Biased artificial intelligence - algorithmic fairness and human perception on biased AI

Bibliographic Details
Main Author: Machill, Sidney Anna
Publication Date: 2020
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/109738
Summary: Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management
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spelling Biased artificial intelligence - algorithmic fairness and human perception on biased AIArtificial intelligenceBiased artificial intelligenceMachine learningAlgorithmic fairnessAlgorithmic perceptionDiscriminationDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementAs artificial intelligence (AI) is given more power in many decisions, potential resulting biases in respect to gender, race, and other minorities have to be analyzed and reduced to a minimum. Machine learning (ML) models are implemented in various areas and can decide who gets invited to an interview, granted a loan, gets the right cancer treatment, or goes to prison. Consequently, biases can have a crucial negative impact on people’s life. This thesis highlights previous research in this field, shows its limitations and breaks down the content into its core components in s systematic manner. Therefore, types of existing biases, and areas where AI bias is most components in a systematic manner. Therefore, types of existing biases, and areas where AI is most prevalent are defined. Further, root causes for discriminating algorithms are analyzed according to the AI model creation chain: data, coder, model, usage. An abundance of fairness measurements is classified and elaborated in a tabular format. Thereafter, bias mitigation techniques naming pre-processing, in-processing, and post-processing for ML algorithms are summarized, critically analyzed and limitations of research for unsupervised learning fairness measures are indicated.Pinheiro, Flávio Luís PortasOrghian, DianaRUNMachill, Sidney Anna2021-01-05T14:32:37Z2020-11-062020-11-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/109738TID:202572374enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:49:39Zoai:run.unl.pt:10362/109738Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:20:58.918070Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Biased artificial intelligence - algorithmic fairness and human perception on biased AI
title Biased artificial intelligence - algorithmic fairness and human perception on biased AI
spellingShingle Biased artificial intelligence - algorithmic fairness and human perception on biased AI
Machill, Sidney Anna
Artificial intelligence
Biased artificial intelligence
Machine learning
Algorithmic fairness
Algorithmic perception
Discrimination
title_short Biased artificial intelligence - algorithmic fairness and human perception on biased AI
title_full Biased artificial intelligence - algorithmic fairness and human perception on biased AI
title_fullStr Biased artificial intelligence - algorithmic fairness and human perception on biased AI
title_full_unstemmed Biased artificial intelligence - algorithmic fairness and human perception on biased AI
title_sort Biased artificial intelligence - algorithmic fairness and human perception on biased AI
author Machill, Sidney Anna
author_facet Machill, Sidney Anna
author_role author
dc.contributor.none.fl_str_mv Pinheiro, Flávio Luís Portas
Orghian, Diana
RUN
dc.contributor.author.fl_str_mv Machill, Sidney Anna
dc.subject.por.fl_str_mv Artificial intelligence
Biased artificial intelligence
Machine learning
Algorithmic fairness
Algorithmic perception
Discrimination
topic Artificial intelligence
Biased artificial intelligence
Machine learning
Algorithmic fairness
Algorithmic perception
Discrimination
description Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management
publishDate 2020
dc.date.none.fl_str_mv 2020-11-06
2020-11-06T00:00:00Z
2021-01-05T14:32:37Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/109738
TID:202572374
url http://hdl.handle.net/10362/109738
identifier_str_mv TID:202572374
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833596629539618816

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