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Social Sciences
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Artificial Intelligence for Policy Analysis, Governance and Society (AI-PAGES)
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Artificial Intelligence for Policy Analysis, Governance and Society (AI-PAGES)

A special issue of Social Sciences (ISSN 2076-0760).

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 19976

Special Issue Editor

Dr. Feras A. Batarseh

E-Mail Website
Guest Editor
Virginia Polytechnic Institute and State University (Virginia Tech), Arlington, VA 22203, USA
Interests: artificial intelligence

Special Issue Information

AI-PAGES is an interdisciplinary Special Issue that is focused on utilizing, redefining, improving, and evaluating artificial intelligence (AI) paradigms for policy making, governance, and society.

AI paradigms include (but are not limited to): machine learning, data mining, deep learning, text mining, reinforcement learning, computer vision, causal learning, context, knowledge-based systems, robotics, and natural language processing.

We invite high-quality, original, scholarly research manuscripts that introduce AI-based methods in economics, trade, finance, law, technology, healthcare, agriculture, defense, education, and transportation. We also invite submissions that present novel data-driven social sciences’ methods, as well as submissions of use-cases and deployments from institutions such as the federal government, state governments, institutes of public education and public health, international organizations, and any other venue or agency of governance. Other accepted areas include data science, data democracy, open data, data privacy, and other data-related topics. This Special Issue will include submissions that are based on rigorous, high-quality quantitative, qualitative, or mixed research methods. The submitted work has to generate new insights into the field, and help policy and decision makers in creating and analyzing data-driven policies.

A statement on Open Access:

This Special Issue adopts open science practices. Accordingly, unless there are contractual obligations, the research building blocks of your work are expected to be submitted with the paper (in case it is considered for publishing); these items include the following:

  1. Raw and cleaned datasets used in the experiment
  2. Assumptions and pre-conditions of the experiment
  3. Code/scripts
  4. Software tools and packages utilized
  5. Other environmental factors that influence the outcomes

By providing unrestricted access to the latest research works and their parts, we can collectively accelerate AI’s scientific discovery, ensure transparency, provide means of reproducibility, and construct a fair system of knowledge that is open to all.

Dr. Feras A. Batarseh
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as case reports are invited.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except workshop papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Social Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial intelligence
  • AI applications
  • Policy making
  • AI for policy
  • Policy scenarios
  • AI for society
  • Data science
  • Governance
  • Policy analytics
  • Big data analytics

Published Papers (4 papers)

