Regulation and ethics in artificial intelligence and machine learning technologies: Where are we now? Who is responsible? Can the information professional play a role?

2020 ◽  
Vol 37 (2) ◽  
pp. 60-68
Author(s):  
Denise Carter
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
White Paper ◽  
Learning Technologies ◽  
Public And Private ◽  
General Data Protection Regulation ◽  
Oecd Principles ◽  
Information Professional ◽  
Uk Government ◽  
The Uk

Artificial intelligence (AI) and machine learning (ML) technologies are rapidly maturing and proliferating through all public and private sectors. The potential for these technologies to do good and to help us in our everyday lives is immense. But there is a risk that unless managed and controlled AI can also cause us harm. Questions about regulation, what form it takes and who is responsible for governance are only just beginning to be answered. In May 2019, 42 countries came together to support a global governance framework for AI. The Organisation for Economic Co-operation and Development (OECD) Principles on Artificial Intelligence (OECD (2019) OECD principles on AI. Available at: https://www.oecd.org/going-digital/ai/principles/ (accessed 2 March 2020)) saw like-minded democracies of the world commit to common AI values of trust and respect. In Europe, the European Commission’s (EC) new president, Ursula von der Leyen has made calls for a General Data Protection Regulation style. As a first step the EC has published a white paper: ‘On Artificial Intelligence – A European Approach to Excellence and Trust’ (European Commission (2020) Report, Europa, February). In February 2020, the UK government has published a report on ‘Artificial Intelligence in the Public Sector’ (The Committee on Standards in Public Life (2020) Artificial intelligence and public standards. Report, UK Government, February). This article discusses some of the potential threats AI may hold if left unregulated. It provides a brief overview of the regulatory activities for AI worldwide, and in more detail the current UK AI regulatory landscape. Finally, the article looks at the role that the information professional might play in AI and ML.

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 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.

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.

Artificial intelligence: accelerator or panacea for financial crime?

2019 ◽  
Vol 26 (2) ◽  
pp. 634-646 ◽  
Author(s):  
Peter Yeoh
Keyword(s):  
Artificial Intelligence ◽  
Secondary Data ◽  
Financial Crime ◽  
Public And Private ◽  
Content Type ◽  
Competitive Edge ◽  
Business Cases ◽  
The Usa ◽  
The Uk ◽  
Administrative Sanctions

PurposeThis purpose of this viewpoint is to address the intended good and unintended bad impacts of artificial intelligence (AI) applications in financial crime.Design/methodology/approachThe paper relied primarily on secondary data resources, business cases and relevant laws and regulations, and it used a legal-economics perspective.FindingsCurrent AI systems could function as antidotes or accelerator of financial crime, in particular cybercrime. Research suggests criminal law could be applied via three approaches to curb these cybercrimes. However, others considered this to be an inappropriate mechanism to hold AI agents accountable, as present AI systems were not deemed capable of making ethically informed choices. Instead, administrative sanctions would be considered more appropriate for now. While keeping vigilance against AI malicious acts, regulatory authorities in the USA and the UK have opted largely for the innovation-friendly, market-oriented, permissionless approach over the state-interventionist stance so as to maintain their global competitive edge in this domain.Originality/valueThe paper reinforced the growing arguments that AI applications should be deployed more as panacea for financial crimes rather than being abused as crime accelerators. There equally though is the need for both public and private sectors to be mindful of the unintended negative, harmful consequences to society, especially those connected to cybercrime. This implied the further need to beef up attention and resources to help mitigate these risks.

The UK's proposed foreign direct investment regime: the tide is turning

2018 ◽  
Vol 17 (2) ◽  
pp. 73-78
Author(s):  
Veronica Roberts
Keyword(s):  
Foreign Direct Investment ◽  
National Security ◽  
Foreign Investment ◽  
Direct Investment ◽  
White Paper ◽  
Security Risk ◽  
Global Context ◽  
Uk Government ◽  
The Government ◽  
The Uk

The UK Government has recently published a White Paper proposing the creation of a new foreign investment regime, under which the Government would have powers to review a very broad range of transactions if they give rise to a national security risk. This article reviews the key provisions of the Government's proposal and also highlights the broader global context, with a number of other countries also expanding their own foreign investment regimes.

