An algorithmic decision-maker incentivizes people to act in certain ways to receive better decisions. These incentives can dramatically influence subjects' behaviors and lives, and it is important that both decision-makers and decision-recipients have clarity on which actions are incentivized by the chosen model. While for linear functions, the changes a subject is incentivized to make may be clear, we prove that for many non-linear functions (e.g. neural networks, random forests), classical methods for interpreting the behavior of models (e.g. input gradients) provide poor advice to individuals on which actions they should take. In this work, we propose a mathematical framework for understanding algorithmic incentives as the challenge of solving a Markov Decision Process, where the state includes the set of input features, and the reward is a function of the model's output. We can then leverage the many toolkits for solving MDPs (e.g. tree-based planning, reinforcement learning) to identify the optimal actions each individual is incentivized to take to improve their decision under a given model. We demonstrate the utility of our method by estimating the maximally-incentivized actions in two real-world settings: a recidivism risk predictor we train using ProPublica's COMPAS dataset, and an online credit scoring tool published by the Fair Isaac Corporation (FICO).

It is known that from purely observational data, a causal DAG is identifiable only up to its Markov equivalence class, and for many ground truth DAGs, the direction of a large portion of the edges will be remained unidentified. The golden standard for learning the causal DAG beyond Markov equivalence is to perform a sequence of interventions in the system and use the data gathered from the interventional distributions. We consider a setup in which given a budget $k$, we design $k$ interventions non-adaptively. We cast the problem of finding the best intervention target set as an optimization problem which aims to maximize the number of edges whose directions are identified due to the performed interventions. First, we consider the case that the underlying causal structure is a tree. For this case, we propose an efficient exact algorithm for the worst-case gain setup, as well as an approximate algorithm for the average gain setup. We then show that the proposed approach for the average gain setup can be extended to the case of general causal structures. In this case, besides the design of interventions, calculating the objective function is also challenging. We propose an efficient exact calculator as well as two estimators for this task. We evaluate the proposed methods using synthetic as well as real data.

M uller, 3 S oren Schleibaum, 3 Mark V ollrath 5 1 Department of Computer Science, Bar-Ilan University, Israel (email: sarit@cs.biu.ac.il) 2 Department of Computer Science, Ariel University, Israel 3 Department of Informatics, TU Clausthal, Germany 4 Chair of Information Management, Georg-August-Universitat G ottingen, Germany 5 Chair of Engineering and Traffic Psychology, TU Braunschweig, Germany Abstract Explanation is necessary for humans to understand and accept decisions made by an AI system when the system's goal is known. It is even more important when the AI system makes decisions in multi-agent environments where the human does not know the systems' goals since they may depend on other agents' preferences. In such situations, explanations should aim to increase user satisfaction, taking into account the system's decision, the user's and the other agents' preferences, the environment settings and properties such as fairness, envy and privacy. Generating explanations that will increase user satisfaction is very challenging; to this end, we propose a new research direction: Explainable decisions in Multi-Agent Environments (xMASE). We then review the state of the art and discuss research directions towards efficient methodologies and algorithms for generating explanations that will increase users' satisfaction from AI system's decisions in multi-agent environments. Introduction Many AI systems need to make decisions in multi-agent environments where the agents, including people and robots, have possibly conflicting preferences. The system should balance between these preferences when making decisions regarding all agents.

Recently image-to-image translation has attracted significant interests in the literature, starting from the successful use of the generative adversarial network (GAN), to the introduction of cyclic constraint, to extensions to multiple domains. However, in existing approaches, there is no guarantee that the mapping between two image domains is unique or one-to-one. Here we propose a self-inverse network learning approach for unpaired image-to-image translation. Building on top of CycleGAN, we learn a self-inverse function by simply augmenting the training samples by swapping inputs and outputs during training and with separated cycle consistency loss for each mapping direction. The outcome of such learning is a proven one-to-one mapping function. Our extensive experiments on a variety of datasets, including cross-modal medical image synthesis, object transfiguration, and semantic labeling, consistently demonstrate clear improvement over the CycleGAN method both qualitatively and quantitatively. Especially our proposed method reaches the state-of-the-art result on the cityscapes benchmark dataset for the label to photo unpaired directional image translation.

