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The Blame Game: Attribution of Respo...

Schneider, Annabel.

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The Blame Game: Attribution of Responsibility In Human, Black Box and Explainable Ai in the Context of Successful and Unsuccessful Managerial Decision-Making = = O Jogo da Culpa: Atribuicao de Responsabilidade em Ia Humana, Caixa Negra e Explicavel no Contexto de Decisoes de Gestao Bem e Mal Sucedidas.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Blame Game: Attribution of Responsibility In Human, Black Box and Explainable Ai in the Context of Successful and Unsuccessful Managerial Decision-Making =/
Reminder of title:	O Jogo da Culpa: Atribuicao de Responsabilidade em Ia Humana, Caixa Negra e Explicavel no Contexto de Decisoes de Gestao Bem e Mal Sucedidas.
Author:	Schneider, Annabel.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	61 p.
Notes:	Source: Masters Abstracts International, Volume: 86-01.
Contained By:	Masters Abstracts International86-01.
Subject:	Innovations. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31054781
ISBN:	9798383387689

The Blame Game: Attribution of Responsibility In Human, Black Box and Explainable Ai in the Context of Successful and Unsuccessful Managerial Decision-Making = = O Jogo da Culpa: Atribuicao de Responsabilidade em Ia Humana, Caixa Negra e Explicavel no Contexto de Decisoes de Gestao Bem e Mal Sucedidas.
Schneider, Annabel.

The Blame Game: Attribution of Responsibility In Human, Black Box and Explainable Ai in the Context of Successful and Unsuccessful Managerial Decision-Making =O Jogo da Culpa: Atribuicao de Responsabilidade em Ia Humana, Caixa Negra e Explicavel no Contexto de Decisoes de Gestao Bem e Mal Sucedidas. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 61 p.

Source: Masters Abstracts International, Volume: 86-01.

Thesis (M.H.R.M.)--Universidade Catolica Portuguesa (Portugal), 2023.

The rise of ChatGPT, deep fake artificial images, and automated machine learning techniques are proof of the growing demand for usable AI methods. The more sophisticated those applications become, the harder it is to create transparency along the responsibility chain. This master's thesis looks into the complex world of responsibility attribution in collaborative human-AI decision-making with an emphasis on different types of AI and different decision outcomes within managerial contexts.The findings support a general trend: compared to AI entities, people tend to place more blame on human decision-makers, which is consistent with the fundamental attribution error. Contrary to predictions, the research finds no significant difference in the allocation of blame between explainable AI and black box AI. This challenges the notion that attribution of responsibility is decreased by AI transparency and highlights the complex nature of this phenomenon. The study challenges common thinking by showing that the success of a decision outcome does not significantly impact responsibility attribution, inferring that accountability stays relatively constant in managerial decision-making regardless of the outcome.In conclusion, this thesis emphasizes the crucial role of human decision-makers in managerial settings and promotes continuous investment in human ethical decision-making training. These findings provide an important contribution to the discussion of AI ethics and responsibility in decision-making.

ISBN: 9798383387689Subjects--Topical Terms:

754112
Innovations.

The Blame Game: Attribution of Responsibility In Human, Black Box and Explainable Ai in the Context of Successful and Unsuccessful Managerial Decision-Making = = O Jogo da Culpa: Atribuicao de Responsabilidade em Ia Humana, Caixa Negra e Explicavel no Contexto de Decisoes de Gestao Bem e Mal Sucedidas.
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520 $a The rise of ChatGPT, deep fake artificial images, and automated machine learning techniques are proof of the growing demand for usable AI methods. The more sophisticated those applications become, the harder it is to create transparency along the responsibility chain. This master's thesis looks into the complex world of responsibility attribution in collaborative human-AI decision-making with an emphasis on different types of AI and different decision outcomes within managerial contexts.The findings support a general trend: compared to AI entities, people tend to place more blame on human decision-makers, which is consistent with the fundamental attribution error. Contrary to predictions, the research finds no significant difference in the allocation of blame between explainable AI and black box AI. This challenges the notion that attribution of responsibility is decreased by AI transparency and highlights the complex nature of this phenomenon. The study challenges common thinking by showing that the success of a decision outcome does not significantly impact responsibility attribution, inferring that accountability stays relatively constant in managerial decision-making regardless of the outcome.In conclusion, this thesis emphasizes the crucial role of human decision-makers in managerial settings and promotes continuous investment in human ethical decision-making training. These findings provide an important contribution to the discussion of AI ethics and responsibility in decision-making.
520 $a O surgimento do ChatGPT, dos deepfakes e de tecnicas de aprendizagem automatica sao a prova da procura crescente de metodos utilizaveis de IA. Quanto mais sofisticadas essas aplicacoes se tornam, mais dificil e criar transparencia ao longo da cadeia de responsabilidade. Esta tese de mestrado analisa o complexo mundo da atribuicao de responsabilidades na tomada de decisoes colaborativas entre humanos e IA, com enfase nos diferentes tipos de IA e nos diferentes resultados das decisoes em contextos de gestao.Os resultados apoiam uma tendencia geral: em comparacao com entidades de IA, as pessoas tendem a atribuir mais culpa aos decisores humanos, o que e consistente com o erro de atribuicao fundamental. Contrariamente as previsoes, a investigacao nao encontra diferencas significativas na atribuicao de culpa entre a IA explicavel e a IA de caixa negra. Isto desafia a nocao de que a atribuicao de responsabilidades e reduzida pela transparencia da IA e realca a natureza complexa deste fenomeno. O estudo desafia o pensamento comum ao mostrar que o sucesso de um resultado de decisao nao afecta significativamente a atribuicao de responsabilidades, inferindo que a responsabilidade se mantem relativamente constante na tomada de decisoes de gestao, independentemente do resultado.Em conclusao, esta tese enfatiza o papel crucial dos decisores humanos em contextos de gestao e promove o investimento continuo na formacao de decisores humanos eticos. Estas conclusoes constituem um contributo importante para o debate sobre a etica e a responsabilidade da IA na tomada de decisoes.
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