When hiring, many organizations use artificial intelligence tools to scan resumes and predict job-relevant skills. Colleges and universities use AI to automatically score essays, process transcripts and review extracurricular activities to predetermine who is likely to be a "good student." With so many unique use-cases, it is important to ask: can AI tools ever be truly unbiased decision-makers? In response to claims of unfairness and bias in tools used in hiring, college admissions, predictive policing, health interventions, and more, the University of Minnesota recently developed a new set of auditing guidelines for AI tools. The auditing guidelines, published in the American Psychologist, were developed by Richard Landers, associate professor of psychology at the University of Minnesota, and Tara Behrend from Purdue University.

When I first got out of Law School in the 1980s, "professionals" didn't type ... that was your assistant's job (or the "typing pool," which was a real thing too). At that point, most people couldn't have imagined what computers and software are capable of now. And if you tried to tell people how pervasive computers and'typing' would be ... they would have thought that you were delusional. My career has spanned a series of cycles where I was able to imagine what advanced tech would enable (and how businesses would have to change to best leverage those new capabilities). Malcolm Gladwell suggests that it takes 10,000 hours of focus and effort for someone to become an expert at something.

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Course Description: Machine learning has recently become the dominant field in AI research and constitutes the main part of the tools applied in industry-based AI positions. Business analysts, data scientists and AI engineers are required to know machine learning at different levels. The course (INF2179H -- Machine Learning with Applications in Python) will give a broad high-level overview of state-of-the-art machine learning methodologies. We shall focus on the application of these techniques to real-world data using the most advanced tools available for Python. The techniques will include: linear regression, basic techniques for classification, advanced regression and classification methods, and unsupervised learning.

Women in AI Ireland in collaboration with Women in Research Ireland present this live virtual event: Artificial Intelligence #BreaktheBias. With this event, we celebrate technical competencies in Artificial Intelligence for both industry and academia by highlighting different pathways from academia to industry and challenges therein to mark the International Women's Day 2022! Dr. Georgiana Ifrim is an Associate Professor at the School of Computer Science, UCD, co-lead of the SFI Centre for Research Training in Machine Learning (ML-Labs) and SFI Funded Investigator with the Insight Centre for Data Analytics and VistaMilk SFI Centre. Dr Ifrim holds a PhD and MSc in Machine Learning, from Max-Planck Institute for Informatics, Germany, and a BSc in Computer Science, from University of Bucharest, Romania. Her research focuses on effective approaches for large scale sequence learning, time series classification and text mining.

You’ll develop foundational technical knowledge of machine learning and the business applications of artificial intelligence across industries.

Warwick Business School's Distance Learning MBA started 36 years ago as a postal course -- a mode of delivery that must seem positively quaint to any students born in that inaugural year of 1986. Today's learners access the course via a bespoke online platform which, Warwick says, enables them to "engage in lectures in real time . . . As online MBA providers vie to attract students, all are becoming more inventive in the way they deliver content. Before long, technologies such as virtual reality and artificial intelligence may make current courses look as outdated as an envelope of study materials thudding on to a doormat. Investment has been accelerated by the coronavirus pandemic, which forced business schools to teach even conventional MBA students remotely. Find out which schools are in our ranking of Online MBA degrees. Take a look at our analysis and methodology. Also, read the rest of our coverage at www.ft.com/online-learning. Warwick's technology now includes green-screen video studios that allow presenters to be superimposed on different backgrounds. "We take some content from a member of faculty that's a flat information-sharing process," says Dot Powell, the school's director of teaching and learning enhancement. "Around that, we'll design activities, interactive features and encourage the students to engage with the content and with each other.

A Vietnamese woman who lives in Miyagi Prefecture sent a message to the "letters from readers" section of the Kahoku Shimpo expressing a grievance. The letter explained that when she informed the Japanese-language school where she had been studying that she had to cancel her enrollment because of financial hardship stemming from the pandemic, she was about to pay a cancellation fee of ¥3 million, after being pressured by the school to do so. The school said that it asks for such a fee to discourage students from quitting the school and switching to a work visa, but experts say the approach takes advantage of students' weak position and is a violation of their human rights. In November 2020, the woman, who is in her 30s, obtained a student visa, came to Japan and entered the Japanese-language school in Sendai's Aoba Ward. She planned to study Japanese for two years with a goal of becoming a nursing care worker in Japan.

In high-stake scenarios like medical treatment and auto-piloting, it's risky or even infeasible to collect online experimental data to train the agent. Simulation-based training can alleviate this issue, but may suffer from its inherent mismatches from the simulator and real environment. It is therefore imperative to utilize the simulator to learn a robust policy for the real-world deployment. In this work, we consider policy learning for Robust Markov Decision Processes (RMDP), where the agent tries to seek a robust policy with respect to unexpected perturbations on the environments. Specifically, we focus on the setting where the training environment can be characterized as a generative model and a constrained perturbation can be added to the model during testing. Our goal is to identify a near-optimal robust policy for the perturbed testing environment, which introduces additional technical difficulties as we need to simultaneously estimate the training environment uncertainty from samples and find the worst-case perturbation for testing. To solve this issue, we propose a generic method which formalizes the perturbation as an opponent to obtain a two-player zero-sum game, and further show that the Nash Equilibrium corresponds to the robust policy. We prove that, with a polynomial number of samples from the generative model, our algorithm can find a near-optimal robust policy with a high probability. Our method is able to deal with general perturbations under some mild assumptions and can also be extended to more complex problems like robust partial observable Markov decision process, thanks to the game-theoretical formulation.

Given a supervised machine learning problem where the training set has been subject to a known sampling bias, how can a model be trained to fit the original dataset? We achieve this through the Bayesian inference framework by altering the posterior distribution to account for the sampling function. We then apply this solution to binary logistic regression, and discuss scenarios where a dataset might be subject to intentional sample bias such as label imbalance. This technique is widely applicable for statistical inference on big data, from the medical sciences to image recognition to marketing. Familiarity with it will give the practitioner tools to improve their inference pipeline from data collection to model selection.