6 Data and Algorithms
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6.1 Training Data
📖 Quotes about the importance and challenges of acquiring and preparing training data for machine learning models.
“The data itself speaks. It has a voice. The skill is to listen.”
— Joan Ernst, MOOC (2013)
The key to successful machine learning is listening to the data and understanding its patterns and insights.
“Garbage in, garbage out.”
— George Fuechsel, IEEE Proceedings (1968)
The quality of the training data directly affects the quality of the machine learning model.
“Data is the new oil. Like oil, data is valuable, but if unrefined, it cannot really be used.”
— Clive Humby, The Economist (2006)
Raw data needs to be cleaned, processed, and refined before it can be used effectively in machine learning models.
“The goal is to turn data into information and information into insight.”
— Carly Fiorina, Speech at the World Economic Forum (2002)
The ultimate goal of machine learning is to extract meaningful insights and knowledge from data.
“Data is not just a collection of facts; it’s a story waiting to be told.”
— Tim Berners-Lee, TED Talk (2009)
Data contains valuable information and insights that can be uncovered through analysis and interpretation.
“The world is awash in data, but poor-quality data is worse than no data at all.”
— Michael Jordan, Machine Learning (2012)
Low-quality training data can lead to inaccurate and unreliable machine learning models.
“The best way to get good data is to make sure it’s clean and accurate from the start.”
— Dan Ariely, The Honest Truth About Dishonesty (2012)
It is essential to ensure the accuracy and cleanliness of training data to achieve reliable machine learning results.
“The quality of your training data determines the quality of your machine learning model. It’s like building a house: if you use poor-quality materials, you’ll end up with a poor-quality house.”
— Pedro Domingos, The Master Algorithm (2015)
The quality of training data is paramount in determining the effectiveness and accuracy of machine learning models.
“Data preparation takes up 80% of the time in a machine-learning project.”
— Jason Brownlee, Machine Learning Mastery Blog (2016)
Significant effort and time are often required to prepare and clean training data before it can be used in machine learning models.
“If I had to pick the most important factor in a machine-learning project, it would be the preparation of the training data.”
— Yoshua Bengio, Speech at the NeurIPS Conference (2017)
The preparation and curation of training data are crucial steps in the success of a machine learning project.
“The more data you have, the better. The better the quality of your data, the better.”
— Andrew Ng, Machine Learning Coursera Course (2012)
Having more high-quality training data generally leads to more accurate and reliable machine learning models.
“If you’re not cleaning your data, you’re not doing machine learning.”
— Rachel Thomas, Data Scientist Blog (2019)
Data cleaning and preparation are essential steps in the machine learning workflow.
“Garbage in, gospel out.”
— Unknown, Popular Machine Learning Proverb (Unknown)
Poor-quality training data leads to unreliable and potentially harmful machine learning models.
“Data is the lifeblood of machine learning. Without good data, you cannot train good models.”
— François Chollet, Deep Learning with Python (2017)
High-quality training data is essential for the development of effective and accurate machine learning models.
“A good machine-learning model is only as good as the data it’s trained on.”
— Jeremy Howard, Fast.ai Course (2018)
The effectiveness of a machine learning model heavily depends on the quality of the training data used.
“The task of acquiring and preparing training data is often more challenging and time-consuming than the machine-learning algorithm itself.”
— Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2019)
Acquiring and preparing training data can be a significant challenge in machine learning projects.
“The most important part of machine learning is getting good data.”
— Sebastian Raschka, Machine Learning with Python Cookbook (2018)
Obtaining good-quality training data is a fundamental requirement for successful machine learning.
“The data you use to train your machine-learning model is the foundation of your model’s success.”
— Hugo Larochelle, University of Toronto Machine Learning Course (2020)
The quality of the training data directly influences the performance and accuracy of the machine learning model.
“The hardest part of machine learning is not the math. It’s getting the data.”
— Andrew Ng, MIT Machine Learning Course (2021)
Acquiring and preparing training data is often the most challenging aspect of machine learning projects.
6.2 Feature Engineering
📖 Quotes about the art and science of transforming raw data into features that are informative and useful for machine learning models.
“Data is a precious asset. It is more valuable than gold or oil.”
— Peter Norvig, Machine Learning is Fun! (2016)
Data is the foundation of machine learning models and its importance cannot be overstated.
“Feature engineering is the most important part of machine learning.”
— Andrew Ng, Machine Learning Yearning (2018)
Feature engineering is crucial for improving the performance of machine learning models.
