Tag Collection
#bias
Explore Books, Authors and Common Highlights on Bias
Showing 50 of 50 highlights
We tend to overvalue what we have and undervalue what we don’t.
From Predictably Irrational by Dan Ariely
People have a tendency to believe what they want to believe.
Expertise is not a guarantee of accuracy.
A lot of people think they are rational but they are just not aware of their biases.
Humans are not as rational as they think they are.
We must be aware of the biases that cloud our judgment.
From The Changing World Order by Ray Dalio
The way we choose to frame a problem can lead us to make different decisions.
From Predictably Irrational by Dan Ariely
The context in which we make decisions can significantly alter our choices.
From Predictably Irrational by Dan Ariely
The law of small numbers is a form of overconfidence.
Machines learn from data, but the data can reflect biases and errors present in society.
From The Alignment Problem by Brian Christian
We often rely on mental shortcuts, which can lead us astray.
From Predictably Irrational by Dan Ariely
Anchoring effects are strong and persistent.
The biases in our data are the biases in ourselves.
From The Big Nine by Amy Webb
The bias-variance tradeoff is a central concept in machine learning.
Acknowledging our biases is the first step to overcoming them.
Anchoring effects are pervasive and powerful.
We are prone to overestimate how much we understand about the world and to underestimate the role of chance in events.
The ease with which we retrieve information influences our perception of frequency.
We can be blind to the obvious, and we are also blind to our blindness.
We often think we are more rational than we actually are.
From The Righteous Mind by Jonathan Haidt
Cognitive biases are the mind's way of shortcutting reasoning, often leading to errors.
To think critically is to be aware of our own biases and to engage in self-reflection.
Understand your biases and you will understand your decisions.
From Seeking Wisdom by Peter Bevelin
Algorithms are not neutral; they carry the biases of their creators.
From Atlas of AI by Kate Crawford
Understanding cognitive biases is crucial for better decision-making.
The lack of transparency in algorithms allows bias to thrive.
Analysts must understand their own cognitive biases.
Intuitive judgments are often wrong, but they are often made with confidence.
Recognizing our biases is the first step towards making better choices.
Bias and noise are two different problems that require different solutions.
To think clearly, you must first identify your biases.
We often forget that AI is a reflection of our own biases and limitations.
When we are faced with a choice, we often rely on the default option.
From Predictably Irrational by Dan Ariely
Understanding the biases in our predictions is crucial for improvement.
Skepticism is a valuable tool for ensuring we don’t fall prey to our biases.
From The Scout Mindset by Julia Galef
We are not designed to be impartial, objective, or rational.
From The Righteous Mind by Jonathan Haidt
The more we analyze, the more we realize our uncertainty.
Many people believe they can predict the future based on past events.
From The Drunkard's Walk by Leonard Mlodinow
Bias is often unconscious, making it all the more challenging to address.
What you see is all there is.
Algorithms are not neutral — they reflect the biases of their creators.
Unconscious bias affects decisions at every level of the organization.
Understanding cognitive biases is crucial for improving the quality of analysis.
Expertise does not always guarantee accuracy.
What we see is all there is.
From The Black Swan by Nassim Nicholas Taleb
We often miss the obvious because we are blinded by our biases.
We need to recognize the limits of our intuition.
We are prone to overestimate our understanding of the world.
From The Black Swan by Nassim Nicholas Taleb
People tend to be overconfident in their judgments.
Data-driven decisions can exacerbate existing biases rather than eliminate them.