- Stop Blaming Humans for Bias in AI - Nov 19, 2021.
Can artificial intelligence be rid of bias? This is an important question, and it’s equally important that we look in the right place for the answer.
AI, Bias, Ethics, Humans, Responsible AI
- Coding Ethics for AI & AIOps: Designing Responsible AI Systems - Aug 26, 2021.
AI ops has taken Human machine collaboration to the next level where humans and machines are not just coexisting but are collaborating and working together like team members.
AI, Bias, DevOps, Ethics, ModelOps, Responsible AI
- Visualizing Bias-Variance - Aug 10, 2021.
In this article, we'll explore some different perspectives of what the bias-variance trade-off really means with the help of visualizations.
Bias, Machine Learning, Variance, Visualization
- How to Create Unbiased Machine Learning Models - Jul 16, 2021.
In this post we discuss the concepts of bias and fairness in the Machine Learning world, and show how ML biases often reflect existing biases in society. Additionally, We discuss various methods for testing and enforcing fairness in ML models.
AI, Bias, Ethics, Machine Learning, Trust
- Ethics, Fairness, and Bias in AI - Jun 30, 2021.
As more AI-enhanced applications seep into our daily lives and expand their reach to larger swaths of populations around the world, we must clearly understand the vulnerabilities trained machine leaning models can exhibit based on the data used during development. Such issues can negatively impact select groups of people, so addressing the ethical decisions made by AI--possibly unknowingly--is important to the long-term fairness and success of this new technology.
AI, Algorithms, Bias, Ethics
- What Makes AI Trustworthy? - May 11, 2021.
This blog pertains to the importance of why AI needs to be trustworthy as well as what makes it trustworthy. AI predictions/suggestions should not just be taken at face value, but rather delved into at a deeper level. We need to understand how an AI system makes its predictions to put our trust in it. Trust should not be built on prediction accuracy alone.
AI, Bias, Explainable AI, Trust
- The Three Edge Case Culprits: Bias, Variance, and Unpredictability - Apr 22, 2021.
Edge cases occur for three basic reasons: Bias – the ML system is too ‘simple’; Variance – the ML system is too ‘inexperienced’; Unpredictability – the ML system operates in an environment full of surprises. How do we recognize these edge cases situations, and what can we do about them?
Bias, iMerit, Machine Learning, Variance
Top 3 Statistical Paradoxes in Data Science - Apr 15, 2021.
Observation bias and sub-group differences generate statistical paradoxes.
Bias, Data Science, Simpson's Paradox, Statistics
Popular Machine Learning Interview Questions - Jan 20, 2021.
Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions.
Bias, Confusion Matrix, Interview Questions, Machine Learning, Overfitting, Variance
- How to easily check if your Machine Learning model is fair? - Dec 24, 2020.
Machine learning models deployed today -- as will many more in the future -- impact people and society directly. With that power and influence resting in the hands of Data Scientists and machine learning engineers, taking the time to evaluate and understand if model results are fair will become the linchpin for the future success of AI/ML solutions. These are critical considerations, and using a recently developed fairness module in the dalex Python package is a unified and accessible way to ensure your models remain fair.
Bias, Dalex, Ethics, Machine Learning
- AI registers: finally, a tool to increase transparency in AI/ML - Dec 9, 2020.
Transparency, explainability, and trust are pressing topics in AI/ML today. While much has been written about why they are important and what you need to do, no tools have existed until now.
AI, Bias, Ethics, Explainability, Helsinki, Machine Learning, Trust
20 Core Data Science Concepts for Beginners - Dec 8, 2020.
With so much to learn and so many advancements to follow in the field of data science, there are a core set of foundational concepts that remain essential. Twenty of these ideas are highlighted here that are key to review when preparing for a job interview or just to refresh your appreciation of the basics.
Beginners, Bias, Cross-validation, Data Science, Data Visualization, Data Wrangling, Outliers, PCA, Variance
- Six Ethical Quandaries of Predictive Policing - Nov 6, 2020.
When predictive machine learning models are applied to real-life scenarios, especially those that directly impact humans, such as cancer detection and other medical-related applications, the risks involved with incorrect predictions carry very high stakes. These risks are also prominent in how machine learning is applied in law enforcement, and serious ethical questions must be considered.
Bias, Crime, Ethics, Police, Predictive Analytics
- Can AI Learn Human Values? - Oct 27, 2020.
OpenAI believes that the path to safe AI requires social sciences.
AI, Bias, Ethics, OpenAI
- DeepMind Relies on this Old Statistical Method to Build Fair Machine Learning Models - Oct 23, 2020.
Causal Bayesian Networks are used to model the influence of fairness attributes in a dataset.
Bayesian Networks, Bias, DeepMind, Machine Learning
- The Ethics of AI - Oct 21, 2020.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about a very important subject - the ethics of AI.
AI, Bias, Ethics, Interview
Top Google AI, Machine Learning Tools for Everyone - Aug 18, 2020.