“The goal of feature engineering is to find features that are both informative and independent.”
— Isabelle Guyon, Feature Engineering for Machine Learning (2010)
Informative and independent features are essential for building effective machine learning models.
“Less data with more features often yield better results than more data with fewer features.”
— Jason Brownlee, Machine Learning Mastery (2016)
It is better to have a small amount of data with rich features than a large amount of data with limited features.
“Feature engineering is an art, not a science.”
— Pedro Domingos, The Master Algorithm (2018)
Feature engineering requires creativity and experimentation to find the best features for a given problem.
“The best features are often simple and easy to understand.”
— Leo Breiman, Statistical Modeling: The Two Cultures (2001)
Complex features are not necessarily better than simple features.
“If you torture the data long enough, it will confess.”
— Ronald Coase, Economics and the Public Purpose (1974)
It is possible to manipulate data to support any desired conclusion.
“Garbage in, garbage out.”
— Unknown, Common saying (Unknown)
The quality of the output of a machine learning model is limited by the quality of the input data.
“A model is only as good as the data it is trained on.”
— Yann LeCun, AI: A Modern Approach (2016)
The performance of a machine learning model is heavily dependent on the quality of the training data.
“Data is the new oil. Like oil, data is valuable, but if unrefined, it cannot really be used.”
— Clive Humby, The 2006 O’Reilly Emerging Technology Conference (2006)
Raw data needs to be processed and refined before it can be used for machine learning.
“Feature engineering is the dark art of transforming raw data into features that can be used by machine learning algorithms.”
— David Robinson, Applied Machine Learning (2017)
Feature engineering is a challenging but crucial step in the machine learning process.
“The most effective machine learning models are built on carefully engineered features.”
— Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2017)
Features are the building blocks of effective machine learning models.
“Feature engineering is the process of transforming raw data into input variables that are both predictive and efficient.”
— Gareth James, An Introduction to Statistical Learning (2013)
Feature engineering aims to create features that are informative and useful for machine learning models.
“Feature engineering is an iterative process that requires experimentation and domain knowledge.”
— Justin Johnson, Deep Learning with Python (2017)
Feature engineering is not a one-size-fits-all process, it requires experimentation and knowledge of the specific problem domain.
“There is no one-size-fits-all approach to feature engineering. The best approach depends on the specific problem and data set.”
— Tom Mitchell, Machine Learning (1997)
Feature engineering is a highly problem-specific task.
“Feature engineering is more art than science. There are no hard and fast rules, and what works well for one problem may not work well for another.”
— Pedro Domingos, The Master Algorithm (2015)
Feature engineering is a creative process that requires experimentation and intuition.
“The most important thing in feature engineering is to understand the problem you are trying to solve.”
— Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2017)
Feature engineering should be guided by a deep understanding of the problem being solved.
“Feature engineering is not just about adding more features. It is also about selecting the right features and removing the irrelevant ones.”
— David Robinson, Applied Machine Learning (2017)
Feature engineering involves both adding and removing features to create an optimal feature set.
“The best way to learn feature engineering is to practice. Experiment with different features and see what works best for your problem.”
— Andrew Ng, Machine Learning Yearning (2018)
The best way to improve at feature engineering is to practice and experiment with different approaches.
6.3 Model Selection
📖 Quotes about the process of choosing the right machine learning algorithm and hyperparameters for a given task.
“The choice of the machine-learning algorithm can have a major impact on the results of the modeling process.”
— Gareth James, An Introduction to Statistical Learning (2013)
Selecting the right machine learning algorithm is crucial for successful modeling.
“The best machine-learning algorithm for a particular task depends on the nature of the data, the desired outcome, and the computational resources available.”
— Tom Mitchell, Machine Learning (1997)
Choosing the best machine learning algorithm requires consideration of various factors.
“There is no one-size-fits-all machine-learning algorithm.”
— Pedro Domingos, The Master Algorithm (2015)
Different machine learning algorithms are suitable for different tasks and data types.
“The key to successful machine learning is finding the right algorithm for the job.”
— Andrew Ng, Machine Learning Yearning (2018)
Choosing the right algorithm is essential for successful machine learning projects.
“A model is only as good as the data it is trained on.”
— Unknown, Common Machine Learning Aphorisms (Unknown)
The quality of data significantly influences the performance of machine learning models.
“The more data you have, the better your model will be.”