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Research

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15 pages, 715 KiB  
Article
Enabling Artificial Intelligence Adoption through Assurance
by Laura Freeman, Abdul Rahman and Feras A. Batarseh
Soc. Sci. 2021, 10(9), 322; https://doi.org/10.3390/socsci10090322 - 25 Aug 2021
Cited by 10 | Viewed by 4050
Abstract
The wide scale adoption of Artificial Intelligence (AI) will require that AI engineers and developers can provide assurances to the user base that an algorithm will perform as intended and without failure. Assurance is the safety valve for reliable, dependable, explainable, and fair [...] Read more.
The wide scale adoption of Artificial Intelligence (AI) will require that AI engineers and developers can provide assurances to the user base that an algorithm will perform as intended and without failure. Assurance is the safety valve for reliable, dependable, explainable, and fair intelligent systems. AI assurance provides the necessary tools to enable AI adoption into applications, software, hardware, and complex systems. AI assurance involves quantifying capabilities and associating risks across deployments including: data quality to include inherent biases, algorithm performance, statistical errors, and algorithm trustworthiness and security. Data, algorithmic, and context/domain-specific factors may change over time and impact the ability of AI systems in delivering accurate outcomes. In this paper, we discuss the importance and different angles of AI assurance, and present a general framework that addresses its challenges. Full article
(This article belongs to the Special Issue Artificial Intelligence for Policy Analysis, Governance and Society (AI-PAGES))
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20 pages, 8556 KiB  
Article
Applied Machine Learning in Social Sciences: Neural Networks and Crime Prediction
by Ricardo Francisco Reier Forradellas, Sergio Luis Náñez Alonso, Javier Jorge-Vazquez and Marcela Laura Rodriguez
Soc. Sci. 2021, 10(1), 4; https://doi.org/10.3390/socsci10010004 - 29 Dec 2020
Cited by 18 | Viewed by 5862
Abstract
This study proposes a crime prediction model according to communes (areas or districts in which the city of Buenos Aires is divided). For this, the Python programming language is used, due to its versatility and wide availability of libraries oriented to Machine Learning. [...] Read more.
This study proposes a crime prediction model according to communes (areas or districts in which the city of Buenos Aires is divided). For this, the Python programming language is used, due to its versatility and wide availability of libraries oriented to Machine Learning. The crimes reported (period 2016–2019) that occurred in the city of Buenos Aires selected to test the model are: homicides, theft, injuries, and robberies. With this, it is possible to generate a crime prediction model according to the city area based on the SEMMA (Sample, Explore, Modify, Model, and Assess) model and after data manipulation, standardization and cleaning; clustering is performed using K-means and subsequently the neural network is generated. For prediction, it is necessary to provide the model with the information corresponding to the predictive characteristics (predict); these characteristics being according to the developed neural network model: year, month, day, time zone, commune, and type of crime. Full article
(This article belongs to the Special Issue Artificial Intelligence for Policy Analysis, Governance and Society (AI-PAGES))
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17 pages, 1587 KiB  
Article
Artificial Intelligence Research and Its Contributions to the European Union’s Political Governance: Comparative Study between Member States
by João Reis, Paula Santo and Nuno Melão
Soc. Sci. 2020, 9(11), 207; https://doi.org/10.3390/socsci9110207 - 16 Nov 2020
Cited by 4 | Viewed by 3871
Abstract
In the last six decades, many advances have been made in the field of artificial intelligence (AI). Bearing in mind that AI technologies are influencing societies and political systems differently, it can be useful to understand what are the common issues between similar [...] Read more.
In the last six decades, many advances have been made in the field of artificial intelligence (AI). Bearing in mind that AI technologies are influencing societies and political systems differently, it can be useful to understand what are the common issues between similar states in the European Union and how these political systems can collaborate with each other, seeking synergies, finding opportunities and saving costs. Therefore, we carried out an exploratory research among similar states of the European Union, in terms of scientific research in areas of AI technologies, namely: Portugal, Greece, Austria, Belgium and Sweden. A key finding of this research is that intelligent decision support systems (IDSS) are essential for the political decision-making process, since politics normally deals with complex and multifaceted decisions, which involve trade-offs between different stakeholders. As public health is becoming increasingly relevant in the field of the European Union, the IDSSs can provide relevant contributions, as it may allow sharing critical information and assist in the political decision-making process, especially in response to crisis situations. Full article
(This article belongs to the Special Issue Artificial Intelligence for Policy Analysis, Governance and Society (AI-PAGES))
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Review

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14 pages, 869 KiB  
Review
An Exploration of Ethical Decision Making with Intelligence Augmentation
by Niyi Ogunbiyi, Artie Basukoski and Thierry Chaussalet
Soc. Sci. 2021, 10(2), 57; https://doi.org/10.3390/socsci10020057 - 08 Feb 2021
Cited by 1 | Viewed by 4015
Abstract
In recent years, the use of Artificial Intelligence agents to augment and enhance the operational decision making of human agents has increased. This has delivered real benefits in terms of improved service quality, delivery of more personalised services, reduction in processing time, and [...] Read more.
In recent years, the use of Artificial Intelligence agents to augment and enhance the operational decision making of human agents has increased. This has delivered real benefits in terms of improved service quality, delivery of more personalised services, reduction in processing time, and more efficient allocation of resources, amongst others. However, it has also raised issues which have real-world ethical implications such as recommending different credit outcomes for individuals who have an identical financial profile but different characteristics (e.g., gender, race). The popular press has highlighted several high-profile cases of algorithmic discrimination and the issue has gained traction. While both the fields of ethical decision making and Explainable AI (XAI) have been extensively researched, as yet we are not aware of any studies which have examined the process of ethical decision making with Intelligence augmentation (IA). We aim to address that gap with this study. We amalgamate the literature in both fields of research and propose, but not attempt to validate empirically, propositions and belief statements based on the synthesis of the existing literature, observation, logic, and empirical analogy. We aim to test these propositions in future studies. Full article
(This article belongs to the Special Issue Artificial Intelligence for Policy Analysis, Governance and Society (AI-PAGES))
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