scholarly journals Actionable Principles for Artificial Intelligence Policy: Three Pathways

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Charlotte Stix
Keyword(s):  
Artificial Intelligence ◽  
Development Process ◽  
Stakeholder Participation ◽  
Expert Group ◽  
Governmental Policy ◽  
Ai Ethics ◽  
Ethics Guidelines ◽  
Multi Stakeholder ◽  
High Level

AbstractIn the development of governmental policy for artificial intelligence (AI) that is informed by ethics, one avenue currently pursued is that of drawing on “AI Ethics Principles”. However, these AI Ethics Principles often fail to be actioned in governmental policy. This paper proposes a novel framework for the development of ‘Actionable Principles for AI’. The approach acknowledges the relevance of AI Ethics Principles and homes in on methodological elements to increase their practical implementability in policy processes. As a case study, elements are extracted from the development process of the Ethics Guidelines for Trustworthy AI of the European Commission’s “High Level Expert Group on AI”. Subsequently, these elements are expanded on and evaluated in light of their ability to contribute to a prototype framework for the development of 'Actionable Principles for AI'. The paper proposes the following three propositions for the formation of such a prototype framework: (1) preliminary landscape assessments; (2) multi-stakeholder participation and cross-sectoral feedback; and, (3) mechanisms to support implementation and operationalizability.

scholarly journals In AI We Trust: Ethics, Artificial Intelligence, and Reliability

2020 ◽  
Vol 26 (5) ◽  
pp. 2749-2767
Author(s):  
Mark Ryan
Keyword(s):  
Artificial Intelligence ◽  
Interpersonal Trust ◽  
Expert Group ◽  
Human Relationships ◽  
Ai Ethics ◽  
Ethics Guidelines ◽  
High Level ◽  
Relationship Of

Abstract One of the main difficulties in assessing artificial intelligence (AI) is the tendency for people to anthropomorphise it. This becomes particularly problematic when we attach human moral activities to AI. For example, the European Commission’s High-level Expert Group on AI (HLEG) have adopted the position that we should establish a relationship of trust with AI and should cultivate trustworthy AI (HLEG AI Ethics guidelines for trustworthy AI, 2019, p. 35). Trust is one of the most important and defining activities in human relationships, so proposing that AI should be trusted, is a very serious claim. This paper will show that AI cannot be something that has the capacity to be trusted according to the most prevalent definitions of trust because it does not possess emotive states or can be held responsible for their actions—requirements of the affective and normative accounts of trust. While AI meets all of the requirements of the rational account of trust, it will be shown that this is not actually a type of trust at all, but is instead, a form of reliance. Ultimately, even complex machines such as AI should not be viewed as trustworthy as this undermines the value of interpersonal trust, anthropomorphises AI, and diverts responsibility from those developing and using them.

Europe

2020 ◽  
pp. 649-666
Author(s):  
Andrea Renda
Keyword(s):  
Artificial Intelligence ◽  
Digital Technology ◽  
Policy Framework ◽  
Expert Group ◽  
Ethical Guidelines ◽  
Ethical Implications ◽  
Strong Focus ◽  
Ethics Guidelines ◽  
Definition Of ◽  
High Level

This chapter assesses Europe’s efforts in developing a full-fledged strategy on the human and ethical implications of artificial intelligence (AI). The strong focus on ethics in the European Union’s AI strategy should be seen in the context of an overall strategy that aims at protecting citizens and civil society from abuses of digital technology but also as part of a competitiveness-oriented strategy aimed at raising the standards for access to Europe’s wealthy Single Market. In this context, one of the most peculiar steps in the European Union’s strategy was the creation of an independent High-Level Expert Group on AI (AI HLEG), accompanied by the launch of an AI Alliance, which quickly attracted several hundred participants. The AI HLEG, a multistakeholder group including fifty-two experts, was tasked with the definition of Ethics Guidelines as well as with the formulation of “Policy and Investment Recommendations.” With the advice of the AI HLEG, the European Commission put forward ethical guidelines for Trustworthy AI—which are now paving the way for a comprehensive, risk-based policy framework.