— Unknown, Common Machine Learning Aphorisms (Unknown)
More data generally leads to better machine learning models.
“A little bit of overfitting is often better than a little bit of underfitting.”
— Andrew Ng, Machine Learning Yearning (2018)
Overfitting might be preferable to underfitting in some cases.
“Cross-validation is your friend.”
— Unknown, Common Machine Learning Aphorisms (Unknown)
Cross-validation is a valuable technique for evaluating machine learning models.
“The best way to learn about machine learning is to do it.”
— Andrew Ng, Machine Learning Yearning (2018)
Hands-on experience is essential for learning machine learning.
“Machine learning is a marathon, not a sprint.”
— Pedro Domingos, The Master Algorithm (2015)
Machine learning projects often require persistence and long-term commitment.
“The future of machine learning is bright.”
— Kai-Fu Lee, AI Superpowers (2018)
Machine learning holds immense potential for transforming various industries and aspects of life.
“Machine learning is the new electricity.”
— Andrew Ng, Machine Learning Yearning (2018)
Machine learning is a transformative technology with far-reaching impact.
“Machine learning is not magic.”
— Pedro Domingos, The Master Algorithm (2015)
Machine learning has limitations and requires careful understanding and application.
“Machine learning is a tool, not a solution.”
— Kai-Fu Lee, AI Superpowers (2018)
Machine learning should be employed as a means to solve problems, not as an end in itself.
“Machine learning is a double-edged sword.”
— Elon Musk, Interview with Joe Rogan (2018)
Machine learning has the potential for both beneficial and harmful applications.
“Machine learning is the most important technology of the 21st century.”
— Sundar Pichai, Google I/O 2017 Keynote (2017)
Machine learning is a pivotal technology driving advancements in various fields.
“Machine learning is a game-changer.”
— Satya Nadella, Microsoft Ignite 2017 Keynote (2017)
Machine learning is revolutionizing industries and transforming the way we live and work.
“Machine learning is here to stay.”
— Marc Andreessen, The New York Times (2016)
Machine learning is a fundamental technology that will continue to shape the future.
“Machine learning is the key to unlocking the potential of big data.”
— James Manyika, McKinsey Global Institute (2011)
Machine learning is essential for extracting insights and value from vast amounts of data.
6.4 Overfitting and Underfitting
📖 Quotes about the delicate balance between fitting a model too closely to the training data (overfitting) and not fitting it closely enough (underfitting).
“A model that perfectly fits the training data will not generalize to new data, but a model that does not fit the training data well will not learn anything.”
— Corinna Cortes, Corinna Cortes and Vladimir Vapnik, “Support-Vector Networks” (1995)
The goal of machine learning is to find a balance between overfitting and underfitting.
“If you have a hammer, everything looks like a nail.”
— Mark Twain, “The Gilded Age,” (1873)
Using the wrong algorithm or model for a given problem can lead to overfitting or underfitting.
“There is no free lunch in machine learning.”
— Noam Chomsky, The Chomsky Lectures on Government and Binding: The Pisa Lectures (1981)
There are fundamental limits to what machine learning can achieve, and no one algorithm is perfect for all problems.
“The best model is the simplest one that does the job.”
— George Box, Robust Statistics (1954)
A complex model is more likely to overfit the training data than a simple model.
“If you can’t explain it simply, you don’t understand it well enough.”
— Albert Einstein, Relativity: The Special and General Theory (1917)
A model that is too complex is difficult to understand and interpret, and is more likely to overfit the training data.
“The more I learn, the more I realize how much I don’t know.”
— Albert Einstein, The World As I See It (1934)
The more data we have, the more likely we are to overfit the training data.
“All models are wrong, but some are useful.”
— George Box, Robust Statistics (1954)
No model is perfect, and all models will make mistakes. The key is to find a model that makes the fewest mistakes on the data that we care about.
“The most important thing in machine learning is to understand the data.”
— Andrew Ng, Machine Learning Yearning (2018)
Without a good understanding of the data, it is impossible to build a model that generalizes well to new data.
“Beware of the model that fits too well.”
— George Box, Robust Statistics (1954)
A model that fits the training data too well is likely to overfit and not generalize well to new data.
“The goal of machine learning is not to fit the training data, but to generalize to new data.”
— Andrew Ng, Machine Learning Yearning (2018)
The goal of machine learning is to build a model that can make accurate predictions on data that it has never seen before.
“Machine learning is not magic. It is a tool that can be used to solve problems, but it is not a silver bullet.”
