ai Archives - Reskilling IT

AI

January 6, 2019 0

13 Programming Languages used for Machine Learning

In this post, you will learn about different programming languages which can be used to create (train) machine learning models…

AI

December 29, 2018 0

Linear Regression Explained with Real Life Example

In this post, linear regression concept in machine learning is explained with multiple real-life examples. Both types of regression (simple…

AI

December 24, 2018 0

Andrew NG Machine Learning Coursera Videos

In this post, you will get to know the list of Andrew NG Machine Learning Coursera Videos. Here is the information: Youtube…

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…

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…

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

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…

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…

AI

October 31, 2018 0

Fairness Metrics – ML Model Sensitivity for Bias Detection

There are many different ways in which machine learning (ML) models’ fairness could be determined. Some of them are statistical parity, the…

AI

Data Science Team Roles & Responsibilities

October 17, 2018 0

Data Science Team Structure – Roles & Responsibilities

Are you planning to set up the AI or data science team in your organization, and hence, looking for some ideas around data…

Data Science

October 14, 2018 0

Data Science Project Folder Structure

Have you been looking out for project folder structure or template for storing artifacts of your data science or machine learning project? Once there are teams…

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