This paper delves into the hidden impact of algorithmic bias on the allocation of online education resources.With the rapid development of online education,algorithms play a crucial role in resource allocation,but alg...This paper delves into the hidden impact of algorithmic bias on the allocation of online education resources.With the rapid development of online education,algorithms play a crucial role in resource allocation,but algorithmic bias has emerged as a significant issue.The study analyzes the impact of bias at three levels:data level,where data collection and annotation biases lead to uneven resource allocation and misdirected recommendations;algorithmic model level,with design flaws and bias accumulation during optimization causing unfair resource allocation decisions;and result level,imposing implicit restrictions on students’learning opportunities and posing potential threats to educational and social equity.Through case studies of Online Education Platform A and Online Education Project B,the actual manifestations and impacts of algorithmic bias are demonstrated.To address these problems,corresponding countermeasures are proposed,including data governance strategies to improve data quality,algorithmic optimization strategies to enhance fairness and transparency,and educational management and policy recommendations to strengthen regulation and promote algorithmic literacy.This research not only reveals the harm of algorithmic bias but also provides a comprehensive and systematic solution framework,which has important theoretical and practical significance for promoting fair resource allocation in online education and realizing educational equity.展开更多
The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it i...The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it is necessary to conduct in-depth research and propose corresponding error detection and error elimination methods.This paper first proposes the root causes and threats of bias in AI algorithms,then summarizes the existing bias detection and error elimination methods,and proposes a bias processing framework in three-level dimensions of data,models,and conclusions,aiming to provide a framework for a comprehensive solution to errors in algorithms.At the same time,it also summarizes the problems and challenges in existing research and makes a prospect for future research trends.It is hoped that it will be helpful for us to build fairer AI.展开更多
In the realm of Artificial Intelligence (AI), there exists a complex landscape where promises of efficiency and innovation clash with unforeseen disruptions across Information Technology (IT) and broader societal real...In the realm of Artificial Intelligence (AI), there exists a complex landscape where promises of efficiency and innovation clash with unforeseen disruptions across Information Technology (IT) and broader societal realms. This paper sets out on a journey to explore the intricate paradoxes inherent in AI, focusing on the unintended consequences that ripple through IT and beyond. Through a thorough examination of literature and analysis of related works, this study aims to shed light on the complexities surrounding the AI paradox. It delves into how this paradox appears in various domains, such as algorithmic biases, job displacement, ethical dilemmas, and privacy concerns. By mapping out these unintended disruptions, this research seeks to offer a nuanced understanding of the challenges brought forth by AI-driven transformations. Ultimately, its goal is to pave the way for the responsible development and deployment of AI, fostering a harmonious integration of technological progress with societal values and priorities.展开更多
This paper examines the“algorithmic gaze”in fashion recommendation systems of Tmall,Rakuten,and Coupang through an eight-week audit using multiple user personas and 1,800 data points.Applying chi-square tests,KL div...This paper examines the“algorithmic gaze”in fashion recommendation systems of Tmall,Rakuten,and Coupang through an eight-week audit using multiple user personas and 1,800 data points.Applying chi-square tests,KL divergence,and Diversity@K,it identifies systemic biases in aesthetics,cultural representation,and body norms.Results show algorithms amplify Westernized mainstream styles,marginalize local attire,and intervene directly for commercial gain.The study proposes the concept of the“East Asian Algorithmic Gaze”to capture how platforms reinforce cultural homogenization,raising concerns for consumer subjectivity,cultural sustainability,and the need for stronger algorithmic accountability.展开更多
This paper delves into the intricate interplay between artificial intelligence(AI)systems and the perpetuation of Anti-Black racism within the United States medical industry.Despite the promising potential of AI to en...This paper delves into the intricate interplay between artificial intelligence(AI)systems and the perpetuation of Anti-Black racism within the United States medical industry.Despite the promising potential of AI to enhance healthcare outcomes and reduce disparities,there is a growing concern that these technologies may inadvertently/advertently exacerbate existing racial inequalities.Focusing specifically on the experiences of Black patients,this research investigates how the following AI components:medical algorithms,machine learning,and natural learning processes are contributing to the unequal distribution of medical resources,diagnosis,and health care treatment of those classified as Black.Furthermore,this review employs a multidisciplinary approach,combining insights from computer science,medical ethics,and social justice theory to analyze the mechanisms through which AI systems may encode and reinforce racial biases.By dissecting the three