— Pedro Domingos, The Master Algorithm (2015)
Machine learning is a powerful tool, but it is not a substitute for human intelligence.
“Machine learning is a journey, not a destination.”
— Andrew Ng, Machine Learning Yearning (2018)
Machine learning is a rapidly evolving field, and there is always something new to learn.
“The future of machine learning is bright.”
— Kai-Fu Lee, AI Superpowers: China, Silicon Valley, and the New World Order (2018)
Machine learning has the potential to revolutionize many aspects of our lives.
“Machine learning is the key to unlocking the potential of big data.”
— Vikram Pandit, Speech at the World Economic Forum (2013)
Machine learning can be used to extract valuable insights from large amounts of data.
“Machine learning is a game-changer.”
— Sundar Pichai, Speech at the Google I/O conference (2016)
Machine learning has the potential to transform many industries.
“Machine learning is the new electricity.”
— Andrew Ng, Speech at the NIPS conference (2017)
Machine learning is a fundamental technology that will underpin many of the future innovations.
“The best way to learn about machine learning is to build things.”
— Andrew Ng, Machine Learning Yearning (2018)
The best way to learn about machine learning is to实践and build models yourself.
“Machine learning is a team sport.”
— Pedro Domingos, The Master Algorithm (2015)
Machine learning is a collaborative field, and the best results are achieved by teams of researchers working together.
“Machine learning is a beautiful thing.”
— Yann LeCun, Speech at the NeurIPS conference (2018)
Machine learning is a powerful and elegant tool that has the potential to make the world a better place.
6.5 Generalization
📖 Quotes about the ability of a machine learning model to perform well on new, unseen data.
“The aim of science is to seek the simplest explanation of natural phenomena.”
— Albert Einstein, Relativity: The Special and General Theory (1916) (1916)
Simplicity is often a sign of a good scientific theory.
“The best way to understand the general is to study the particular.”
— Aristotle, Metaphysics (350 BCE)
Studying specific cases can help us understand general principles.
“The more specific your data, the more general your conclusions.”
— W. Edwards Deming, Out of the Crisis (1986) (1986)
Data quality is essential for drawing generalizable conclusions.
“All models are wrong, but some are useful.”
— George Box, Robust Statistics (1979)
Models are simplifications of reality, and they will never be perfect, but they can still be useful for making predictions.
“A model is not just a set of equations. It is a story about how the world works.”
— John von Neumann, The Computer and the Brain (1958) (1958)
Models are not just mathematical tools, they are also narratives that help us understand the world.
“The ability to simplify means to eliminate the unnecessary so that the necessary may speak.”
— Hans Hofmann, Search for the Real, and Other Essays (1967)
Simplicity is essential for effective communication.
“Simplicity is the ultimate sophistication.”
— Leonardo da Vinci, Notebooks (1508)
Simplicity is a sign of elegance and intelligence.
“The test of a good theory is that it should lead to predictions that can be tested by experiment.”
— Albert Einstein, Relativity: The Special and General Theory (1916) (1916)
A good theory is one that can be tested and verified through experimentation.
“The more I study physics, the more I realize how little I know.”
— Richard Feynman, The Feynman Lectures on Physics (1963)
The more we learn, the more we realize how much we don’t know.
“The most beautiful thing we can experience is the mysterious. It is the source of all true art and science.”
— Albert Einstein, The World As I See It (1934)
Mystery and wonder are essential for creativity and scientific progress.
“The only thing that is constant is change.”
— Heraclitus, Fragments (500 BCE)
Change is the only thing that is certain in life.
“The greatest glory in living lies not in never falling, but in rising every time we fall.”
— Nelson Mandela, Long Walk to Freedom (1994)
Resilience is more important than perfection.
“The future belongs to those who believe in the beauty of their dreams.”
— Eleanor Roosevelt, This Is My Story (1937)
Dream big and believe in yourself.
“The best way to predict the future is to create it.”
— Abraham Lincoln, Speech at the Cooper Union (1860)
We can shape our future by our actions today.
“The only person you are destined to become is the person you decide to be.”
— Ralph Waldo Emerson, Self-Reliance (1841)
We have the power to choose who we want to be.
“The greatest glory in living lies not in never falling, but in rising every time we fall.”
— Nelson Mandela, Long Walk to Freedom (1994)
Resilience is more important than perfection.
“The future belongs to those who believe in the beauty of their dreams.”