scholarly journals Advancing Post-Genome Data and System Integration through Machine Learning

2002 ◽  
Vol 3 (1) ◽  
pp. 28-31 ◽  
Author(s):  
Francisco Azuaje
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
System Integration ◽  
Intelligent Systems ◽  
Biological Data ◽  
Markup Languages ◽  
Public And Private ◽  
Genome Data ◽  
The Creation ◽  
Biological Data Integration

Research on biological data integration has traditionally focused on the development of systems for the maintenance and interconnection of databases. In the next few years, public and private biotechnology organisations will expand their actions to promote the creation of a post-genome semantic web. It has commonly been accepted that artificial intelligence and data mining techniques may support the interpretation of huge amounts of integrated data. But at the same time, these research disciplines are contributing to the creation of content markup languages and sophisticated programs able to exploit the constraints and preferences of user domains. This paper discusses a number of issues on intelligent systems for the integration of bioinformatic resources.

scholarly journals Automation of business processes of entrepreneurial activity in the field of real estate based on artificial intelligence technologies

2021 ◽  
Vol 18 (5) ◽  
pp. 150-159
Author(s):  
A. N. Asaul ◽  
◽  
G. F. Shcherbina ◽  
M. A. Asaul ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Estate ◽  
Business Processes ◽  
Learning Technologies ◽  
Entrepreneurial Activity ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Sector ◽  
Entrepreneurial Activities

The article refines the concept of «business process», the essence of business processes` automation in entrepreneurial activities is considered through the use of artificial intelligence and machine learning technologies for IT integration in the real estate sector. Based on the market analysis, the state of development of artificial intelligence and machine learning in Russia, its significance and prospects for implementation in business activities in the real estate sector are studied.

scholarly journals Data Analytics and Public Safety

2021 ◽  
Vol 3 (3) ◽  
pp. 59-62
Author(s):  
Mark Masongsong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
West Coast ◽  
Data Analytics ◽  
Public Safety ◽  
Learning Technologies ◽  
Key Points

On November 27, 2020, UrbanLogiq CEO Mark Masongsong spoke on the topic of Data Analytics and Public Safety at the 2020 CASIS West Coast Security Conference. The key points of discussion focused on the challenges of artificial intelligence and machine learning technologies and their utility towards public safety. 

scholarly journals Predictive Analysis of Taxi Fare using Machine Learning

2020 ◽  
pp. 373-378
Author(s):  
Pallab Banerjee ◽  
Biresh Kumar ◽  
Amarnath Singh ◽  
Priyeta Ranjan ◽  
Kunal Soni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Comparative Analysis ◽  
Supervised Learning ◽  
Analysis Of Algorithms ◽  
Learning Technologies ◽  
Predictive Analysis ◽  
Prediction Problem ◽  
Prediction Modeling

This research aims to study the predictive analysis, which is a method of analysis in Machine Learning. Many companies like Ola, Uber etc uses Artificial Intelligence and machine learning technologies to find the solution of accurate fare prediction problem. We are proposing this paper after comparative analysis of algorithms like regression and classification, which are useful for prediction modeling to get the most accurate value. This research will be helpful to those, who are involved in fare forecasting. In previous era, the fare was only dependent on distance, but with the enhancement in technologies the cab’s fare is dependent on a lot of factors like time, location, number of passengers, traffic, number of hours, base fare etc. The study is based on Supervised learning whose one application is prediction, in machine learning.

Divergence from EU on AI rules may hurt UK firms

2021 ◽  
Keyword(s):  
Artificial Intelligence ◽  
United Kingdom ◽  
New Technologies ◽  
Digital Economy ◽  
Content Type ◽  
The United Kingdom ◽  
Uk Government ◽  
The Uk ◽  
The Eu

Significance Freed from the EU’s control, London insists, the United Kingdom could become a hub for new technologies. To this end, besides setting out some other objectives, it has prepared a ten-year strategy to foster innovation in artificial intelligence (AI) systems. Impacts UK plans to adapt GDPR to favour business and innovation will be opposed by consumer and privacy activists, possibly in courts. Regulatory divergence with the EU in critical sectors such as the digital economy will hurt UK-EU ties. The UK government may struggle to benefit from partnerships developed in the EU-US Trade and Technology Council.

Sign in / Sign up

Export Citation Format

Share Document