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...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2021-03-01
|
Series: | iScience |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004221002170 |
id |
doaj-029fb8ece4fb40f09ae696c7999064ae |
---|---|
record_format |
Article |
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 |
work_keys_str_mv |
AT brunolepri ethicalmachinesthehumancentricuseofartificialintelligence AT nuriaoliver ethicalmachinesthehumancentricuseofartificialintelligence AT alexpentland ethicalmachinesthehumancentricuseofartificialintelligence |
_version_ |
1724207514271088640 |