AI Maintenance: A Robustness Perspective

In this article, we carve out artificial intelligence (AI) maintenance from the robustness perspective. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle, which is an essential milestone towa...

Full description

Saved in:
Bibliographic Details
Published in:Computer (Long Beach, Calif.) Calif.), 2023-02, Vol.56 (2), p.48-56
Main Authors: Chen, Pin-Yu, Das, Payel
Format: Article
Language:English
Subjects:
Artificial intelligence
Computational modeling
Ecosystems
Maintenance
Proposals
Risk management
Robustness
Sustainable development
Tracking
Trust computing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c331t-e9a90cecc087909a8b4762477a2260038ca762b2f101c65594057e85ca68b6883
cites cdi_FETCH-LOGICAL-c331t-e9a90cecc087909a8b4762477a2260038ca762b2f101c65594057e85ca68b6883
container_end_page 56
container_issue 2
container_start_page 48
container_title Computer (Long Beach, Calif.)
container_volume 56
creator Chen, Pin-Yu
Das, Payel
description In this article, we carve out artificial intelligence (AI) maintenance from the robustness perspective. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle, which is an essential milestone toward building sustainable and trustworthy AI ecosystems.
doi_str_mv 10.1109/MC.2022.3218005
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2775103760</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10042081</ieee_id><sourcerecordid>2775103760</sourcerecordid><originalsourceid>FETCH-LOGICAL-c331t-e9a90cecc087909a8b4762477a2260038ca762b2f101c65594057e85ca68b6883</originalsourceid><addsrcrecordid>eNpNkEtLw0AUhQdRsFbXblwE3LhJeu9M5uWuBB-FFkV0PUyGG0jRpM4kgv_elHbh6nLgO-fCx9g1QoEIdrGpCg6cF4KjAZAnbIZSmhwMlqdsBoAmt6j4ObtIaTvF0kgxY3fLVbbxbTdQ57tA99kye-vrMQ0dpZS9Ukw7CkP7Q5fsrPGfia6Od84-Hh_eq-d8_fK0qpbrPAiBQ07WWwgUAhhtwXpTl1rxUmvPuQIQJvgp17xBwKCktCVITUYGr0ytjBFzdnvY3cX-e6Q0uG0_xm566bjWEkFoBRO1OFAh9ilFatwutl8-_joEt9fhNpXb63BHHVPj5tBoiegfDSWfFIk_SlxXtQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2775103760</pqid></control><display><type>article</type><title>AI Maintenance: A Robustness Perspective</title><source>IEEE Electronic Library (IEL) Journals</source><creator>Chen, Pin-Yu ; Das, Payel</creator><creatorcontrib>Chen, Pin-Yu ; Das, Payel</creatorcontrib><description>In this article, we carve out artificial intelligence (AI) maintenance from the robustness perspective. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle, which is an essential milestone toward building sustainable and trustworthy AI ecosystems.</description><identifier>ISSN: 0018-9162</identifier><identifier>EISSN: 1558-0814</identifier><identifier>DOI: 10.1109/MC.2022.3218005</identifier><identifier>CODEN: CPTRB4</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Artificial intelligence ; Computational modeling ; Ecosystems ; Maintenance ; Proposals ; Risk management ; Robustness ; Sustainable development ; Tracking ; Trust computing</subject><ispartof>Computer (Long Beach, Calif.), 2023-02, Vol.56 (2), p.48-56</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c331t-e9a90cecc087909a8b4762477a2260038ca762b2f101c65594057e85ca68b6883</citedby><cites>FETCH-LOGICAL-c331t-e9a90cecc087909a8b4762477a2260038ca762b2f101c65594057e85ca68b6883</cites><orcidid>0000-0003-1039-8369</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10042081$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,27922,27923,54794</link.rule.ids></links><search><creatorcontrib>Chen, Pin-Yu</creatorcontrib><creatorcontrib>Das, Payel</creatorcontrib><title>AI Maintenance: A Robustness Perspective</title><title>Computer (Long Beach, Calif.)</title><addtitle>MC</addtitle><description>In this article, we carve out artificial intelligence (AI) maintenance from the robustness perspective. