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International scientific and technical publication on Artificial Intelligence | ISSN 2695-6411 | Officially founded in September, 2019

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The State of AI Ethics Report (June 2020)

It has never been more important that we keep a sharp eye out on the development of this field and how it is shaping our society and interactions with each other. With this inaugural edition of the State of AI Ethics we hope to bring forward the most important developments that caught our attention at the Montreal AI Ethics Institute this past quarter. Our goal is to help you navigate this ever-evolving field swiftly and allow you and your organization to make informed decisions.

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Montreal AI Ethics Institute: Response to the European Commission’s white paper on AI

In February 2020, the European Commission (EC) published a white paper entitled, On Artificial Intelligence – A European approach to excellence and trust. This paper outlines the EC’s policy options for the promotion and adoption of artificial intelligence (AI) in the European Union. We reviewed this paper and published a response addressing the EC’s plans to build an “ecosystem of excellence” and an “ecosystem of trust,” as well as the safety and liability implications of AI, the internet of things (IoT), and robotics.

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A Rigorous Analysis of Self‐Adaptation in Discrete Evolutionary Algorithms

A key challenge to making effective use of evolutionary algorithms (EAs) is to choose appropriate settings for their parameters. However, the appropriate parameter setting generally depends on the structure of the optimization problem, which is often unknown to the user. Non‐deterministic parameter control mechanisms adjust parameters using information obtained from the evolutionary process.

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Covid-19 and contact tracing apps

Multi-presenter format with exciting Speakers from the current European ICT research projects AI4EU (www.ai4eu.eu) and Helios (helios-social.com/) as well as Guest Speakers.

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Will the fashion industry survive without AI?

Yooneeque has made digitalisation its motto. An artificial intelligence called YOONA is the fashion designer here. This time again during the Berlin Fashion Week the latest outputs of the software were presented

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Microsoft NLP Best Practices

This repository contains examples and best practices for building NLP systems, provided as Jupyter notebooks and utility functions. The focus of the repository is on state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language

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The Super Duper NLP Repo & The Big Bad NLP Database

A database housing more than 100 Colab notebooks running ML code for various NLP tasks. Colab is an excellent destination to experiment with the latest models as it comes with a free GPU/TPU housed in Google’s back-end servers… And a collection of more than 400 NLP datasets that it include papers.

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The Virtual Humans Factory

Harnessing the power of supercomputer and patient modelling to deliver unparallelled medical insights and predict treatment outcomes for patients.

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Guideline for AI for medical products

The objective of this guideline is to provide medical device manufacturers and notified bodies instructions and to provide them with a concrete checklist to understand what the expectations of the notified bodies are, to promote step-by-step implementation of safety of medical devices, that implement artificial intelligence methods, in particular machine learning, to compensate for the lack of a harmonized standard (in the interim) to the greatest extent possible.

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A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

This paper contributes the first human-centered observational study of a deep learning system deployed directly in clinical care with patients. Through field observations and interviews at eleven clinics across Thailand, we explored the expectations and realities that nurses encounter in bringing a deep learning model into their clinical practices. First, we outline typical eye-screening workflows and challenges that nurses experience when screening hundreds of patients. Then, we explore the expectations nurses have for an AI-assisted eye screening process. Next, we present a human-centered, observational study of the deep learning system used in clinical care, examining nurses’ experiences with the system, and the socio-environmental factors that impacted system performance. Finally, we conclude with a discussion around applications of HCI methods to the evaluation of deep learning algorithms in clinical environments.

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A socio-technical framework for digital contact tracing

In their efforts to tackle the COVID-19 crisis, decision makers are considering the development and use of smartphone applications for contact tracing. Even though these applications differ in technology and methods, there is an increasing concern about their implications for privacy and human rights. Here we propose a framework to evaluate their suitability in terms of impact on the users, employed technology and governance methods.

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#Standards4Quantum: Making Quantum Technology Ready for Industry

The Joint Research Center (JRC) in cooperation with the European Committee for Standardization (CEN) and the European Committee for Electrotechnical Standardization (CENELEC), European Commission’s Directorate General Communications Networks, Content and Technology (DG CNECT), and the German Institute of Standardisation (DIN), organised in Brussels on 28-29 March 2019 the Putting-Science-Into-Standards (PSIS) workshop on Quantum Technologies.

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Artificial Intelligence and Machine Learning in Software as a Medical Device: discussion Paper and Request for Feedback

Artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care. The FDA is considering a total product lifecycle-based regulatory framework for these technologies.

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The need for a system view to regulate artificial intelligence/machine learning-based software as medical device

FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition

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ICT security certification opportunities in the healthcare sector

Digital solutions for healthcare open a plethora of new possibilities in this area. They provide a technical base for easy testing, they improve significantly the quality of service by allowing immediate access to medical data – results of tests, history of treatment; they facilitate correct diagnosis by easier analytics and correlation of data and easier monitoring of patients’ health parameters. They facilitate setting up appointments with appropriate doctors at a convenient time

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