Making fairness an intrinsic part of AI for financial services

First appeared here: https://www.cefpro.com/0506ri-making-fairness-an-intrinsic-part-of-ai-for-financial-services/ What, for you, are the benefits of attending a conference like the ‘X-Tech Europe Summit’ and what can attendees expect to learn from your session? Conferences like the X-Tech Europe Summit do a great job of providing a one-stop platform to acquaint oneself with innovative disruptions in the financial world. My […]

Fair AI: How to Detect and Remove Bias from Financial Services AI Models

First appeared here: https://www.finextra.com/blogposting/17864/fair-ai-how-to-detect-and-remove-bias-from-financial-services-ai-models Artificial intelligence (AI) and machine learning (ML) promise a smarter, more automated future for everyone. But the algorithms that underpin these technologies are at risk of bias, a substantial threat that could undermine their entire purpose. What exactly is AI bias, why is it important for financial services (FS) firms to […]

Making Fairness an Intrinsic Part of Machine Learning

First appeared here: https://opendatascience.com/making-fairness-an-intrinsic-part-of-machine-learning/ The suitability of Machine Learning models is traditionally measured on its accuracy. A highly accurate model based on metrics like RMSE, MAPE, AUC, ROC, Gini, etc are considered to be high performing models. While such accuracy metrics important, are there other metrics that the data science community has been ignoring so […]

THE RICHNESS IN DATA OF POORS

First appeared here: https://analyticsindiamag.com/the-richness-in-data-of-poors/ Without denial, Data Science has brought a paradigm shift in the way business use to happen. Be it B2B or B2C, business have come much closer to their customer than before. It won’t be wrong when I say that each one of us experience some or other kind of data science […]

How to use Multinomial and Ordinal Logistic Regression in R ?

First appeared here: https://www.analyticsvidhya.com/blog/2016/02/multinomial-ordinal-logistic-regression/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29 Introduction Most of us have limited knowledge of regression. Of which, linear and logistic regression are our favorite ones. As an interesting fact, regression has extended capabilities to deal with different types of variables. Do you know, regression has provisions for dealing with multi-level dependent variables too? I’m sure, you didn’t. Neither did I. Until […]

Checks for Logistic Regressions

Confusion Matrix: PS: Misclassifying a true negative as positive or False positive is Type I error and otherwise s type II error Sensitivity = TPR = TP/(FN+TP) Specificity = TNR = TN/(TN+FP) data(‘ActualsAndScores’) confusionMatrix(actuals=ActualsAndScores$Actuals, predictedScores=ActualsAndScores$PredictedScores) specificity(actuals=ActualsAndScores$Actuals, predictedScores=ActualsAndScores$PredictedScores) sensitivity(actuals=ActualsAndScores$Actuals, predictedScores=ActualsAndScores$PredictedScores) precision(actuals=ActualsAndScores$Actuals, predictedScores=ActualsAndScores$PredictedScores) plotROC(actuals=ActualsAndScores$Actuals, predictedScores=ActualsAndScores$PredictedScores) Concordance & Discordance A pair is said to be concordant if 1 has […]

Plots in Linear Regression

Residual Plots The residuals should be random around 0 which implies that that the relationship is linear. It also shows outliers. If no residual “stands out” from the basic random pattern of residuals it shows that there is no outliers else vice versa QQ Plots The normal plot of the residuals displays the residuals versus […]

Bias-Variance, Bagging, Boosting… For Dummies I

Bias-Variance · Bias is the tendency to learn the wrong thing by not taking into account all the information in the data (underfitting). It is also used to denote by how much the average accuracy of the algorithm changes as input/training data changes. · Variance is the tendency to learn random things irrespective of the real signal by fitting […]

Interpreting Linear Regression

P-Values in Linear Regression The p-value for each term tests the null hypothesis that the coefficient is equal to zero. A low p-value (< 0.05) indicates that you can reject the null hypothesis. A low p-value indicates meaningful addition to your model. Regression Coefficients Regression coefficients represent the mean change in the response variable for […]

The Intelligent Omnipresent Bank

First appeared here: https://cio.economictimes.indiatimes.com/tech-talk/the-intelligent-omnipresent-bank/2621 A computer today can create new recipes, diagnose or suggest treatment options for cancer patients and even answer legal queries. These were nuances of sci-fi flicks earlier, but a reality now. A computer today can create new recipes, diagnose or suggest treatment options for cancer patients and even answer legal queries. […]