primary components of AI,this paper aims to present a clear understanding of how these technologies work,how they intersect,and how they may inherently perpetuate harmful stereotypes resulting in negligent outcomes for Black patients.Furthermore,this paper explores the ethical implications of deploying AI in healthcare settings and calls for increased transparency,accountability,and diversity in the development and implementation of these technologies.Finally,it is important that I prefer the following paper with a clear and concise definition of what I refer to as Anti-Black racism throughout the text.Therefore,I assert the following:Anti-Black racism refers to prejudice,discrimination,or antagonism directed against individuals or communities of African descent based on their race.It involves the belief in the inherent superiority of one race over another and the systemic and institutional practices that perpetuate inequality and disadvantage for Black people.Furthermore,I proclaim that this form of racism can be manifested in various ways,such as unequal access to opportunities,resources,education,employment,and fair treatment within social,economic,and political systems.It is also pertinent to acknowledge that Anti-Black racism is deeply rooted in historical and societal structures throughout the U.S.borders and beyond,leading to systemic disadvantages and disparities that impact the well-being and life chances of Black individuals and communities.Addressing Anti-Black racism involves recognizing and challenging both individual attitudes and systemic structures that contribute to discrimination and inequality.Efforts to combat Anti-Black racism include promoting awareness,education,advocacy for policy changes,and fostering a culture of inclusivity and equality.展开更多
The integration of artificial intelligence(AI)in education has revolutionized teaching and learning methodologies,offering personalized experiences and efficient resource management.However,this technological advancem...The integration of artificial intelligence(AI)in education has revolutionized teaching and learning methodologies,offering personalized experiences and efficient resource management.However,this technological advancement has also surfaced a plethora of ethical concerns that necessitate careful consideration.This paper delves into the ethical issues arising from AI applications in education,such as data privacy,algorithmic bias,educational equity,and the evolving role of teachers.Through a comprehensive analysis,we identify the challenges and propose strategic countermeasures to mitigate these ethical dilemmas.Case studies from both domestic and international contexts are employed to illustrate real-world applications and the associated ethical decision-making processes.The paper concludes with a summary of findings,policy recommendations,and an outlook on future research directions,emphasizing the need for a balanced approach that respects both technological innovation and ethical standards in educational AI deployment.展开更多
Background:The research problem addresses the need for accurate and efficient detection of retina diseases using artificial intelligence(AI)technologies.The specific aim is to evaluate the performance,ethical consider...Background:The research problem addresses the need for accurate and efficient detection of retina diseases using artificial intelligence(AI)technologies.The specific aim is to evaluate the performance,ethical considerations,and clinical implementation of AI-driven retina disease detection systems.Methods:This study is a systematic review.Data sources assessed included various electronic databases searched up to July 31,2023.The prespecified criteria for study inclusion were studies involving AI algorithms for retina disease detection,including those focused on diabetic retinopathy,age-related macular degeneration,and glaucoma.Participant eligibility criteria encompassed human subjects of all ages,and the interventions assessed were AI-based diagnostic tools compared to traditional diagnostic methods.Only randomized controlled trials and observational studies set in clinical environments were included,covering a time span from the inception of AI technology.Findings:The search identified 145 studies,of which 61 met the inclusion criteria and were eligible for analysis.The narrative summary of findings indicated that AI algorithms generally demonstrated high accuracy,sensitivity,and specificity in detecting retinal diseases.Deep learning algorithms showed a sensitivity of 90%and specificity of 98%for diabetic retinopathy detection.However,several studies highlighted concerns about algorithmic bias,data privacy,and the need for diverse and representative datasets to ensure generalizability across different populations.Interpretation:The AI-driven retina disease detection systems have significant potential to improve diagnostic accuracy and efficiency in clinical practice.Ethical considerations regarding patient privacy,the risk of algorithmic bias,and the challenges of integrating AI into existing healthcare workflows must be addressed.The study underscores the importance of ongoing validation,ethical scrutiny,and interdisciplinary collaboration to harness the benefits of AI while mitigating its risks,ensuring responsible and equitable implementation in clinical settings.展开更多
