scholarly journals ASSESSING ETHICAL AI-BASED DECISION-MAKING: TOWARDS AN APPLIED ANALYTICAL FRAMEWORK

2019 ◽  
Vol 2019 ◽  
Author(s):  
Paul Henman
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Analytical Framework ◽  
Government Accountability ◽  
Ethical Challenges ◽  
Government Decision ◽  
Policy Work ◽  
Key Questions

Globally there is strong enthusiasm for using Artificial Intelligence (AI) in government decision making, yet this technocratic approach is not without significant downsides including bias, exacerbating discrimination and inequalities, and reducing government accountability and transparency. A flurry of analytical and policy work has recently sought to identify principles, policies, regulations and institutions for enacting ethical AI. Yet, what is lacking is a practical framework and means by which AI can be assessed as un/ethical. This paper provides an overview of an applied analytical framework for assessing the ethics of AI. It notes that AI (or algorithmic) decision-making is an outcome of data, code, context and use. Using these four categories, the paper articulates key questions necessary to determine the potential ethical challenges of using an AI/algorithm in decision making, and provides the basis for their articulation within a practical toolkit that can be demonstrated against known AI decision-making tools.

Cultivating Trustworthy Artificial Intelligence in Digital Government

2020 ◽  
pp. 089443932098012
Author(s):  
Teresa M. Harrison ◽  
Luis Felipe Luna-Reyes
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Policy Analysis ◽  
Public Trust ◽  
Governance Structures ◽  
Cognitive Tools ◽  
Lens Model ◽  
Digital Government ◽  
Government Decision ◽  
Decision Making Processes

While there is growing consensus that the analytical and cognitive tools of artificial intelligence (AI) have the potential to transform government in positive ways, it is also clear that AI challenges traditional government decision-making processes and threatens the democratic values within which they are framed. These conditions argue for conservative approaches to AI that focus on cultivating and sustaining public trust. We use the extended Brunswik lens model as a framework to illustrate the distinctions between policy analysis and decision making as we have traditionally understood and practiced them and how they are evolving in the current AI context along with the challenges this poses for the use of trustworthy AI. We offer a set of recommendations for practices, processes, and governance structures in government to provide for trust in AI and suggest lines of research that support them.

scholarly journals Ethical considerations about artificial intelligence for prognostication in intensive care

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Michael Beil ◽  
Ingo Proft ◽  
Daniel van Heerden ◽  
Sigal Sviri ◽  
Peter Vernon van Heerden
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Intensive Care ◽  
Prediction Models ◽  
Ethical Challenges ◽  
Ethical Considerations ◽  
Or Groups ◽  
Data Stewardship ◽  
Technical Refinement ◽  
Selection Of

Abstract Background Prognosticating the course of diseases to inform decision-making is a key component of intensive care medicine. For several applications in medicine, new methods from the field of artificial intelligence (AI) and machine learning have already outperformed conventional prediction models. Due to their technical characteristics, these methods will present new ethical challenges to the intensivist. Results In addition to the standards of data stewardship in medicine, the selection of datasets and algorithms to create AI prognostication models must involve extensive scrutiny to avoid biases and, consequently, injustice against individuals or groups of patients. Assessment of these models for compliance with the ethical principles of beneficence and non-maleficence should also include quantification of predictive uncertainty. Respect for patients’ autonomy during decision-making requires transparency of the data processing by AI models to explain the predictions derived from these models. Moreover, a system of continuous oversight can help to maintain public trust in this technology. Based on these considerations as well as recent guidelines, we propose a pathway to an ethical implementation of AI-based prognostication. It includes a checklist for new AI models that deals with medical and technical topics as well as patient- and system-centered issues. Conclusion AI models for prognostication will become valuable tools in intensive care. However, they require technical refinement and a careful implementation according to the standards of medical ethics.

scholarly journals Writing a Moral Code: Algorithms for Ethical Reasoning by Humans and Machines

Religions ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 240 ◽  
Author(s):  
James McGrath ◽  
Ankur Gupta
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Science Fiction ◽  
Ethical Reasoning ◽  
Moral Code ◽  
Human Beings ◽  
Ethical Challenges ◽  
Value System ◽  
Autonomous Machines

The moral and ethical challenges of living in community pertain not only to the intersection of human beings one with another, but also our interactions with our machine creations. This article explores the philosophical and theological framework for reasoning and decision-making through the lens of science fiction, religion, and artificial intelligence (both real and imagined). In comparing the programming of autonomous machines with human ethical deliberation, we discover that both depend on a concrete ordering of priorities derived from a clearly defined value system.

scholarly journals The digital transition of social security in Finland. Frontrunner experiencing headwinds?

2021 ◽  
Vol 150 (3) ◽  
Author(s):  
Niko Väänänen
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Social Security ◽  
State Of The Art ◽  
Ethical Challenges ◽  
Organizational Challenges ◽  
Current State ◽  
Digital Transition ◽  
Digital Processes

Digitalization transforms our societies in a profound way. Public administrations and social security institutions are at different stages in this process. Digitalization poses technological, legal, and organizational challenges. Finland has typically been a frontrunner in the adaptation of ICT technology. This case study critically assesses the current state-of-the-art in the field of digitalization in Finnish social security. The text singles out the projects that are on-going and those that are planned for the immediate future. The article shows that Finnish social security institutions have integrated digital processes into their operations, but legal and ethical challenges exist, especially in the use of artificial intelligence and automatic decision-making in social security.

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

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