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Archive · 2018-10-01

AI ethics on Monday, 1 October 2018

4 items published this day, across 1 categories.

Research (4)

Digital adherence technologies for the management of tuberculosis therapy: mapping the landscape and research priorities

Poor medication adherence may increase rates of loss to follow-up, disease relapse and drug resistance for individuals with active tuberculosis (TB). While TB programmes have historically used directly observed therapy (DOT) to address adherence, concerns have been raised about the patient burden, ethical limitations, effectiveness in improving treatment outcomes and long-term feasibility of DOT for health systems. Digital adherence technologies (DATs)-which include feature phone-based and smart
OpenAlex 2860d ago Healthcare

Explaining Explanations: An Overview of Interpretability of Machine Learning

There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions in some level of detail. These explanations are important to ensure algorithmic fairness, identify potential bias/problems in the training
OpenAlex 2860d ago Bias & fairnessSafety & alignment

Sustainable Industrial Value Creation in SMEs: A Comparison between Industry 4.0 and Made in China 2025

Abstract The Industrial Internet of Things (IIoT) confronts industrial manufactures with economic, ecological, as well as social benefits and challenges, referring to the Triple Bottom Line of sustainability. So far, research has mainly investigated its dimensions in isolation or economic aspects have not been compared with ecological and social perspectives. Further, research misses studies that are devoted to the special characteristics and requirements of Small and Medium-sized Enterprises (S
OpenAlex 2860d ago Finance, VC & PE

Logistic regression model training based on the approximate homomorphic encryption

BACKGROUND: Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives. METHODS: This paper presents a metho
OpenAlex 2860d ago Privacy