Google is much more than a search company. Learn about all the tools they are developing to help turn your ideas into reality through Google AI.
AI, AutoML, Bias, Data Science Platforms, Datasets, Google, Google Cloud, Google Colab, Machine Learning, TensorFlow
- Word Embedding Fairness Evaluation - Aug 5, 2020.
With word embeddings being such a crucial component of NLP, the reported social biases resulting from the training corpora could limit their application. The framework introduced here intends to measure the fairness in word embeddings to better understand these potential biases.
Bias, Ethics, Machine Learning, Word Embeddings
- Free From Stanford: Ethical and Social Issues in Natural Language Processing - Jul 17, 2020.
Perhaps it's time to take a look at this relatively new offering from Stanford, Ethical and Social Issues in Natural Language Processing (CS384), an advanced seminar course covering ethical and social issues in NLP.
Bias, Ethics, NLP, Social Good
- Bias in AI: A Primer - Jun 23, 2020.
Those interested in studying AI bias, but who lack a starting point, would do well to check out this introductory set of slides and the accompanying talk on the subject from Google researcher Margaret Mitchell.
AI, Bias, Computer Vision, NLP
Five Cognitive Biases In Data Science (And how to avoid them) - Jun 12, 2020.
Everyone is prey to cognitive biases that skew thinking, but data scientists must prevent them from spoiling their work. Learn more about five biases that can all too easily make your seemingly objective work become surprisingly subjective.
Advice, Bias, Cognitive Bias, Confirmation Bias, Data Science
- 5 Ways to Apply Ethics to AI - Dec 19, 2019.
Here are six more lessons based on real life examples that I think we should all remember as people working in machine learning, whether you’re a researcher, engineer, or a decision-maker.
Algorithms, Bias, Ethics, Goodhart’s Law, Machine Learning, Social Good
5 Statistical Traps Data Scientists Should Avoid - Oct 30, 2019.
Here are five statistical fallacies — data traps — which data scientists should be aware of and definitely avoid.
Bias, Fallacies, Simpson's Paradox, Statistics
Python Libraries for Interpretable Machine Learning - Sep 4, 2019.
In the following post, I am going to give a brief guide to four of the most established packages for interpreting and explaining machine learning models.
Bias, Interpretability, LIME, Machine Learning, Python, SHAP
Types of Bias in Machine Learning - Aug 29, 2019.
The sample data used for training has to be as close a representation of the real scenario as possible. There are many factors that can bias a sample from the beginning and those reasons differ from each domain (i.e. business, security, medical, education etc.)
Bias, Data Science, Data Scientist, Machine Learning
- Is Bias in Machine Learning all Bad? - Jul 23, 2019.
We have been taught over our years of predictive model building that bias will harm our model. Bias control needs to be in the hands of someone who can differentiate between the right kind and wrong kind of bias.
Bias, Data Science, Machine Learning
- “Please, explain.” Interpretability of machine learning models - May 9, 2019.
Unveiling secrets of black box models is no longer a novelty but a new business requirement and we explain why using several different use cases.
Bias, Explainable AI, Interpretability, LIME, Machine Learning, SHAP, XAI
- 3 Big Problems with Big Data and How to Solve Them - Apr 18, 2019.
We discuss some of the negatives of using big data, including false equivalences and bias, vulnerability to security breaches, protecting against unauthorized access and the lack of international standards for data privacy regulations.
Advice, Bias, Big Data, Privacy, Security
- Building NLP Classifiers Cheaply With Transfer Learning and Weak Supervision - Mar 15, 2019.
In this blog, I’ll walk you through a personal project in which I cheaply built a classifier to detect anti-semitic tweets, with no public dataset available, by combining weak supervision and transfer learning.
Pages: 1 2
Bias, fast.ai, NLP, Python, Text Classification, Transfer Learning, Twitter, ULMFiT
- Designing Ethical Algorithms - Mar 8, 2019.
Ethical algorithm design is becoming a hot topic as machine learning becomes more widespread. But how do you make an algorithm ethical? Here are 5 suggestions to consider.
AI, Algorithms, Bias, Ethics, Machine Learning
- The Algorithms Aren’t Biased, We Are - Jan 29, 2019.
We explain the concept of bias and how it can appear in your projects, share some illustrative examples, and translate the latest academic research on “algorithmic bias.”
Algorithms, Bias, Machine Learning
- 10 Exciting Ideas of 2018 in NLP - Jan 16, 2019.
We outline a selection of exciting developments in NLP from the last year, and include useful recent papers and images to help further assist with your learning.
BERT, Bias, ICLR, Machine Translation, NLP, Transformer, Unsupervised Learning
- A Case For Explainable AI & Machine Learning - Dec 27, 2018.
In support of the explainable AI cause, we present a variety of use cases covering operational needs, regulatory compliance and public trust and social acceptance.
Bias, Explainable AI, Explanation, Interpretability, Machine Learning
You Aren’t So Smart: Cognitive Biases are Making Sure of It - Sep 17, 2018.
