Interpretable machine learning (IML) becomes increasingly important in highly regulated industry sectors related to the health and safety or fundamental rights of human beings. In general, the inherently IML models should be adopted because of their transparency and explainability, while black-box models with model-agnostic explainability can be more difficult to defend under regulatory scrutiny. For assessing inherent interpretability of a machine learning model, we propose a qualitative template based on feature effects and model architecture constraints. It provides the design principles for high-performance IML model development, with examples given by reviewing our recent works on ExNN, GAMI-Net, SIMTree, and the Aletheia toolkit for local linear interpretability of deep ReLU networks. We further demonstrate how to design an interpretable ReLU DNN model with evaluation of conceptual soundness for a real case study of predicting credit default in home lending. We hope that this work will provide a practical guide of developing inherently IML models in high risk applications in banking industry, as well as other sectors.

In an entailment relation, if p is true is a difficult task that requires an accurate understanding then h cannot be false, otherwise there is a contradiction. of natural language meaning. The ambiguity and variability NLI is a broader task than conflict identification, and thus, of linguistic expression in natural language complicates the good models to classify logical relations will naturally be recognition of these relations such as entailment and contradiction applicable to detect contract conflicts. Importantly, since contained in texts. The ability to classify these logical NLI has seen a surge in research, including new machine inferences among different text is a significant feature learning models and dataset curation (Bowman et al. 2015; of an intelligent system (Bos and Markert 2005). Detecting Williams, Nangia, and Bowman 2018), it offers substantial these logical relations can help humans to interpret a more labelled training data in much larger quantities than contract complex text, where entailment and contradiction are crucial conflict datasets (Aires, Pinheiro, and Meneguzzi 2017).

The task of assigning and validating internationally accepted commodity code (HS code) to traded goods is one of the critical functions at the customs office. This decision is crucial to importers and exporters, as it determines the tariff rate. However, similar to court decisions made by judges, the task can be non-trivial even for experienced customs officers. The current paper proposes a deep learning model to assist this seemingly challenging HS code classification. Together with Korea Customs Service, we built a decision model based on KoELECTRA that suggests the most likely heading and subheadings (i.e., the first four and six digits) of the HS code. Evaluation on 129,084 past cases shows that the top-3 suggestions made by our model have an accuracy of 95.5% in classifying 265 subheadings. This promising result implies algorithms may reduce the time and effort taken by customs officers substantially by assisting the HS code classification task.

Although it claims to protect our liberties, the EU's draft text on artificial intelligence (AI), presented by Margrethe Vestager, actually promotes the accelerated development of all aspects of AI, in particular for security purposes. Loaded with exceptions, resting on a stale risk-based approach, and picking up the French government's rhetoric on the need for more experimentation, this text should be modified down to its foundation. In its current state it risks endangering the slim legal protections that European law holds out in face of the massive deployment of surveillance techniques in public space. On April 21, 2021 the European Commission (EC) published a regulation proposal for a "European approach" to AI, accompanied by a coordinating plan to guide member states' action for the years to come. Beyond the rethoric of the European Commission, the draft regulation is deeply insufficient in how it treats the danger that AI systems represent for fundamental freedoms.

The U.S. Centers for Disease Control and Prevention estimates that one in seven children in the United States experienced abuse or neglect in the past year. Child protective services agencies around the nation receive a high number of reports each year (about 4.4 million in 2019) of alleged neglect or abuse. With so many cases, some agencies are implementing machine learning models to help child welfare specialists screen cases and determine which to recommend for further investigation. But these models don't do any good if the humans they are intended to help don't understand or trust their outputs. Researchers at MIT and elsewhere launched a research project to identify and tackle machine learning usability challenges in child welfare screening.

The promises of quantum computing, artificial intelligence, and other advancing technologies sound like magic. However, even magic is subject to the laws of economics. And even quantum computers are "legal things…technological tools that are bound to affect our lives in a tangible manner," as Valentin Jeutner explains in The Quantum Imperative: Addressing the Legal Dimension of Quantum Computers. Analogous to Asimov's Three Laws of Robotics, Professor Jeutner proposes a three-part "quantum imperative," which "provides that regulators and developers must ensure that the development of quantum computers: 1. does not create or exacerbate inequalities, 2. does not undermine individual autonomy, 3. does not occur without consulting those whose interests they affect." Should regulators seek to apply these principles?

OpenFL is an open-source framework for Federated Learning (FL) developed at Intel. FL is a technique for training statistical models on sharded datasets, distributed across several nodes. Moreover, data may be not identically distributed between different shards and cannot be moved between nodes, due to privacy / legal concerns (laws such as HIPAA or GDPR), size of the dataset, or other reasons. OpenFL is designed to solve so-called cross-silo federated learning problems when data is split between organizations or remote data centers. OpenFL aims to provide an effective and secure infrastructure for data scientists. With the v1.2 update OpenFL team endeavors to raise the framework's learnability and decouple the procedure of setting up the Federation and using it to run FL experiments.

In the global AI investment, innovation and implementation stakes, the UK lies in a creditable third place. Trailing the US and second-placed China, it holds a slight lead over Canada and South Korea, according to the Global AI Index published in December 2020 by Tortoise Media. The moral of Aesop's most famous fable involving a tortoise may be'more haste, less speed', but Westminster is seeking to hare ahead in this race over the coming decade. Its national AI strategy, published in September 2021, is a 10-year plan to make the country an "AI superpower". But what does that mean exactly?

Facebook trained its artificial intelligence systems to detect and block any future attempt to livestream a shooting spree with "police/military body cams footage," and other violent material, in the aftermath of the Christchurch terror attack. The emergency exercise – detailed in corporate papers leaked by whistleblower Frances Haugen – followed the March 2019 mass murder in the New Zealand city, described internally as "a watershed moment" for the Facebook Live video service. The white supremacist attacker was able to broadcast a 17-minute live stream of the attack on two mosques that was not detected by the company's systems, allowing it to be swiftly replicated online. "It was clear that Live was a vulnerable surface which can be repurposed by bad actors to cause societal harm," the leaked review stated. "Since this event, we've faced international media pressure and have seen regulatory and legal risks increase on Facebook increase considerably."

Quanergy Systems, Inc., a leading provider of OPA-based solid state LiDAR sensors and smart 3D solutions for automotive and IoT, announced the release of QORTEX DTC 2.2, the latest version of its 3D perception software, designed for advanced smart city and security applications. In June, Quanergy entered into a definitive merger agreement with CITIC Capital Acquisition Corp. (NYSE: CCAC) ("CCAC"). Upon closing of the transaction, the combined company will be named Quanergy Systems, Inc. and is expected to be listed on the New York Stock Exchange (NYSE) under the ticker symbol "QNGY." The transaction is expected to close in the fourth quarter of 2021, subject to satisfaction of customary closing conditions. QORTEX DTC is a core proprietary computer vision software platform compatible with Quanergy's suite of LiDAR sensors.