- 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
- 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
- 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
- Data Protection Techniques Needed to Guarantee Privacy - Oct 2, 2020.
This article takes a look at the concepts of data privacy and personal data. It presents several privacy protection techniques and explains how they contribute to preserving the privacy of individuals.
Big Data Privacy, Compliance, Privacy, Trust
- Top 7 Data Science Use Cases in Trust and Security - Dec 2, 2019.
What are trust and safety? What is the role of trust and security in the modern world? Read this overview of 7 data science application use cases in the realm of trust and security.
AI, Data Science, Security, Trust, Use Cases
- Reproducibility, Replicability, and Data Science - Nov 19, 2019.
As cornerstones of scientific processes, reproducibility and replicability ensure results can be verified and trusted. These two concepts are also crucial in data science, and as a data scientist, you must follow the same rigor and standards in your projects.
Best Practices, Data Science, Overfitting, Reproducibility, Trust, Validation
- Interpretability is crucial for trusting AI and machine learning - Nov 30, 2018.
We explain what exactly interpretability is and why it is so important, focusing on its use for data scientists, end users and regulators.
AI, Explainable AI, Explanation, Interpretability, Machine Learning, Trust
- How Not To Lie With Statistics - Jan 11, 2018.
Darrell Huff's classic How to Lie with Statistics is perhaps more relevant than ever. In this short article, I revisit this theme from some different angles.
Statistics, Trust
- Big Data Desperately Needs Transparency - Mar 6, 2017.
If Big Data is to realize its potential, people need to understand what it is capable of, what information is out there and where every piece of data comes from. Without such transparency and understanding, it will be difficult to persuade people to rely on the findings.
Big Data, Interpretability, Transparency, Trust