Cognitive biases are tendencies to think in certain ways that can lead to systematic deviations from a standard of rationality or good judgment. They have all sorts of practical impacts on our lives, whether we want to admit it or not.
Bias, Cognitive Bias, Confirmation Bias
- The 2018 Data Scientist Report is Here - Aug 23, 2018.
Learn about the data and tools that data scientists are working with in 2018, Ethical issues around AI, Algorithmic bias, Job satisfaction, and more.
Bias, Career, Data Science Platform, Data Science Tools, Data Scientist, Ethics, Figure Eight
- Error Analysis to your Rescue – Lessons from Andrew Ng, part 3 - Jan 29, 2018.
The last entry in a series of posts about Andrew Ng's lessons on strategies to follow when fixing errors in your algorithm
Andrew Ng, Bias, Distribution, Machine Learning, Variance
- Propensity Score Matching in R - Jan 18, 2018.
Propensity scores are an alternative method to estimate the effect of receiving treatment when random assignment of treatments to subjects is not feasible.
Pages: 1 2
Bias, R, Statistics
- Learning Curves for Machine Learning - Jan 17, 2018.
But how do we diagnose bias and variance in the first place? And what actions should we take once we've detected something? In this post, we'll learn how to answer both these questions using learning curves.
Pages: 1 2
Bias, Machine Learning, Metrics, Training Data, Variance
- How AI Learns What You’re Willing to Pay - Dec 28, 2017.
Why are we all paying different prices? Is it price "personalization" or price "discrimination"? The answer isn't so simple.
AI, Airlines, Bias, Consumer Analytics, Machine Learning, Personalization
- How to Improve Machine Learning Algorithms? Lessons from Andrew Ng, part 2 - Dec 21, 2017.
The second chapter of ML lessons from Ng’s experience. This one will only be talking about Human Level Performance & Avoidable Bias.
Algorithms, Andrew Ng, Bias, Machine Learning
- NIPS 2017 Key Points & Summary Notes - Dec 18, 2017.
Third year Ph.D student David Abel, of Brown University, was in attendance at NIP 2017, and he labouriously compiled and formatted a fantastic 43-page set of notes for the rest of us. Get them here.
Bias, Conference, Machine Learning, NeurIPS, NIPS, Reinforcement Learning
Data Science and the Imposter Syndrome - Sep 15, 2017.
You are not the only one who wonders how much longer they can get away with pretending to be a data scientist. You are not the only one who has nightmares about being laughed out of your next interview.
Bias, Data Science, Data Scientist
Data Science Primer: Basic Concepts for Beginners - Aug 11, 2017.
This collection of concise introductory data science tutorials cover topics including the difference between data mining and statistics, supervised vs. unsupervised learning, and the types of patterns we can mine from data.
Bias, Data Mining, Data Science, Distribution, Ensemble Methods, Statistics
How GDPR Affects Data Science - Jul 17, 2017.
Coming European GDPR directive affects data science practice mainly in 3 areas: limits on data processing and consumer profiling, a “right to an explanation” for automated decision-making, and accountability for bias and discrimination in automated decisions.
Bias, Data Science, Europe, GDPR, Privacy, Thomas Dinsmore
17 More Must-Know Data Science Interview Questions and Answers - Feb 15, 2017.
17 new must-know Data Science Interview questions and answers include lessons from failure to predict 2016 US Presidential election and Super Bowl LI comeback, understanding bias and variance, why fewer predictors might be better, and how to make a model more robust to outliers.
Pages: 1 2
Anomaly Detection, Bias, Classification, Data Science, Donald Trump, Interview Questions, Outliers, Overfitting, Variance
- The Top Predictive Analytics Pitfalls to Avoid - Jan 23, 2017.
Predictive modelling and machine learning are significantly contributing to business, but they can be very sensitive to data and changes in it, which makes it very important to use proper techniques and avoid pitfalls in building data science models.
Bias, Machine Learning, Model Performance, Predictive Analytics, Regularization, Statistics
4 Reasons Your Machine Learning Model is Wrong (and How to Fix It) - Dec 21, 2016.
This post presents some common scenarios where a seemingly good machine learning model may still be wrong, along with a discussion of how how to evaluate these issues by assessing metrics of bias vs. variance and precision vs. recall.
Bias, Overfitting, Variance
- 4 Cognitive Bias Key Points Data Scientists Need to Know - Dec 9, 2016.
Cognitive biases are inherently problematic in a variety of fields, including data science. Is this something that can be mitigated? A solid understanding of cognitive biases is the best weapon, which this overview hopes to help provide.
Bias, Cognitive Bias, Confirmation Bias
- Understanding the Bias-Variance Tradeoff: An Overview - Aug 8, 2016.
A model's ability to minimize bias and minimize variance are often thought of as 2 opposing ends of a spectrum. Being able to understand these two types of errors are critical to diagnosing model results.
Bias, Cross-validation, Model Performance, Variance
