Exploring the role of AI algorithmic agents: The impact of algorithmic decision autonomy on consumer purchase decisions

Yue-jiao, Fan; Liu, Xianggang

doi:10.3389/fpsyg.2022.1009173

Cited by 5 publications

(2 citation statements)

References 96 publications

(123 reference statements)

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“…This includes examining the output of algorithms, such as loan approvals or career opportunity allocations, to see if there are unfair or unequal outcomes. If it is found that certain groups are being treated unfavourably, then this may be a sign of unintentional discrimination ( Fan and Liu, 2022 ).…”

Section: Judicial Review Of Algorithmic Discriminationmentioning

confidence: 99%

Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices

Wang,

Wu,

et al. 2024

Front. Artif. Intell.

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IntroductionAlgorithmic decision-making systems are widely used in various sectors, including criminal justice, employment, and education. While these systems are celebrated for their potential to enhance efficiency and objectivity, they also pose risks of perpetuating and amplifying societal biases and discrimination. This paper aims to provide an indepth analysis of the types of algorithmic discrimination, exploring both the challenges and potential solutions.MethodsThe methodology includes a systematic literature review, analysis of legal documents, and comparative case studies across different geographic regions and sectors. This multifaceted approach allows for a thorough exploration of the complexity of algorithmic bias and its regulation.ResultsWe identify five primary types of algorithmic bias: bias by algorithmic agents, discrimination based on feature selection, proxy discrimination, disparate impact, and targeted advertising. The analysis of the U.S. legal and regulatory framework reveals a landscape of principled regulations, preventive controls, consequential liability, self-regulation, and heteronomy regulation. A comparative perspective is also provided by examining the status of algorithmic fairness in the EU, Canada, Australia, and Asia.ConclusionReal-world impacts are demonstrated through case studies focusing on criminal risk assessments and hiring algorithms, illustrating the tangible effects of algorithmic discrimination. The paper concludes with recommendations for interdisciplinary research, proactive policy development, public awareness, and ongoing monitoring to promote fairness and accountability in algorithmic decision-making. As the use of AI and automated systems expands globally, this work highlights the importance of developing comprehensive, adaptive approaches to combat algorithmic discrimination and ensure the socially responsible deployment of these powerful technologies.

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Section: Judicial Review Of Algorithmic Discriminationmentioning

confidence: 99%

Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices

Wang,

Wu,

et al. 2024

Front. Artif. Intell.

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“…Building on communication theory, Osburg et al (2019) developed and tested a framework illustrating the relationship between morality and purchase decisions. Based on self-determination theory, Fan and Liu (2022) explored the impact of the decisive role on consumer purchase decisions for different AI algorithmic decision autonomy induced. In recent years, some studies have shown that consumer satisfaction has a certain impact on customers' purchase decisions (Hult et al, 2019;Guo et al, 2017b).…”

Section: Purchase Decision Theorymentioning

confidence: 99%

Artificial intelligent housekeeper based on consumer purchase decision: a case study of online E-commerce

Guo,

Tang,

Wang

et al. 2024

IMDS

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PurposeThe rise of artificial intelligence (AI) and machine learning has largely promoted the emergence of “autonomous decision-making” (ADM). This paper aims to establish a personalized artificial intelligent housekeeper (AIH) that knows more about our hobbies, habits, personality traits, and shopping needs than ourselves and can replace us to do some habitual purchasing behavior.Design/methodology/approachWe propose an AI decision-making method based on machine learning algorithm, a novel framework for personalized customer preference and purchase. First, the method uses interactive big data to predict a potential consumer’s decision possibility. Then, the method mines the correlation between consumer decision possibility and various factors affecting consumer behavior. Finally, the machine learning algorithm is used to estimate the consumer’s purchase decision according to the comprehensive influencing factors data of the target consumer.FindingsThe experimental results show that the method can predict the regular consumption behavior of consumers in advance and make accurate decision-making behavior. It can find correlations from a large amount of data to help predict many simple purchase decisions in our life, and become our AIH.Originality/valueThis study introduces a new approach that not only has the auxiliary decision-making function but also has the decision-making function. These findings contribute to the research on automated decision-making process of AI and on human–technology interaction by investigating how data attributes consumer purchase decision to AI.

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Study on the Impact of Service Robot Autonomy on Customer Satisfaction

2023

Lecture Notes in Computer Science

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Exploring the role of AI algorithmic agents: The impact of algorithmic decision autonomy on consumer purchase decisions

Cited by 5 publications

References 96 publications

Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices

Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices

Artificial intelligent housekeeper based on consumer purchase decision: a case study of online E-commerce

Study on the Impact of Service Robot Autonomy on Customer Satisfaction

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