scholarly journals Before and beyond trust: reliance in medical AI

2021 ◽  
pp. medethics-2020-107095
Author(s):  
Charalampia (Xaroula) Kerasidou ◽  
Angeliki Kerasidou ◽  
Monika Buscher ◽  
Stephen Wilkinson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
New Technology ◽  
Public Trust ◽  
Learning Technologies ◽  
Data Driven ◽  
Policy Makers ◽  
The Public ◽  
Regulatory Frameworks ◽  
Practice Of Medicine

Artificial intelligence (AI) is changing healthcare and the practice of medicine as data-driven science and machine-learning technologies, in particular, are contributing to a variety of medical and clinical tasks. Such advancements have also raised many questions, especially about public trust. As a response to these concerns there has been a concentrated effort from public bodies, policy-makers and technology companies leading the way in AI to address what is identified as a "public trust deficit". This paper argues that a focus on trust as the basis upon which a relationship between this new technology and the public is built is, at best, ineffective, at worst, inappropriate or even dangerous, as it diverts attention from what is actually needed to actively warrant trust. Instead of agonising about how to facilitate trust, a type of relationship which can leave those trusting vulnerable and exposed, we argue that efforts should be focused on the difficult and dynamic process of ensuring reliance underwritten by strong legal and regulatory frameworks. From there, trust could emerge but not merely as a means to an end. Instead, as something to work in practice towards; that is, the deserved result of an ongoing ethical relationship where there is the appropriate, enforceable and reliable regulatory infrastructure in place for problems, challenges and power asymmetries to be continuously accounted for and appropriately redressed.

scholarly journals Tekoäly, koneoppiminen ja teknologinen murros:

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Henriikka Vartiainen ◽  
Matti Tedre ◽  
Ilkka Jormanainen ◽  
Juho Kahila ◽  
Teemu Valtonen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Technology Education ◽  
Computer Software ◽  
Network Connectivity ◽  
Learning Technologies ◽  
Data Driven ◽  
Technological Transformation ◽  
The Future ◽  
Design Skills

Tekoälyn ja erityisesti uudet koneoppimisen tekniikat ovat teknologisen murroksen keskeisiä ajureita. Tänä päivänä koneoppiminen on myös yhä enemmän sulautumassa osaksi kehollista ja materiaalista maailmaa sekä vuorovaikutusta. Antureiden, verkkoyhteyksien ja tietokoneohjelmistojen kautta rakennukset, esineet ja tekstiilit ovat muuttumassa älykkäiden esineiden ja toimintojen verkostoiksi. Virtuaalisen, materiaalisen ja kehollisuuden uudenlainen kohtaaminen tarjoaa myös ennennäkemättömiä mahdollisuuksia sekä haasteita koneoppimisen sekä datalähtöisen suunnittelun ja innovoinnin tukemiseen kouluopetuksessa. Tämän artikkelin tavoitteena on rakentaa näkökulmia datatoimijuuteen sekä datalähtöiseen design-ajatteluun koneoppimisen muovaamassa maailmassa.  Artikkeli esittelee digitaalisen, materiaalisen sekä kehollisuuden uudenlaisia mahdollisuuksia sekä riskejä, joka tuo koneoppimisen ajamaan murrokseen liittyviä näkökulmia osaksi käsityön ja teknologiakasvatuksen tulevaisuudesta käytävää tieteellistä ja julkista keskustelua.   Artificial intelligence, machine learning, and technological transformation: Towards data agency and design skills for the future Abstract Artificial intelligence, and especially new machine learning technologies, are key drivers of technological breakthroughs. Today, machine learning is also increasingly merging into the physical and material world as well as into social interaction. Buildings, artifacts, and textiles are transforming into networks of smart objects and activities through sensors, network connectivity, and computer software.  These novel encounters of virtual, material, and bodily interactions also offer unprecedented opportunities and challenges to enhance understandings of machine learning and data-driven design in school education. This article aims to build perspectives on data agency and data-driven design needed in the age of machine learning. It also provides perspectives on the blurring boundaries of virtual, material, and physical worlds and in a manner that brings the breakthrough of machine learning into the scientific and public discussion about the future of craft and technology education. Keywords: artificial intelligence, machine learning, data-driven design, technology education, skills for the future

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

scholarly journals NeMoR: a New Method Based on Data-Driven for Neonatal Mortality Rate Forecasting

2021 ◽  
Author(s):  
Carlos Eduardo Beluzo ◽  
Luciana Correia Alves ◽  
Natália Martins Arruda ◽  
Cátia Sepetauskas ◽  
Everton Silva ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Neonatal Mortality ◽  
Regression Models ◽  
Mortality Rates ◽  
Data Driven ◽  
Health Policies ◽  
Neonatal Mortality Rate ◽  
Policy Makers ◽  
Public Health Policies

