A recent paper from the Center for Applied Data Ethics (CADE) at the University of San Francisco urges AI practitioners to adopt terms from anthropology when reviewing the performance of large machine learning models. The research suggests using this terminology to interrogate and analyze bureaucracy, states, and power structures in order to critically assess the performance of large machine learning models with the potential to harm people. "This paper centers power as one of the factors designers need to identify and struggle with, alongside the ongoing conversations about biases in data and code, to understand why algorithmic systems tend to become inaccurate, absurd, harmful, and oppressive. This paper frames the massive algorithmic systems that harm marginalized groups as functionally similar to massive, sprawling administrative states that James Scott describes in Seeing Like a State," the author wrote. The paper was authored by CADE fellow Ali Alkhatib, with guidance from director Rachel Thomas and CADE fellows Nana Young and Razvan Amironesei. The researchers particularly look to the work of James Scott, who has examined hubris in administrative planning and sociotechnical systems.

We are living through an AI renaissance thought wholly unimaginable just a few decades ago -- automobiles are becoming increasingly autonomous, machine learning systems can craft prose nearly as well as human poets, and almost every smartphone on the market now comes equipped with an AI assistant. Oxford professor Michael Woolridge has spent the past quarter decade studying technology. In his new book, A Brief History of Artificial Intelligence, Woolridge leads readers on an exciting tour of the history of AI, its present capabilities, and where the field is heading into the future. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher. In his 1962 book, The Structure of Scientific Revolutions, the philosopher Thomas Kuhn argued that, as scientific understanding advances, there will be times when established scientific orthodoxy can no longer hold up under the strain of manifest failures.

The healthcare industry is straining under the impact of COVID-19. The sudden influx of patients in hospitals is exposing vulnerabilities in the current healthcare system. Some hospitals became hotspots for infection, disrupting routine healthcare procedures, while others closed their Outpatient Departments (OPDs), fearing transmission. This dire situation ushered in a massive digital transformation of the healthcare industry to improve care quality, reduce operational costs, and save time for treatments. Although the pandemic accelerated the transformation and saw pioneering research in medical science, healthcare advancement is a phased evolution.

Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable T. To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS. Specifically, we first propose the concept of N-structures, then design an efficient Markov Blanket (MB) discovery subroutine to integrate MB learning with N-structures to learn the MB of T and simultaneously distinguish direct causes from direct effects of T. With the proposed MB subroutine, ELCS starts from the target variable, sequentially finds MBs of variables connected to the target variable and simultaneously constructs local causal structures over MBs until the direct causes and direct effects of the target variable have been distinguished. Using eight Bayesian networks the extensive experiments have validated that ELCS achieves better accuracy and efficiency than the state-of-the-art algorithms.

One of the most important problems in transfer learning is the task of domain adaptation, where the goal is to apply an algorithm trained in one or more source domains to a different (but related) target domain. This paper deals with domain adaptation in the presence of covariate shift while there exist invariances across domains. A main limitation of existing causal inference methods for solving this problem is scalability. To overcome this difficulty, we propose SCTL, an algorithm that avoids an exhaustive search and identifies invariant causal features across the source and target domains based on Markov blanket discovery. SCTL does not require to have prior knowledge of the causal structure, the type of interventions, or the intervention targets. There is an intrinsic locality associated with SCTL that makes SCTL practically scalable and robust because local causal discovery increases the power of computational independence tests and makes the task of domain adaptation computationally tractable. We show the scalability and robustness of SCTL for domain adaptation using synthetic and real data sets in low-dimensional and high-dimensional settings.

This scholarship has been established to provide financial assistance to a PhD student to undertake research funded by an ARC Grant headed by Buhui Qiu within the University of Sydney Business School. The project will develop an innovative machine-learning-based approach for measuring, monitoring and evaluating bank lending activities and risk disclosures to take advantage of the big data available. It will use multidimensional data to produce more relevant metrics for assessing bank risks and risk disclosure quality and apply them in regulatory policy evaluation. The project findings will significantly advance the knowledge on mitigating banking misconduct. They will also equip regulatory authorities with an efficient monitoring tool and an early-warning device to promote better lending and risk disclosure practices, and foster a more transparent and stable financial system to support financial intermediation in Australia and worldwide.

