Smith School of Business at Queen's University for providing funding to support Y. Chen's postdoctoral appointment.
[2]
AI thinks like us -- flaws and all: Study finds ChatGPT mirrors human decision biases in half the tests
by Ashley Smith, Institute for Operations Research and the Management Sciences Can we really trust AI to make better decisions than humans? A new study says ... not always. Researchers have discovered that OpenAI's ChatGPT, one of the most advanced and popular AI models, makes the same kinds of decision-making mistakes as humans in some situations -- showing biases like overconfidence of hot-hand (gambler's) fallacy -- yet acting inhuman in others (e.g., not suffering from base-rate neglect or sunk cost fallacies). Published in the Manufacturing & Service Operations Management journal, the study reveals that ChatGPT doesn't just crunch numbers -- it "thinks" in ways eerily similar to humans, including mental shortcuts and blind spots. These biases remain rather stable across different business situations but may change as AI evolves from one version to the next. AI: A smart assistant with human-like flaws The study, "A Manager and an AI Walk into a Bar: Does ChatGPT Make Biased Decisions Like We Do?," put ChatGPT through 18 different bias tests. The results? Why this matters From job hiring to loan approvals, AI is already shaping major decisions in business and government. But if AI mimics human biases, could it be reinforcing bad decisions instead of fixing them? "As AI learns from human data, it may also think like a human -- biases and all," says Yang Chen, lead author and assistant professor at Western University. "Our research shows when AI is used to make judgment calls, it sometimes employs the same mental shortcuts as people." The study found that ChatGPT tends to: "When a decision has a clear right answer, AI nails it -- it is better at finding the right formula than most people are," says Anton Ovchinnikov of Queen's University. "But when judgment is involved, AI may fall into the same cognitive traps as people." So, can we trust AI to make big decisions? With governments worldwide working on AI regulations, the study raises an urgent question: Should we rely on AI to make important calls when it can be just as biased as humans? "AI isn't a neutral referee," says Samuel Kirshner of UNSW Business School. "If left unchecked, it might not fix decision-making problems -- it could actually make them worse." The researchers say that's why businesses and policymakers need to monitor AI's decisions as closely as they would a human decision-maker. "AI should be treated like an employee who makes important decisions -- it needs oversight and ethical guidelines," says Meena Andiappan of McMaster University. "Otherwise, we risk automating flawed thinking instead of improving it." What's next? The study's authors recommend regular audits of AI-driven decisions and refining AI systems to reduce biases. With AI's influence growing, making sure it improves decision-making -- rather than just replicating human flaws -- will be key. "The evolution from GPT-3.5 to 4.0 suggests the latest models are becoming more human in some areas, yet less human but more accurate in others," says Tracy Jenkin of Queen's University. "Managers must evaluate how different models perform on their decision-making use cases and regularly re-evaluate to avoid surprises. Some use cases will need significant model refinement."
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A new study reveals that ChatGPT, while excelling in logic and math, exhibits many of the same cognitive biases as humans when making subjective decisions, raising questions about AI's reliability in high-stakes decision-making processes.
A groundbreaking study published in the INFORMS journal Manufacturing & Service Operations Management has revealed that ChatGPT, one of the most advanced AI language models, exhibits many of the same cognitive biases as humans when making subjective decisions 1. This finding raises significant concerns about the reliability of AI in high-stakes decision-making processes.
Researchers conducted 18 different bias tests on ChatGPT, examining its decision-making processes across various scenarios. The study, titled "A Manager and an AI Walk into a Bar: Does ChatGPT Make Biased Decisions Like We Do?" uncovered several key insights:
The study's findings have significant implications for the use of AI in various sectors:
Yang Chen, lead author and assistant professor at Western University, emphasizes that AI may think like humans, including inheriting their biases, as it learns from human data 2.
Anton Ovchinnikov of Queen's University notes that while AI excels at finding the right formula for clear-cut decisions, it may fall into the same cognitive traps as humans when judgment is involved 2.
The researchers propose several steps to address the challenges identified:
As AI continues to evolve and influence various aspects of society, ensuring that it improves decision-making rather than replicating human flaws will be crucial for its responsible and effective implementation.
Reference
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OpenAI's recent study shows that ChatGPT exhibits minimal bias in responses based on users' names, with only 0.1% of responses containing harmful stereotypes. The research highlights the importance of first-person fairness in AI interactions.
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7 Sources
OpenAI has introduced a new version of ChatGPT with improved reasoning abilities in math and science. While the advancement is significant, it also raises concerns about potential risks and ethical implications.
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Recent research reveals that while larger AI language models demonstrate enhanced capabilities in answering questions, they also exhibit a concerning trend of increased confidence in incorrect responses. This phenomenon raises important questions about the development and deployment of advanced AI systems.
5 Sources
5 Sources
Recent studies reveal that as AI language models grow in size and sophistication, they become more likely to provide incorrect information confidently, raising concerns about reliability and the need for improved training methods.
3 Sources
3 Sources
A BBC investigation finds that major AI chatbots, including ChatGPT, Copilot, Gemini, and Perplexity AI, struggle with accuracy when summarizing news articles, raising concerns about the reliability of AI in news dissemination.
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