Work todate has highlighted the challenges inmitigating bias within these datasets.

This dataset includes images from homes with no internet access and incomes as low as $26.99 per

In this paper, we propose a methodology for the analysis of questionnaire data along with its application on discovering insights from investor data motivated by a day trading competition. The questionnaire includes categorical questions, which are reduced to binary questions, 'yes' or 'no'. The methodology reduces dimensionality by grouping questions and participants with similar responses using clustering analysis. Rule discovery was performed by using a conversion rate metric. Innovative visual representations were proposed to validate the cluster analysis and the relation discovery between questions. When crossing with financial data, additional insights were revealed related to the recognized clusters.

Skilled employees are the most important pillars of an organization. Despite this, most organizations face high attrition and turnover rates. While several machine learning models have been developed to analyze attrition and its causal factors, the interpretations of those models remain opaque. In this paper, we propose the HR-DSS approach, which stands for Human Resource (HR) Decision Support System, and uses explainable AI for employee attrition problems. The system is designed to assist HR departments in interpreting the predictions provided by machine learning models. In our experiments, we employ eight machine learning models to provide predictions. We further process the results achieved by the best-performing model by the SHAP explainability process and use the SHAP values to generate natural language explanations which can be valuable for HR. Furthermore, using "What-if-analysis", we aim to observe plausible causes for attrition of an individual employee. The results show that by adjusting the specific dominant features of each individual, employee attrition can turn into employee retention through informative business decisions.

In this article, we will go through the concept of outliers in statistics and its application in the field of Machine Learning. Starting from scratch, we will build up to identifying outliers and…

Object recognition algorithms sold by tech companies, including Google, Microsoft, and Amazon, perform worse when asked to identify items from lower-income countries. These are the findings of a new study conducted by Facebook's AI lab, which shows that AI bias can not only reproduce inequalities within countries, but also between them. In the study (which we spotted via Jack Clark's Import AI newsletter), researchers tested five popular off-the-shelf object recognition algorithms -- Microsoft Azure, Clarifai, Google Cloud Vision, Amazon Rekognition, and IBM Watson -- to see how well each program identified household items collected from a global dataset. The dataset included 117 categories (everything from shoes to soap to sofas) and a diverse array of household incomes and geographic locations (from a family in Burundi making $27 a month to a family in Ukraine with a monthly income of $10,090). The researchers found that the object recognition algorithms made around 10 percent more errors when asked to identify items from a household with a $50 monthly income compared to those from a household making more than $3,500.