ABSTRACTReduction in child mortality is one of the United Nations Sustainable Development Goals for 2030. In Brazil, despite recent reduction in child mortality in the last decades, the neonatal mortality is a persistent problem and it is associated with the quality of prenatal, childbirth care and social-environmental factors. In a proper health system, the effect of some of these factors could be minimized by the appropriate number of newborn intensive care units, number of health care units, number of neonatal incubators and even by the correct level of instruction of mothers, which can lead to a proper care along the prenatal period. With the intent of providing knowledge resources for planning public health policies focused on neonatal mortality reduction, we propose a new data-driven machine leaning method for Neonatal Mortality Rate forecasting called NeMoR, which predicts neonatal mortality rates for 4 months ahead, using NeoDeathForecast, a monthly base time series dataset composed by these factors and by neonatal mortality rates history (2006-2016), having 57,816 samples, for all 438 Brazilian administrative health regions. In order to build the model, Extra-Tree, XGBoost Regressor, Gradient Boosting Regressor and Lasso machine learning regression models were evaluated and a hyperparameters search was also performed as a fine tune step. The method has been validated using São Paulo city data, mainly because of data quality. On the better configuration the method predicted the neonatal mortality rates with a Mean Square Error lower than 0.18. Besides that, the forecast results may be useful as it provides a way for policy makers to anticipate trends on neonatal mortality rates curves, an important resource for planning public health policies.Graphical AbstractHighlightsProposition of a new data-driven approach for neonatal mortality rate forecast, which provides a way for policy-makers to anticipate trends on neonatal mortality rates curves, making a better planning of health policies focused on NMR reduction possible;a method for NMR forecasting with a MSE lower than 0.18;an extensive evaluation of different Machine Learning (ML) regression models, as well as hyperparameters search, which accounts for the last stage in NeMoR;a new time series database for NMR prediction problems;a new features projection space for NMR forecasting problems, which considerably reduces errors in NRM prediction.

scholarly journals Artificial Intelligence Approach for Analyzing Anaemia Prevalence in Children and Adolescents in BRICS Countries: A Review

2021 ◽  
Vol 9 (1) ◽  
pp. 01-10
Author(s):  
Natisha Dukhi ◽  
Ronel Sewpaul ◽  
Machoene Derrick Sekgala ◽  
Olushina Olawale Awe
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Children And Adolescents ◽  
Risk Indicators ◽  
Machine Learning Techniques ◽  
Policy Makers ◽  
Public Health Burden ◽  
Brics Countries ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

Anemia prevalence, especially among children and adolescents, is a serious public health burden in the BRICS countries. This article gives an overview of the current anaemia status in children and adolescents in three BRICS countries, as part of a study that utilizes an artificial intelligence approach for analyzing anaemia prevalence in children and adolescents in South Africa, India and Russia. It posits that the use of machine learning in this area of health research is still novel. The weightage assessment of the crosslink between anaemia risk indicators using a machine learning approach will assist policy makers in identifying the areas of priority to intervene in the BRICS participating countries. Health interventions utilizing artificial intelligence and more specifically, machine learning techniques, remains nascent in LMICs but could lead to improved health outcomes.

scholarly journals Reports of the 2013 AAAI Spring Symposium Series

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
pp. 93-98 ◽  
Author(s):  
Vita Markman ◽  
Georgi Stojanov ◽  
Bipin Indurkhya ◽  
Takashi Kido ◽  
Keiki Takadama ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Development ◽  
Behavior Change ◽  
Autonomous Systems ◽  
Data Driven ◽  
Intelligent Robots ◽  
Symposium Series ◽  
Development Data ◽  
Weakly Supervised

The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25-27, 2013. The titles of the eight symposia were Analyzing Microtext, Creativity and (Early) Cognitive Development, Data Driven Wellness: From Self-Tracking to Behavior Change, Designing Intelligent Robots: Reintegrating AI II, Lifelong Machine Learning, Shikakeology: Designing Triggers for Behavior Change, Trust and Autonomous Systems, and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.

Network Security Policy Automation

2022 ◽  
pp. 83-112
Author(s):  
Myo Zarny ◽  
Meng Xu ◽  
Yi Sun
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Network Security ◽  
Security Policy ◽  
Learning Technologies ◽  
Use Cases ◽  
Hybrid Cloud ◽  
Cloud Environments ◽  
Enterprise Security

Network security policy automation enables enterprise security teams to keep pace with increasingly dynamic changes in on-premises and public/hybrid cloud environments. This chapter discusses the most common use cases for policy automation in the enterprise, and new automation methodologies to address them by taking the reader step-by-step through sample use cases. It also looks into how emerging automation solutions are using big data, artificial intelligence, and machine learning technologies to further accelerate network security policy automation and improve application and network security in the process.

