Reskilling IT - Page 2 of 58 - Tutorials, Tests, Interviews, News & Insights

AI

December 18, 2018 0

MIT OCW Machine Learning Courses Information

In this post, you get the information related to MIT OCW machine learning course from MIT OpencourseWare (OCW). They use Matlab as…

AI

December 16, 2018 0

Machine Learning – Insurance Applications Use Cases

In this post, you will learn about some of the following insurance applications use cases where machine learning or AI-powered solution can be applied:…

AI

November 30, 2018 0

AWS reInvent – Top 7 New Machine Learning Services

In this post, you will learn about some great new and updated machine learning services which have been launched at AWS…

News

November 25, 2018 0

Solution – Macbook Air Stuck at Progress Bar Loading

In this post, you will learn about the solution related to Mac Air Stuck at Progress Bar Loading. The reason I…

AI

November 23, 2018 0

Guidelines for Creating an Ethical AI Framework

In this post, you will learn about how to create an Ethical AI Framework which could be used in your organization. In case, you…

Build Automation

Gitflow Workflow - Master, Release, Develop, Feature

November 23, 2018 0

GitFlow Workflow Best Practices & Quiz Questions

In this post, you will learn about some of the best practices in relation to managing your development and production releases using GitFlow…

Application Security

November 21, 2018 0

Javascript Security Vulnerabilities Examples (DarwinBox)

In this post, you will learn quick tips on security vulnerabilities related to Javascript based on analysis of how Javascript assets are…

AngularJS

November 21, 2018 0

Angular Bootstrap Hello World App – Sample Code

In this post, you will learn about how to get your Angular App to use Bootstrap CSS and Javascript libraries for creating some great…

AI

November 20, 2018 0

Ethical AI Principles – IBM, Google, Intel, Microsoft

In this post, you will get a quick glimpse of ethical AI principles of companies such as IBM, Intel, Google,…

AI

November 20, 2018 0

IEEE Bookmarks on Ethical AI Considerations

In this post, you will get to have bookmarks for ethical AI by IEEE (Institute of Electrical and Electronics Engineers) group. Those…

AI

November 19, 2018 0

AI-powered Project Baseline to Map Human Health

In this post, you will learn about technologies and data gathering strategy for Project Baseline, an initiative by Google. Project…

AI

November 16, 2018 0

Machine Learning Models – Bias Mitigation Strategies

In this post, you will learn about some of the bias mitigation strategies which could be applied in ML Model Development lifecycle (MDLC) to…

AI

Facebook ML System Integrity Compromised

November 14, 2018 0

Facebook Machine Learning Tool to Check Terrorists Posts

In this post, you will learn about details on Facebook machine learning tool to contain online terrorists propaganda. The following topics are discussed…

DevOps

November 13, 2018 0

DevOps Maturity Model – Telstra DevOps Implementation

In this post, you will get a quick glimpse of how Telstra successfully rolled out DevOps across the entire organization (170 teams).…

AngularJS

November 13, 2018 0

Angular – Firebase Authentication using Email Password

In this post, you will learn about angular code sample or example related to firebase authentication using email and password in an Angular app. The code has…

AI

In this post, you would learn about details (brief information and related URLs) on some of the research work done on AI / machine learning model ethics & fairness / bias in companies such as Google, IBM, Microsoft and others. This post will be updated from time-to-time covering latest projects/research work happening in various companies. You may want to bookmark the page for checking out latest details. Before we go ahead, it may be worth visualizing the great deal of research happening in the field of machine learning model fairness represented using the cartoon below, which is taken from the course CS 294: Fairness in Machine Learning course taught at UC Berkley. IBM Research for ML Model Fairness AI Fairness 360 - AIF360: AIF360 Toolkit is aimed to help data scientists, not only detect biases at different points (training data, classifier and predictions) in machine learning pipeline but also apply bias mitigation strategies to handle any discovered bias. Here is the link for AIF360 Portal Trusted AI Research: List down research publications and related work in the following areas: Robustness (Security & reliability of AI systems) Fairness Explainability / Interpretability Trackability (Lineage) AI Fairness Tutorials: Presents tutorials with the following projects: Credit scoring Medical expenditure Gender classification of face images AI Model Fairness research papers based on which AIF360 toolkit is created. Google Research/Courses on ML Model Fairness Here are some links in relation to machine learning model fairness. Machine learning fairness Google Machine Learning crash course - Fairness module: In addition, the module also presents information on some of the following: Types of Bias. Discussed are some of the following different types of bias: Selection bias (coverage bias, non-response bias, sampling bias) Group attribution bias (in-group bias, out-group homogeneity bias) Implicit bias (confirmation & experimenter's bias) Identifying bias: The following are some of the topics discussed for identifying the bias: Missing feature values Unexpected feature values Data skew Evaluating Bias: Confusion matrix (accuracy vs recall or sensitivity) could be used to evaluate bias for different groups. Interactive visualization on attacking discrimination with smarter machine learning Microsoft Research on Model FATE FATE: Defines initiatives in relation to some of the following: Fairness Accountability Transparency Ethics Kate Crawford - The Rise of Autonomous Experimentation: Technical, Social, and Ethical Implications of AI. Details & some great videos could be found on Kate Crawford Website. Hanna Wallach - Work on FATE Summary In this post, you learned about details on courses and research initiatives happening in the area of machine learning model fairness in different companies such as Google, IBM and others.

November 7, 2018 0

ML Model Fairness Research from IBM, Google & Others

In this post, you would learn about details (brief information and related URLs) on some of the research work done on AI/machine learning…