A framework for a data interest analysis of artificial intelligence

First Monday ◽  
2021 ◽  
Author(s):  
Gry Hasselbalch
Keyword(s):  
Artificial Intelligence ◽  
Applied Ethics ◽  
Expert Group ◽  
Governance Framework ◽  
Ethics Guidelines ◽  
High Level ◽  
Ethical Governance ◽  
The Eu

This article makes a case for a data interest analysis of artificial intelligence (AI) that explores how different interests in data are empowered or disempowered by design. The article uses the EU High-Level Expert Group on AI’s Ethics Guidelines for Trustworthy AI as an applied ethics approach to data interests with a human-centric ethical governance framework and accordingly suggests ethical questions that will help resolve conflicts between data interests in AI design

scholarly journals On the Governance of Artificial Intelligence through Ethics Guidelines

2020 ◽  
pp. 1-15
Author(s):  
Stefan LARSSON
Keyword(s):  
Artificial Intelligence ◽  
White Paper ◽  
Expert Group ◽  
Policy Area ◽  
Legal Perspective ◽  
China And Japan ◽  
Ethics Guidelines ◽  
High Level ◽  
Eu Commission ◽  
The Eu

Abstract This article uses a socio-legal perspective to analyze the use of ethics guidelines as a governance tool in the development and use of artificial intelligence (AI). This has become a central policy area in several large jurisdictions, including China and Japan, as well as the EU, focused on here. Particular emphasis in this article is placed on the Ethics Guidelines for Trustworthy AI published by the EU Commission’s High-Level Expert Group on Artificial Intelligence in April 2019, as well as the White Paper on AI, published by the EU Commission in February 2020. The guidelines are reflected against partially overlapping and already-existing legislation as well as the ephemeral concept construct surrounding AI as such. The article concludes by pointing to (1) the challenges of a temporal discrepancy between technological and legal change, (2) the need for moving from principle to process in the governance of AI, and (3) the multidisciplinary needs in the study of contemporary applications of data-dependent AI.

scholarly journals Gender and feminist considerations in Artificial Intelligence from a developing-world perspective, with India as a case study

2021 ◽  
Author(s):  
SANGHAMITRA CHOUDHURY ◽  
Shailendra Kumar
Keyword(s):  
Artificial Intelligence ◽  
Developing Countries ◽  
Developing World ◽  
Developed Countries ◽  
High Level ◽  
The Impact ◽  
The Relationship ◽  
Precarious Situation ◽  
And Robotics

<p>The relationship between women, technology manifestation, and likely prospects in the developing world is discussed in this manuscript. Using India as a case study, the paper goes on to discuss how ontology and epistemology views utilised in AI (Artificial Intelligence) and robotics will affect women's prospects in developing countries. Women in developing countries, notably in South Asia, are perceived as doing domestic work and are underrepresented in high-level professions. They are disproportionately underemployed and face prejudice in the workplace. The purpose of this study is to determine if the introduction of AI would exacerbate the already precarious situation of women in the developing world or if it would serve as a liberating force. While studies on the impact of AI on women have been undertaken in developed countries, there has been less research in developing countries. This manuscript attempts to fill that need.</p>

scholarly journals Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

2020 ◽  
Vol 12 (6) ◽  
pp. 2427 ◽  
Author(s):  
Behrouz Pirouz ◽  
Sina Shaffiee Haghshenas ◽  
Sami Shaffiee Haghshenas ◽  
Patrizia Piro
Keyword(s):  
Artificial Intelligence ◽  
Sustainable Development ◽  
Relative Humidity ◽  
Development Process ◽  
Binary Classification ◽  
Daily Temperature ◽  
Maximum Daily Temperature ◽  
The Sustainable Development ◽  
Epidemic Diseases