Social Climate Change and the Modern Police Department

2017 ◽  
pp. 296-313
Author(s):  
Adrienne C. Bradford ◽  
Heather K. McElroy ◽  
Rachel Rosenblatt
Keyword(s):  
Law Enforcement ◽  
Mental Health Professionals ◽  
Police Officers ◽  
Health Professionals ◽  
New Technology ◽  
Police Department ◽  
Body Art ◽  
Policy Makers ◽  
The Public ◽  
Organizational Challenges

The advent of social media, blogs, smartphones, and the 24-hour all access news channels make information available to us constantly on the television, the internet, and even while mobile. This chapter highlights contemporary social and generational trends including the arrival of the Millennial generation into the workforce, legalization of marijuana, the mainstream acceptance of body art as a form of self-expression, and the influence of mass media on the lives of police officers, particularly in officer-involved shootings. These emerging factors challenge law enforcement managers to consider complex issues in the workplace while maintaining the core values, camaraderie, and professional standards inherent in policing. The public safety psychologist's role is also evolving with new technology, social developments, and organizational challenges. This chapter aims to encourage dialogue between mental health professionals, law enforcement managers, and policy-makers.

scholarly journals How to translate artificial intelligence? Myths and justifications in public discourse

2020 ◽  
Vol 7 (1) ◽  
pp. 205395172091996 ◽  
Author(s):  
Jonathan Roberge ◽  
Marius Senneville ◽  
Kevin Morin
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Science And Technology Studies ◽  
Public Discourse ◽  
Social Expectations ◽  
Specific Language ◽  
The Public ◽  
Gradual Introduction ◽  
Congressional Hearing ◽  
The Many

Automated technologies populating today’s online world rely on social expectations about how “smart” they appear to be. Algorithmic processing, as well as bias and missteps in the course of their development, all come to shape a cultural realm that in turn determines what they come to be about. It is our contention that a robust analytical frame could be derived from culturally driven Science and Technology Studies while focusing on Callon’s concept of translation. Excitement and apprehensions must find a specific language to move past a state of latency. Translations are thus contextual and highly performative, transforming justifications into legitimate claims, translators into discursive entrepreneurs, and power relations into new forms of governance and governmentality. In this piece, we discuss three cases in which artificial intelligence was deciphered to the public: (i) the Montreal Declaration for a Responsible Development of Artificial Intelligence, held as a prime example of how stakeholders manage to establish the terms of the debate on ethical artificial intelligence while avoiding substantive commitment; (ii) Mark Zuckerberg’s 2018 congressional hearing, where he construed machine learning as the solution to the many problems the platform might encounter; and (iii) the normative renegotiations surrounding the gradual introduction of “killer robots” in military engagements. Of interest are not only the rational arguments put forward, but also the rhetorical maneuvers deployed. Through the examination of the ramifications of these translations, we intend to show how they are constructed in face of and in relation to forms of criticisms, thus revealing the highly cybernetic deployment of artificial intelligence technologies.

Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery

2020 ◽  
Vol 50 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Changwon Suh ◽  
Clyde Fare ◽  
James A. Warren ◽  
Edward O. Pyzer-Knapp
Keyword(s):  
Machine Learning ◽  
Materials Science ◽  
State Of The Art ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Computational Learning ◽  
Data Intensive ◽  
Research Activities ◽  
Materials Genome

Machine learning, applied to chemical and materials data, is transforming the field of materials discovery and design, yet significant work is still required to fully take advantage of machine learning algorithms, tools, and methods. Here, we review the accomplishments to date of the community and assess the maturity of state-of-the-art, data-intensive research activities that combine perspectives from materials science and chemistry. We focus on three major themes—learning to see, learning to estimate, and learning to search materials—to show how advanced computational learning technologies are rapidly and successfully used to solve materials and chemistry problems. Additionally, we discuss a clear path toward a future where data-driven approaches to materials discovery and design are standard practice.

Geophysics Bright Spots

2020 ◽  
Vol 39 (7) ◽  
pp. 518-519
Author(s):  
Jyoti Behura
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Driven ◽  
Daily Routines ◽  
Theoretical Approaches ◽  
Bright Spots ◽  
Catch Up ◽  
A Minor ◽  
Silver Lining

Welcome to the latest installment of Geophysics Bright Spots. For most of us, the pandemic has upended our daily routines. Personally, a minor silver lining in all of this chaos has been the gain of a few extra hours every week. I have used this time to catch up on the fascinating work being done in the field of artificial intelligence and its applications to numerous disciplines. An emerging trend is physics-informed machine learning, which will help us bridge the gap between traditional theoretical approaches and more recent data-driven methodologies, leading to physically plausible and meaningful results. To follow is a list of research that the editors found interesting in the latest issue of Geophysics. I sincerely hope these articles enlighten you and take your mind off some of the chaos surrounding us.

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