The astronauts set for the Artemis mission to the moon will put on suits that may look like today's gear, but will be redesigned with new technology to accomplish more complex tasks. The new space suits are set to allow for better mobility, allowing them to lift their arms and objects over their head and flexibility at the hips and knees, for smoother travel over the lunar surface. NASA also plans to take full-body, 3D scans of each astronaut to provide them with the most comfort and broadest range of motion. A famous video of Gene Cernan, the last man to walk on the moon, floating around the web that shows the American astronaut bunny hopping across the lunar surface – and this is one of the issues NASA hopes to eliminate with the new design. The new space hear is set to have interchangeable parts that can be used for spacewalks in microgravity or on a planetary surface.

Today, Amazon Web Services (AWS) announced that Amazon Textract, a machine learning service that quickly and easily extracts text and data from forms and tables in scanned documents, is now eligible for healthcare and life science workloads that require HIPAA compliance. This launch builds upon the existing portfolio of AWS artificial intelligence services that are HIPAA-eligible, including Amazon Translate, Amazon Comprehend, Amazon Transcribe, Amazon Polly, Amazon SageMaker and Amazon Rekognition – that help customers retrieve data from documents more accurately to reach better healthcare decisions, operate more efficiently, and help identify medical and scientific trends. Critical healthcare information often lies within documents such as medical records and forms. Healthcare and life science organizations need to access data that is locked inside those documents in order to fulfil medical claims, streamline administrative processes, and process electronic health records. They routinely extract text and data from documents through manual data entry or simple optical character recognition (OCR) software.

As a recent graduate of the Flatiron School's Data Science Bootcamp, I've been inundated with advice on how to ace technical interviews. A soft skill that keeps coming to the forefront is the ability to explain complex machine learning algorithms to a non-technical person. This series of posts is me sharing with the world how I would explain all the machine learning topics I come across on a regular basis...to my grandma. Some get a bit in-depth, others less so, but all I believe are useful to a non-Data Scientist. In the upcoming parts of this series, I'll be going over: "a model is like a Vending Machine, which given an input (money), will give you some output (a soda can maybe) . . . An algorithm is what is used to train a model, all the decisions a model is supposed to take based on the given input, to give an expected output. For example, an algorithm will decide based on the dollar value of the money given, and the product you chose, whether the money is enough or not, how much balance you are supposed to get [back], and so on."

ORLANDO, Fla.--(BUSINESS WIRE)--HIMSS19 Booth 3679--Change Healthcare today announced Claims Lifecycle Artificial Intelligence, a new capability being integrated into the company's Intelligent Healthcare NetworkTM and financial solutions, to help providers and payers optimize the entire claims processing lifecycle. This Change Healthcare Claims Lifecycle AI service is trained on more than 500 million service lines making up over 205 million unique claims that touch $268 billion in charges. Solutions and services across the Change Healthcare portfolio are using artificial intelligence (AI) to help customers with improving payment accuracy, reducing denials, enhancing payment forecasting, and reducing administrative overhead. "Our strategy is to bring AI capabilities to the entire healthcare financial and administrative ecosystem, and claims lifecycle management is the logical place to start," said Nick Giannasi, Ph.D., chief AI officer, Change Healthcare. "We're using AI to bend the cost/quality curve of healthcare. By applying AI to our Intelligent Healthcare Network data, combined with our pervasive presence in payer and provider workflows, we are delivering new health IT solutions that help customers address the financial pressures from healthcare costs in ways not previously possible. Applying AI will transform the claims lifecycle process."

The gymnastics world championships in Germany, the biggest gymnastics meet outside the Olympics, for the first time used an artificial intelligence system to evaluate athletes' performance. The gymnastics world championships in Germany, the biggest gymnastics meet outside the Olympics, for the first time used an artificial intelligence (AI) system to evaluate athletes' performance by measuring and analyzing skeletal positions, speed, and angles via three-dimensional laser sensors. International Gymnastics Federation president Morinari Watanabe envisions such robot judges eliminating human error and subjectivity from gymnastics contests; "this is a step toward the challenge of justice through technology," Watanabe said. At the world championships, the AI system was a means for human judges to confirm scores when gymnasts either formally contested their score, or the score widely deviated between judges. International Gymnastics Federation sports director Steve Butcher said all athlete information collected at the competition would be discarded at a predetermined expiration date, to address privacy concerns.

Deep neural networks excel at image recognition, but are easily hacked. A self-driving car approaches a stop sign, but instead of slowing down, it accelerates into the busy intersection. An accident report later reveals that four small rectangles had been stuck to the face of the sign. These fooled the car's onboard artificial intelligence (AI) into misreading the word'stop' as'speed limit 45'. Such an event hasn't actually happened, but the potential for sabotaging AI is very real.