Nowadays, sustainable development is considered a key concept and solution in creating a promising and prosperous future for human societies. Nevertheless, there are some predicted and unpredicted problems that epidemic diseases are real and complex problems. Hence, in this research work, a serious challenge in the sustainable development process was investigated using the classification of confirmed cases of COVID-19 (new version of Coronavirus) as one of the epidemic diseases. Hence, binary classification modeling was used by the group method of data handling (GMDH) type of neural network as one of the artificial intelligence methods. For this purpose, the Hubei province in China was selected as a case study to construct the proposed model, and some important factors, namely maximum, minimum, and average daily temperature, the density of a city, relative humidity, and wind speed, were considered as the input dataset, and the number of confirmed cases was selected as the output dataset for 30 days. The proposed binary classification model provides higher performance capacity in predicting the confirmed cases. In addition, regression analysis has been done and the trend of confirmed cases compared with the fluctuations of daily weather parameters (wind, humidity, and average temperature). The results demonstrated that the relative humidity and maximum daily temperature had the highest impact on the confirmed cases. The relative humidity in the main case study, with an average of 77.9%, affected positively, and maximum daily temperature, with an average of 15.4 °C, affected negatively, the confirmed cases.

Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence

First Monday ◽  
2019 ◽  
Author(s):  
Niel Chah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Historical Context ◽  
Historical Background ◽  
Deep Roots ◽  
Technical Guide ◽  
Learning Machine ◽  
High Level

Interest in deep learning, machine learning, and artificial intelligence from industry and the general public has reached a fever pitch recently. However, these terms are frequently misused, confused, and conflated. This paper serves as a non-technical guide for those interested in a high-level understanding of these increasingly influential notions by exploring briefly the historical context of deep learning, its public presence, and growing concerns over the limitations of these techniques. As a first step, artificial intelligence and machine learning are defined. Next, an overview of the historical background of deep learning reveals its wide scope and deep roots. A case study of a major deep learning implementation is presented in order to analyze public perceptions shaped by companies focused on technology. Finally, a review of deep learning limitations illustrates systemic vulnerabilities and a growing sense of concern over these systems.

Augmenting Healthcare with Human-Centered Technologies

2020 ◽  
Vol 25 (4) ◽  
Author(s):  
Christopher Moehle ◽  
Jessica Gibson
Keyword(s):  
Artificial Intelligence ◽  
Science Fiction ◽  
Precision Medicine ◽  
Economic Factors ◽  
Central Theme ◽  
Business Cases ◽  
Self Driving Cars ◽  
High Level ◽  
Near Future

“Robotics”, “Artificial Intelligence”, and “Machine Learning” have become an almost impossibly broad amalgam of terminologies that span across industries to include everything from the cotton gin to self-driving cars, and touch a broad range of biotechnology and med tech applications.  We address the spread of these transformative technologies across every interpretation of the analogy, including the spectrum ranging from practical, highly economic products to inventive science fiction with speculative business cases.  In this two-part article, we first briefly overview the high-level commonalities between historically successful products and the economic factors driving adoption of these intelligent technologies in our current economy.  In doing so, we focus heavily on “Augmentation” as a central theme of the best products historically, now, and in the near future.  In the second part of the article, we further illustrate how “Augmented Intelligence” can be applied to biotech. This is done through a mini-case study, or a detailed practicum, on Ariel Precision Medicine, to illustrate how “Augmented Intelligence” can be applied to precision medicine currently.

SELF-IMPOSED ETHICAL GUIDELINES FOR AI IN EDUCATION

2020 ◽  
Vol 1 ◽  
Author(s):  
Anri Leimanis
Keyword(s):  
Artificial Intelligence ◽  
The Self ◽  
Ethical Principles ◽  
Educational Institutions ◽  
Public Agencies ◽  
Ethical Guidelines ◽  
Ai Ethics ◽  
Global Discourse ◽  
The Common ◽  
Ethics Guidelines

Advances in Artificial Intelligence (AI) applications to education have encouraged an extensive global discourse on the underlying ethical principles and values. In a response numerous research institutions, companies, public agencies and non-governmental entities around the globe have published their own guidelines and / or policies for ethical AI. Even though the aim for most of the guidelines is to maximize the benefits that AI delivers to education, the policies differ significantly in content as well as application. In order to facilitate further discussion about the ethical principles, responsibilities of educational institutions using AI and to potentially arrive at a consensus concerning safe and desirable uses of AI in education, this paper performs an evaluation of the self-imposed AI ethics guidelines identifying the common principles and approaches as well as drawbacks limiting the practical and legal application of the policies.

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