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bias

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Model Perspective
Model Perspective
May 25, 2025 · Fundamentals

Why We Pretend to Win: The Hidden Math Behind Evaluation Bias

The article explores how people manipulate evaluation systems by redefining variables, adjusting weights, and shifting perspectives, turning losses into perceived wins, and reveals the psychological and statistical biases that create this illusion, urging more honest, multi‑dimensional, transparent modeling for genuine assessment.

biasdecision makingevaluation
0 likes · 9 min read
Why We Pretend to Win: The Hidden Math Behind Evaluation Bias
Model Perspective
Model Perspective
Dec 19, 2023 · Fundamentals

Why Hospital Survival Rates Can Mislead: Unveiling Simpson’s Paradox

Simpson’s Paradox shows how aggregated data can suggest one trend while each subgroup reveals the opposite, illustrated with hospital survival rates where overall A appears better than B, yet detailed analysis by severity flips the conclusion, highlighting the need to consider background variables in statistical interpretation.

Simpson's paradoxbiasdata analysis
0 likes · 5 min read
Why Hospital Survival Rates Can Mislead: Unveiling Simpson’s Paradox
DataFunSummit
DataFunSummit
Jul 3, 2023 · Big Data

Avoiding Data Misuse: Case Studies on Invalid Data, Simpson’s Paradox, and Statistical Pitfalls

This article examines how data can be misused or misinterpreted through real‑world case studies—ranging from breakfast myths and toothpaste advertising to contraceptive risks, crime statistics, judicial decisions, questionnaire bias, airline efficiency, and correlation‑causation confusion—offering practical guidelines to recognize and prevent invalid data analysis in the big‑data era.

Simpson's paradoxbiasdata analysis
0 likes · 22 min read
Avoiding Data Misuse: Case Studies on Invalid Data, Simpson’s Paradox, and Statistical Pitfalls
Model Perspective
Model Perspective
Nov 8, 2022 · Fundamentals

Why Causal Relationships Matter: From Prediction to Counterfactuals

Understanding why causal relationships matter reveals the limits of predictive machine learning, introduces counterfactual reasoning, explains potential outcomes, treatment effects, bias, and how to distinguish correlation from causation using simple examples like tablet distribution in schools.

biascausal inferencecounterfactuals
0 likes · 18 min read
Why Causal Relationships Matter: From Prediction to Counterfactuals
Architects Research Society
Architects Research Society
Oct 13, 2022 · Artificial Intelligence

Six Business Risks of Ignoring AI Ethics and Governance

Neglecting AI ethics and governance can expose companies to severe public‑relations crises, biased outcomes, regulatory penalties, unexplainable systems, and employee disengagement, ultimately threatening both societal trust and business sustainability.

AI ethicsbiasexplainability
0 likes · 13 min read
Six Business Risks of Ignoring AI Ethics and Governance
Model Perspective
Model Perspective
Sep 5, 2022 · Fundamentals

Why Understanding Causal Relationships Is Crucial for Machine Learning

This article explains why causal inference matters beyond prediction, introduces potential outcomes notation, demonstrates how bias separates correlation from causation, and outlines the conditions under which observed differences can be interpreted as true causal effects.

biascausal inferencemachine learning
0 likes · 16 min read
Why Understanding Causal Relationships Is Crucial for Machine Learning
Python Programming Learning Circle
Python Programming Learning Circle
Mar 5, 2020 · Artificial Intelligence

Biological Neurons and Their Simple Mathematical Representation in Neural Networks

This article explains how biological neurons inspire artificial neural networks, describing neuron concepts, threshold firing, weighted inputs, bias, activation functions such as the step and sigmoid functions, and shows how these ideas are expressed mathematically and visualized with diagrams.

Artificial Intelligenceactivation functionbias
0 likes · 13 min read
Biological Neurons and Their Simple Mathematical Representation in Neural Networks
Architects Research Society
Architects Research Society
Oct 4, 2015 · Artificial Intelligence

What AI Can Teach Us About Human Creativity and Cognitive Biases

The article explores how IBM Watson’s cognitive computing reveals human creative processes and common decision‑making biases such as the ambiguity effect, confirmation bias, and “not‑invented‑here” syndrome, showing how AI can help overcome these mental constraints.

AICognitive Computingbias
0 likes · 9 min read
What AI Can Teach Us About Human Creativity and Cognitive Biases