Yoshua Bengio is one of the world's leading experts in artificial intelligence and deep learning. Also known as the father of deep learning, he says that for the world to change for the better with AI, a global shift in how organizations and governments share their research needs to come. In many countries, private companies, government entities, and academic institutions conduct AI research. These places must foster a global culture of open science. These research places the need to rethink how to encourage the development of impactful artificial intelligence.

Purpose: The development of metaheuristic algorithms has increased by researchers to use them extensively in the field of business, science, and engineering. One of the common metaheuristic optimization algorithms is called Grey Wolf Optimization (GWO). The algorithm works based on imitation of the wolves' searching and the process of attacking grey wolves. The main purpose of this paper to overcome the GWO problem which is trapping into local optima. Design or Methodology or Approach: In this paper, the K-means clustering algorithm is used to enhance the performance of the original Grey Wolf Optimization by dividing the population into different parts. The proposed algorithm is called K-means clustering Grey Wolf Optimization (KMGWO). Findings: Results illustrate the efficiency of KMGWO is superior to GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve 10 CEC2019 benchmark test functions. Results prove that KMGWO is better compared to GWO. KMGWO is also compared to Cat Swarm Optimization (CSO), Whale Optimization Algorithm-Bat Algorithm (WOA-BAT), and WOA, so, KMGWO achieves the first rank in terms of performance. Statistical results proved that KMGWO achieved a higher significant value compared to the compared algorithms. Also, the KMGWO is used to solve a pressure vessel design problem and it has outperformed results. Originality/value: Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA, and GWO so KMGWO achieved the first rank in terms of performance. Also, the KMGWO is used to solve a classical engineering problem and it is superior

The Varroa destructor mite is one of the most dangerous Honey Bee (Apis mellifera) parasites worldwide and the bee colonies have to be regularly monitored in order to control its spread. Here we present an object detector based method for health state monitoring of bee colonies. This method has the potential for online measurement and processing. In our experiment, we compare the YOLO and SSD object detectors along with the Deep SVDD anomaly detector. Based on the custom dataset with 600 ground-truth images of healthy and infected bees in various scenes, the detectors reached a high F1 score up to 0.874 in the infected bee detection and up to 0.727 in the detection of the Varroa Destructor mite itself. The results demonstrate the potential of this approach, which will be later used in the real-time computer vision based honey bee inspection system. To the best of our knowledge, this study is the first one using object detectors for this purpose. We expect that performance of those object detectors will enable us to inspect the health status of the honey bee colonies.

Another is the family of optimal transport central to many machine learning algorithms, with distances (Villani, 2008), including the Wasserstein-1 metric. Lipschitz constrained variants recently gaining In general, variational representations are supremums attention. Inspired by this, we generalize the of integral formulas taken over sets of functions, such as the so-called tight variational representation of f-Donsker-Varadhan formula (Donsker & Varadhan, 1976) divergences in the case of probability measures for the Kullback-Leibler divergence or the Kantorovich-on compact metric spaces to be taken over the Rubinstein formula (Villani, 2008) for the Wasserstein-1 space of Lipschitz functions vanishing at an arbitrary metric. Informally speaking, one can implement (Nowozin base point, characterize functions achieving et al., 2016; Arjovsky et al., 2017) such a formula by constructing the supremum in the variational representation, a real-valued neural network taking samples from propose a practical algorithm to calculate the the two probability measures as inputs, which is then trained tight convex conjugate of f-divergences compatible to maximize the integral formula in order to approximate with automatic differentiation frameworks, the supremum, resulting in a learned proxy to the actual define the Moreau-Yosida approximation of f-divergence of said probability measures. Implementing the divergences with respect to the Wasserstein-1 metric, Kantorovich-Rubinstein formula in such a way involves and derive the corresponding variational formulas, restricting the Lipschitz constant of the neural network (Gulrajani providing a generalization of a number et al., 2017; Petzka et al., 2018; Miyato et al., 2018), of recent results, novel special cases of interest which effectively stabilizes the approximation procedure.