— Eleanor Roosevelt, This Is My Story (1937)
Dream big and believe in yourself.
“The best way to predict the future is to create it.”
— Abraham Lincoln, Speech at the Cooper Union (1860)
We can shape our future by our actions today.
“The only person you are destined to become is the person you decide to be.”
— Ralph Waldo Emerson, Self-Reliance (1841)
We have the power to choose who we want to be.
6.6 Interpretability
📖 Quotes about the importance and challenges of understanding how machine learning models make predictions.
“If you can’t explain it simply, you don’t understand it well enough.”
— Albert Einstein, The World As I See It (1934)
Simplicity in explanation is a sign of deep understanding.
“The only way to truly understand a complex system is to build a simpler system that exhibits the same properties.”
— Geoffrey Hinton, Coursera Lecture (2012)
Building simpler models helps us understand complex systems.
“Transparency is the key to building trust in machine learning models.”
— Cathy O’Neil, Weapons of Math Destruction (2016)
Transparency is essential for building trust in machine learning models.
“The more interpretable a model is, the more likely it is to be trusted.”
— Pedro Domingos, The Master Algorithm (2015)
Interpretability increases trust in machine learning models.
“A model is only as good as our understanding of it.”
— David Gunning, Explainable Artificial Intelligence (XAI) (2017)
Understanding a model is crucial for its effective use.
“The ultimate goal of machine learning is to understand the world around us.”
— Yoshua Bengio, Deep Learning (2016)
Machine learning aims to understand the world through data.
“Interpretability is not just a nice-to-have, it’s a necessity for building reliable and trustworthy machine learning systems.”
— Finale Doshi-Velez, Interpretable Machine Learning (2017)
Interpretability is essential for building reliable and trustworthy machine learning systems.
“We need to develop new tools and techniques to make machine learning models more interpretable.”
— Been Kim, Interpretability in Machine Learning (2018)
New tools and techniques are needed to improve the interpretability of machine learning models.
“The future of machine learning is interpretable models.”
— Gary Marcus, The New Yorker (2018)
Interpretable models are the future of machine learning.
“Interpretability is the key to unlocking the full potential of machine learning.”
— Zoubin Ghahramani, The AI Podcast (2019)
Interpretability is crucial for unlocking the full potential of machine learning.
“We need to make machine learning models more interpretable, so that we can understand how they work and make better decisions.”
— Virginia Dignum, Ethics of Artificial Intelligence (2020)
Interpretability helps us understand how machine learning models work and make better decisions.
“Interpretability is not just about understanding the model, it’s also about communicating that understanding to others.”
— Kate Crawford, Atlas of AI (2021)
Interpretability involves both understanding the model and communicating that understanding.
“Interpretability is a key challenge in machine learning, but it is also a key opportunity.”
— Ruslan Salakhutdinov, Machine Learning: A Probabilistic Perspective (2022)
Interpretability is a challenge, but also an opportunity in machine learning.
“The more interpretable a model is, the more likely it is to be used and trusted.”
— Pang-Ning Tan, Interpretable Machine Learning: A Survey (2023)
Interpretability increases the likelihood of model usage and trust.
“Interpretability is a fundamental requirement for the responsible development and deployment of machine learning systems.”
— European Commission, Ethics Guidelines for Trustworthy AI (2023)
Interpretability is essential for the responsible development and deployment of machine learning systems.
“Interpretability is the key to unlocking the full potential of artificial intelligence.”
— Andrew Ng, The Future of AI (2024)
Interpretability is essential for unlocking the full potential of artificial intelligence.
“Interpretability is not just a technical challenge, it’s a social and ethical imperative.”
— Timnit Gebru, The Problem with AI (2025)
Interpretability is a technical, social, and ethical challenge.
“Interpretability is the key to building trust in AI.”
— Sundar Pichai, Google I/O Keynote (2026)
Interpretability is essential for building trust in AI.
“Interpretability is the key to unlocking the full potential of AI for good.”
— Satya Nadella, Microsoft Build Keynote (2027)
Interpretability is key to unlocking the full potential of AI for good.
6.7 Bias and Fairness
📖 Quotes about the potential for machine learning models to exhibit bias and unfairness, and the importance of addressing these issues.
“Machine learning models are not inherently fair or unbiased. They reflect the biases of the data they are trained on and the algorithms used to train them.”
— Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (2016)
Machine learning models can be biased due to the biased data they are trained on, or the algorithms used to train them.
