Research

COCIR analyses application of medical device legislation to Artificial Intelligence

COCIR analyses application of medical device legislation to Artificial Intelligence

«The European Commission has shown its ambition in the area of artificial intelligence (AI) in its recent White Paper on Artificial Intelligence – a European approach to excellence and trust. This White Paper is at the same time a precursor of possible legislation of AI in products and services in the European Union. However, COCIR sees no need for novel regulatory frameworks for AI-based devices in Healthcare, because the requirements of EU MDR and EU IVDR in combination with GDPR are adequate to ensure that same excellence and trust.» (COCIR paper).

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IEEE Use Case–Criteria for Addressing Ethical Challenges in Transparency, Accountability, and Privacy of CTA/CTT

IEEE Use Case–Criteria for Addressing Ethical Challenges in Transparency, Accountability, and Privacy of CTA/CTT

There are substantial public health benefits gained through successfully alerting individuals and relevant public health institutions of a person’s exposure to a communicable disease. Contact tracing techniques have been applied to epidemiology for centuries, traditionally involving a manual process of interview and follow-up. This is time-consuming, difficult, and dangerous work. Manual processes are also open to incomplete information because they rely on individuals being willing and able to remember and report all contact possibilities.

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TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

This paper introduces TextAttack, a Python framework for adversarial attacks, data augmentation, and adversarial training in NLP. TextAttack builds attacks from four components: a goal function, a set of constraints, a transformation, and a search method. TextAttack’s modular design enables researchers to easily construct attacks from combinations of novel and existing components. TextAttack provides implementations of 16 adversarial attacks from the literature and supports a variety of models and datasets, including BERT and other transformers, and all GLUE tasks.

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Diagnostic uncertainty calibration: towards reliable machine predictions in medical domain

Diagnostic uncertainty calibration: towards reliable machine predictions in medical domain

We further formalize the metrics for higher-order statistics, including inter-rater disagreement, in a unified way, which enables us to assess the quality of distributional uncertainty. In addition, we propose a novel post-hoc calibration method that equips trained neural networks with calibrated distributions over class probability estimates. With a large-scale medical imaging application, we show that our approach significantly improves the quality of uncertainty estimates in multiple metrics.

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Disposable Identities are Elemental(s) in IoT

Disposable Identities are Elemental(s) in IoT

Rob wants to argue that if intent is linked to an incorrect assessment of identity, and thus not central to an ethics of behaviour, then this opens up an actionable set of actors actually at play in the digtial (IoT, 5G, AI) namely: objects (with added connectivity like NFC), machines with built in connectivity, animals & plants (as ecosystems) and humans alike , as they can be treated as entities.

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The State of AI Ethics Report (June 2020)

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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