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle, which is an essential milestone toward building sustainable and trustworthy AI ecosystems.</description><subject>Artificial intelligence</subject><subject>Computational modeling</subject><subject>Ecosystems</subject><subject>Maintenance</subject><subject>Proposals</subject><subject>Risk management</subject><subject>Robustness</subject><subject>Sustainable development</subject><subject>Tracking</subject><subject>Trust computing</subject><issn>0018-9162</issn><issn>1558-0814</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpNkEtLw0AUhQdRsFbXblwE3LhJeu9M5uWuBB-FFkV0PUyGG0jRpM4kgv_elHbh6nLgO-fCx9g1QoEIdrGpCg6cF4KjAZAnbIZSmhwMlqdsBoAmt6j4ObtIaTvF0kgxY3fLVbbxbTdQ57tA99kye-vrMQ0dpZS9Ukw7CkP7Q5fsrPGfia6Od84-Hh_eq-d8_fK0qpbrPAiBQ07WWwgUAhhtwXpTl1rxUmvPuQIQJvgp17xBwKCktCVITUYGr0ytjBFzdnvY3cX-e6Q0uG0_xm566bjWEkFoBRO1OFAh9ilFatwutl8-_joEt9fhNpXb63BHHVPj5tBoiegfDSWfFIk_SlxXtQ</recordid><startdate>20230201</startdate><enddate>20230201</enddate><creator>Chen, Pin-Yu</creator><creator>Das, Payel</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0003-1039-8369</orcidid></search><sort><creationdate>20230201</creationdate><title>AI Maintenance: A Robustness Perspective</title><author>Chen, Pin-Yu ; Das, Payel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c331t-e9a90cecc087909a8b4762477a2260038ca762b2f101c65594057e85ca68b6883</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial intelligence</topic><topic>Computational modeling</topic><topic>Ecosystems</topic><topic>Maintenance</topic><topic>Proposals</topic><topic>Risk management</topic><topic>Robustness</topic><topic>Sustainable development</topic><topic>Tracking</topic><topic>Trust computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Pin-Yu</creatorcontrib><creatorcontrib>Das, Payel</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computer (Long Beach, Calif.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Pin-Yu</au><au>Das, Payel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>AI Maintenance: A Robustness Perspective</atitle><jtitle>Computer (Long Beach, Calif.)</jtitle><stitle>MC</stitle><date>2023-02-01</date><risdate>2023</risdate><volume>56</volume><issue>2</issue><spage>48</spage><epage>56</epage><pages>48-56</pages><issn>0018-9162</issn><eissn>1558-0814</eissn><coden>CPTRB4</coden><abstract>In this article, we carve out artificial intelligence (AI) maintenance from the robustness perspective. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle, which is an essential milestone toward building sustainable and trustworthy AI ecosystems.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/MC.2022.3218005</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-1039-8369</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0018-9162
ispartof Computer (Long Beach, Calif.), 2023-02, Vol.56 (2), p.48-56
issn 0018-9162
1558-0814
language eng
recordid cdi_proquest_journals_2775103760
source IEEE Electronic Library (IEL) Journals
subjects Artificial intelligence
Computational modeling
Ecosystems
Maintenance
Proposals
Risk management
Robustness
Sustainable development
Tracking
Trust computing
title AI Maintenance: A Robustness Perspective
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T19%3A16%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=AI%20Maintenance:%20A%20Robustness%20Perspective&rft.jtitle=Computer%20(Long%20Beach,%20Calif.)&rft.au=Chen,%20Pin-Yu&rft.date=2023-02-01&rft.volume=56&rft.issue=2&rft.spage=48&rft.epage=56&rft.pages=48-56&rft.issn=0018-9162&rft.eissn=1558-0814&rft.coden=CPTRB4&rft_id=info:doi/10.1109/MC.2022.3218005&rft_dat=%3Cproquest_cross%3E2775103760%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c331t-e9a90cecc087909a8b4762477a2260038ca762b2f101c65594057e85ca68b6883%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2775103760&rft_id=info:pmid/&rft_ieee_id=10042081&rfr_iscdi=true