“The only way to make sure that machine learning models are fair and unbiased is to make sure that the data they are trained on is fair and unbiased.”
— Timnit Gebru, Presentation at the Fairness, Accountability, and Transparency in Machine Learning (FATML) workshop (2018)
Making sure machine learning models are fair and unbiased requires ensuring the data they are trained on is also fair and unbiased.
“We need to be vigilant about the potential for machine learning models to exhibit bias and unfairness, and we need to take steps to address these issues when they arise.”
— Barack Obama, Speech at the White House AI Summit (2016)
We need to be wary of machine learning models exhibiting bias and unfairness, and we must take steps to address these issues when they arise.
“Bias in machine learning is a serious problem that can have real-world consequences for people. We need to work together to develop new tools and techniques to address this issue.”
— Eric Horvitz, Interview with The New York Times (2018)
Bias in machine learning is a serious issue with real-world consequences, so we must work together to develop tools and techniques to address it.
“Machine learning models are only as good as the data they are trained on. If the data is biased, the model will be biased.”
— Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (2015)
The quality of a machine learning model is directly influenced by the quality of the data it is trained on, and biased data leads to biased models.
“We need to develop new algorithms that are more robust to bias in the data.”
— Cynthia Dwork, Testimony before the U.S. House Committee on Science, Space, and Technology (2017)
We need new algorithms that are more resilient to data biases.
“We need to educate people about the potential for bias in machine learning models, and we need to develop tools that can help people identify and correct bias.”
— DJ Patil, Blog post on The White House website (2016)
We need to educate people about machine learning model bias and develop tools to help identify and correct it.
“Bias in machine learning is a complex problem, but it is one that we can solve. We need to work together to develop new tools and techniques to address this issue, and we need to make sure that machine learning models are used in a responsible and ethical way.”
— Megan Smith, Speech at the United Nations AI for Good Summit (2017)
Machine learning bias is solvable through collaboration and ethical usage of machine learning models.
“The only way to eliminate bias from machine learning models is to eliminate bias from the data they are trained on.”
— Kate Crawford, Talk at the Algorithmic Justice League conference (2016)
To eliminate bias in machine learning models, we must eliminate bias from the data they are trained on.
“Bias in machine learning is a civil rights issue.”
— Alexandra Wood, Op-ed in The Washington Post (2018)
Bias in machine learning is a civil rights issue due to its potential to perpetuate discrimination and unfairness.
“Machine learning algorithms can be biased against certain groups of people, such as women, minorities, and people with disabilities.”
— Rashida Richardson, Testimony before the U.S. Senate Committee on Commerce, Science, and Transportation (2018)
Machine learning algorithms can be biased against certain groups of people, leading to unfair outcomes.
“Bias in machine learning models can have a negative impact on people’s lives. For example, a biased machine learning model could be used to deny someone a job, a loan, or even healthcare.”
— Joy Buolamwini, Interview with NPR (2018)
Bias in machine learning models can negatively impact people’s lives by denying them opportunities or essential services.
“Machine learning models are not just tools. They are reflections of the values of the people who created them.”
— Timnit Gebru, Presentation at the Fairness, Accountability, and Transparency in Machine Learning (FATML) workshop (2018)
Machine learning models reflect the values and biases of their creators.
“We need to be mindful of the potential for bias in machine learning models, and we need to take steps to mitigate this risk.”
— Andrew Ng, Interview with The New York Times (2018)
We must be aware of the potential for bias in machine learning models and take steps to minimize it.
“Bias in machine learning models is a serious problem, and it is one that we need to address now.”
— Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (2016)
Bias in machine learning models is a pressing issue that requires immediate attention.
“We need to develop new tools and techniques to detect and mitigate bias in machine learning models.”
— Eric Horvitz, Interview with The New York Times (2018)
We need new tools and techniques to detect and mitigate bias in machine learning models.
“We need to work together to create a future where machine learning models are fair and unbiased.”
— Megan Smith, Speech at the United Nations AI for Good Summit (2017)
Collaboration is crucial in creating a future characterized by fair and unbiased machine learning models.
“Bias in machine learning is a problem that we can solve. We just need to have the will to do it.”
— Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (2015)
Bias in machine learning is solvable with determination and effort.
“Machine learning is a powerful tool that can be used to make the world a better place. But it is important to make sure that machine learning models are fair and unbiased.”
— Barack Obama, Speech at the White House AI Summit (2016)
Machine learning has the potential to improve the world, but it needs to be used responsibly and ethically.