For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training data, and the other is that the model is correctly specified. In real applications, however, we often have little prior knowledge on the test data and on the underlying true model. Under model misspecification, agnostic distribution shift between training and test data leads to inaccuracy of parameter estimation and instability of prediction across unknown test data. To address these problems, we propose a novel Decor-related Weighting Regression (DWR) algorithm which jointly optimizes a variable decorrelation regularizer and a weighted regression model. The variable decorrelation regularizer estimates a weight for each sample such that variables are decor-related on the weighted training data. Then, these weights are used in the weighted regression to improve the accuracy of estimation on the effect of each variable, thus help to improve the stability of prediction across unknown test data. Extensive experiments clearly demonstrate that our DWR algorithm can significantly improve the accuracy of parameter estimation and stability of prediction with model misspecification and agnostic distribution shift.

We demonstrate how easy it is for modern machine-learned systems to violate common deontological ethical principles and social norms such as "favor the less fortunate", and "do not penalize good attributes." We propose that in some cases such ethical principles can be incorporated into a machine-learned model by adding shape constraints that constrain the model to respond only positively to relevant inputs. We analyze the relationship between these deontological constraints that act on individuals and the consequentialist group-based fairness goals of one-sided statistical parity and equal opportunity. This strategy works with sensitive attributes that are Boolean or real-valued such as income and age, and can help produce more responsible and trustworthy AI.

Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs Anne-Katrin Mahlein & Kristian Kersting Abstract Deep neural networks have shown excellent performances in many real-world applications. Unfortunately, they may show "Clever Hans"-like behavior--making use of confounding factors within datasets--to achieve high performance. In this work we introduce the novel learning setting of explanatory interactive learning (XIL) and illustrate its benefits on a plant phenotyping research task. XIL adds the scientist into the training loop such that she interactively revises the original model via providing feedback on its explanations. Our experimental results demonstrate that XIL can help avoiding Clever Hans moments in machine learning and encourages (or discourages, if appropriate) trust into the underlying model. Imagine a plant phenotyping team attempting to characterize crop resistance to plant pathogens. The plant physiologist records a larger amount of hyperspectral imaging data. Impressed by the results of deep learning in other scientific areas, she wants to establish similar results for phenotyping. Consequently, she asks a machine learning expert to apply deep learning to analyze the data. Luckily, the resulting predictive accuracy is very high. The plant physiologist, however, remains skeptical. The results are "too good, to be true". Checking the decision process of the deep model using explainable artificial intelligence (AI), the machine learning expert is flabbergasted to find that the learned deep model uses clues within the data that do not relate to the biological problem at hand, so-called confounding factors. The physiologist loses trust in AI and turns away from it, proclaiming it to be useless. Indeed, the seminal paper of Lapuschkin et al. [3] helps in "unmasking Clever Hans predictors and assessing what machines really learn". However, rather than proclaiming, as the plant physiologist might, that the machines have learned the right predictions for wrong reasons and can therefore not be trusted, we here showcase that interactions between the learning system and the human user can correct the model towards making the right predictions for the right reasons. This may also increase the trust in machine learning models.

Will artificial intelligence (AI) create an environment where design thinking skills are more valuable than data science skills? Will AI alter how we define human intelligence? That sounds like questions one might expect from an episode of Rod Serling's TV series Twilight Zone. Instead of AI replacing humans, will AI actually make humans more human? Will characteristics such as empathy, compassion, and collaboration actually become the future high-value skills that are cherished by leading organizations?

As these teachers have come to understand, you don't have to be technically minded to introduce your students to the important concepts behind artificial intelligence. When computer science teacher Sharon Harrison wanted to introduce her eighth graders to the basic idea of artificial intelligence, she had them try out an online chatbot called Akinator, which asks the user questions to determine what historic or fictional character he or she is thinking of. In some instances, the students marveled at how quickly the program could figure out the answer. "Sometimes it would guess in three or four guesses, and we'd say, 'How on earth was it able to do that?'" From there, the discussion in this elective class at the University of Chicago Laboratory Schools examined how responses to the chatbot could be sabotaged -- by responding to the questions incorrectly, and thereby "damage the integrity of the program."

The University of Helsinki (https://www.helsinki.fi/en) is an international scientific community of 40,000 students and researchers. It is one of the leading multidisciplinary research universities in Europe and ranks among the top 100 international universities in the world. It offers comprehensive services to its employees, including occupational health care and health insurance, sports facilities, and opportunities for professional development. The Department of Mathematics and Statistics is the largest university department for mathematical sciences in Finland. The multifaceted research carried out at the Department of Mathematics and Statistics has received the highest points in several evaluations.

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After years in the (mostly Canadian) wilderness followed by seven years of plenty, Deep Learning was officially recognized as the "dominant" AI paradigm and "a critical component of computing," with its three key proponents, Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, receiving the Turing Award in March 2019. Turing Award winners (from left to right) Yoshua Bengio, Yann LeCun, and Geoffrey Hinton at the ... [ ] ReWork Deep Learning Summit, Montreal, October 2017. In October 2012, a deep neural network achieved an error rate of only 16% in the ImageNet Large Scale Visual Recognition Challenge, a significant improvement over the 25% error rate achieved by the best entry the year before. Yann LeCun: "The difference there was so great that a lot of people, you could see a big switch in their head going'clunk.' Now they were convinced;" Geoffrey Hinton: "Until we could produce results that were clearly better than the current state of the art, people were very skeptical;" Yoshua Bengio: "[Anyone hoping to make the next Turing-winning breakthrough in AI] should not follow the trend--which right now is deep learning." Deep Learning is a "critical component of computing"… or biology? As customary for Turing Awards laureates, Hinton, LeCun and Bengio delivered the A. M. Turing Lecture.

History teaches us that when you're in the middle of sweeping technology-driven change, the struggle to simply keep pace can make it hard to maintain perspective. This is the situation we find ourselves in now, with the rapid emergence of artificial intelligence (AI). Suddenly there's great urgency to explore how businesses and society can realize the benefits of AI while simultaneously grappling with the wide range of issues it has raised--issues like the future of employment and how we can preserve individual privacy and protect public safety. To do all this in a smart and balanced way requires a long-term view of what the decisions we make now will mean for the future. This is something that is probably a little easier to do at a company like Schneider Electric.

In many online learning problems the computational bottleneck for gradient-based methods is the projection operation. For this reason, in many problems the most efficient algorithms are based on the Frank-Wolfe method, which replaces projections by linear optimization. In the general case, however, online projection-free methods require more iterations than projection-based methods: the best known regret bound scales as $T^{3/4}$. Despite significant work on various variants of the Frank-Wolfe method, this bound has remained unchanged for a decade. In this paper we give an efficient projection-free algorithm that guarantees $T^{2/3}$ regret for general online convex optimization with smooth cost functions and one linear optimization computation per iteration. As opposed to previous Frank-Wolfe approaches, our algorithm is derived using the Follow-the-Perturbed-Leader method and is analyzed using an online primal-dual framework.