Fundamentals of Responsible Artificial Intelligence/ML

ARTIFICIAL INTELLIGENCE? NATURAL KNOWLEDGEThere's no doubt that AI is everywhere.In our cell phones, our computers, our cars, our apps, and many other aspects of life.Knowing how to design and train effective AI and ML models is not an easy task.But even when you master it, these may not be responsible.Due to bias, error, malicious management, or other factors, AI/ML models may hurt data subjects.There are, naturally, several courses on fragmented topics of the AI industry.How to train models, how to debias datasets, and other specific areas.Frequently, you can find information on aspects of AI, or on aspects of responsible AI. But not both.And on top that, many courses use different definitions, so you may become confused.In short, most courses on AI don't present a single, united source of training on responsible AI.And this has consequences not just for your career, but yourself personally as well.What happens when you don't have enough information (or in the adequate format)?You'll become confused by what are responsible - and irresponsible - algorithms. Do we need debiasing? Do we need explainability? Others?You won't be able to properly diagnose - and address - model problems that may be hurting data subjects;You'll become frustrated and irritated due to not knowing what is wrong with a specific model;You won't be able to make choices in terms of the models - from specific to sensitive classifiers, accurate versus explainable models, or many other specific model inferences - that, each, carry ethical consequences;You won't be able to tell an employer - or the end user - that you can design "responsible" AI, with confidence;So, if you want to know everything about what makes AI/ML models responsible (or not), as well as how to address issues, where should you