Data Science Archives - Reskilling IT

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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…

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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…

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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…

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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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October 13, 2018 0

Job Description – Chief Artificial Intelligence (AI) Officer

Whether your organization needs a chief artificial intelligence (AI) officer is a topic where there have been differences of opinions. However, the…

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FairML for Bias Detection in Machine Learning Models

October 12, 2018 0

Bias Detection in Machine Learning Models using FairML

Detecting bias in machine learning model has become of great importance in recent times. Bias in the machine learning model is about…

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machine learning models bias variance vs complexity

October 9, 2018 0

AI / Machine Learning Bias Explained with Examples

In the artificial intelligence (AI) / machine learning (ML) powered world where predictive models have started getting used more often…

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Threat Model - Security Attacks on Machine Learning Models

October 6, 2018 0

Security Attacks Analysis of Machine Learning Models

Have you wondered around what would it be like to have your machine learning (ML) models come under security attack? In other words,…

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October 2, 2018 0

JupyterLab & Jupyter Notebook Cheat Sheet Commands

Are you starting to create machine learning models (using python programming) using JupyterLab or Jupyter Notebook? This post list down some commands which…

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October 2, 2018 0

Missing Data Imputation Techniques in Machine Learning

Have you come across the problem of handling missing data/values for respective features in machine learning (ML) models during prediction time? This is different from…

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