Ethical machines: The human-centric use of artificial intelligence

Summary: Today's increased availability of large amounts of human behavioral data and advances in artificial intelligence (AI) are contributing to a growing reliance on algorithms to make consequential decisions for humans, including those related to access to credit or medical treatments, hiri...

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Main Authors: Bruno Lepri, Nuria Oliver, Alex Pentland
Format: Article
Language:English
Published: Elsevier 2021-03-01
Series:iScience
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2589004221002170
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spelling doaj-029fb8ece4fb40f09ae696c7999064ae2021-03-22T12:52:18ZengElsevieriScience2589-00422021-03-01243102249Ethical machines: The human-centric use of artificial intelligenceBruno Lepri0Nuria Oliver1Alex Pentland2Digital Society Center, Fondazione Bruno Kessler, Trento 38123, Italy; Data-Pop Alliance, New York, NY, USA; Corresponding authorELLIS (the European Laboratory for Learning and Intelligent Systems) Unit Alicante, Alicante 03690, Spain; Data-Pop Alliance, New York, NY, USAData-Pop Alliance, New York, NY, USA; MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USASummary: Today's increased availability of large amounts of human behavioral data and advances in artificial intelligence (AI) are contributing to a growing reliance on algorithms to make consequential decisions for humans, including those related to access to credit or medical treatments, hiring, etc. Algorithmic decision-making processes might lead to more objective decisions than those made by humans who may be influenced by prejudice, conflicts of interest, or fatigue. However, algorithmic decision-making has been criticized for its potential to lead to privacy invasion, information asymmetry, opacity, and discrimination. In this paper, we describe available technical solutions in three large areas that we consider to be of critical importance to achieve a human-centric AI: (1) privacy and data ownership; (2) accountability and transparency; and (3) fairness. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy makers, and citizens to co-develop and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness, accountability, and transparency while respecting privacy.http://www.sciencedirect.com/science/article/pii/S2589004221002170AlgorithmsArtificial IntelligenceComputer Privacy
collection DOAJ
language English
format Article
sources DOAJ
author Bruno Lepri
Nuria Oliver
Alex Pentland
spellingShingle Bruno Lepri
Nuria Oliver
Alex Pentland
Ethical machines: The human-centric use of artificial intelligence
iScience
Algorithms
Artificial Intelligence
Computer Privacy
author_facet Bruno Lepri
Nuria Oliver
Alex Pentland
author_sort Bruno Lepri
title Ethical machines: The human-centric use of artificial intelligence
title_short Ethical machines: The human-centric use of artificial intelligence
title_full Ethical machines: The human-centric use of artificial intelligence
title_fullStr Ethical machines: The human-centric use of artificial intelligence
title_full_unstemmed Ethical machines: The human-centric use of artificial intelligence
title_sort ethical machines: the human-centric use of artificial intelligence
publisher Elsevier
series iScience
issn 2589-0042
publishDate 2021-03-01
description Summary: Today's increased availability of large amounts of human behavioral data and advances in artificial intelligence (AI) are contributing to a growing reliance on algorithms to make consequential decisions for humans, including those related to access to credit or medical treatments, hiring, etc. Algorithmic decision-making processes might lead to more objective decisions than those made by humans who may be influenced by prejudice, conflicts of interest, or fatigue. However, algorithmic decision-making has been criticized for its potential to lead to privacy invasion, information asymmetry, opacity, and discrimination. In this paper, we describe available technical solutions in three large areas that we consider to be of critical importance to achieve a human-centric AI: (1) privacy and data ownership; (2) accountability and transparency; and (3) fairness. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy makers, and citizens to co-develop and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness, accountability, and transparency while respecting privacy.
topic Algorithms
Artificial Intelligence
Computer Privacy
url http://www.sciencedirect.com/science/article/pii/S2589004221002170
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