There have been widespread concerns about two aspects of the current explosion of predictive text models and other algorithm-based computational tools.On one hand,it is often insisted that Artificial Intelligence(AI)s...There have been widespread concerns about two aspects of the current explosion of predictive text models and other algorithm-based computational tools.On one hand,it is often insisted that Artificial Intelligence(AI)should be made“ethical”,and software providers take this seriously,attempting to make sure that their tools are not used to facilitate grossly criminal or widely condemned activities.On the other hand,it is also widely understood that those who create these tools have a responsibility to ensure that they are“unbiased”,as opposed to simply helping one side in political contestation define their perspectives as reality for all.Unfortunately,these two goals cannot be jointly satisfied,as there are perhaps no ethical prescriptions worthy of notice that are not contested by some.Here I investigate the current ethico-political sensibility of ChatGPT,demonstrating that the very attempt to give it an ethical keel has also given it a measurably left position in the political space and a concomitant position in social space among the privileged.展开更多
The transformative potential of Artificial Intelligence(AI)in the realm of smart cities is an evolving landscape of innovation and challenges.This research undertook a comprehensive exploration of AI's impact,harn...The transformative potential of Artificial Intelligence(AI)in the realm of smart cities is an evolving landscape of innovation and challenges.This research undertook a comprehensive exploration of AI's impact,harnessing both quantitative and qualitative methodologies.Detailed analyses were performed on data from select smart cities globally,focusing on sectors such as energy,traffic,health services,and waste management.Additionally,perceptions and experiences of urban stakeholders were captured through interviews.The results solidified AI's tangible benefits in enhancing urban life quality,while also bringing forth concerns about data privacy,algorithmic biases,and socio-economic implications.The study concludes with a call for holistic AI frameworks,heightened public engagement,and interdisciplinary collaborations to ensure the ethical and sustainable evolution of AI-integrated smart cities.展开更多
文摘This paper delves into the hidden impact of algorithmic bias on the allocation of online education resources.With the rapid development of online education,algorithms play a crucial role in resource allocation,but algorithmic bias has emerged as a significant issue.The study analyzes the impact of bias at three levels:data level,where data collection and annotation biases lead to uneven resource allocation and misdirected recommendations;algorithmic model level,with design flaws and bias accumulation during optimization causing unfair resource allocation decisions;and result level,imposing implicit restrictions on students’learning opportunities and posing potential threats to educational and social equity.Through case studies of Online Education Platform A and Online Education Project B,the actual manifestations and impacts of algorithmic bias are demonstrated.To address these problems,corresponding countermeasures are proposed,including data governance strategies to improve data quality,algorithmic optimization strategies to enhance fairness and transparency,and educational management and policy recommendations to strengthen regulation and promote algorithmic literacy.This research not only reveals the harm of algorithmic bias but also provides a comprehensive and systematic solution framework,which has important theoretical and practical significance for promoting fair resource allocation in online education and realizing educational equity.
文摘The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it is necessary to conduct in-depth research and propose corresponding error detection and error elimination methods.This paper first proposes the root causes and threats of bias in AI algorithms,then summarizes the existing bias detection and error elimination methods,and proposes a bias processing framework in three-level dimensions of data,models,and conclusions,aiming to provide a framework for a comprehensive solution to errors in algorithms.At the same time,it also summarizes the problems and challenges in existing research and makes a prospect for future research trends.It is hoped that it will be helpful for us to build fairer AI.
文摘In the realm of Artificial Intelligence (AI), there exists a complex landscape where promises of efficiency and innovation clash with unforeseen disruptions across Information Technology (IT) and broader societal realms. This paper sets out on a journey to explore the intricate paradoxes inherent in AI, focusing on the unintended consequences that ripple through IT and beyond. Through a thorough examination of literature and analysis of related works, this study aims to shed light on the complexities surrounding the AI paradox. It delves into how this paradox appears in various domains, such as algorithmic biases, job displacement, ethical dilemmas, and privacy concerns. By mapping out these unintended disruptions, this research seeks to offer a nuanced understanding of the challenges brought forth by AI-driven transformations. Ultimately, its goal is to pave the way for the responsible development and deployment of AI, fostering a harmonious integration of technological progress with societal values and priorities.
文摘This paper examines the“algorithmic gaze”in fashion recommendation systems of Tmall,Rakuten,and Coupang through an eight-week audit using multiple user personas and 1,800 data points.Applying chi-square tests,KL divergence,and Diversity@K,it identifies systemic biases in aesthetics,cultural representation,and body norms.Results show algorithms amplify Westernized mainstream styles,marginalize local attire,and intervene directly for commercial gain.The study proposes the concept of the“East Asian Algorithmic Gaze”to capture how platforms reinforce cultural homogenization,raising concerns for consumer subjectivity,cultural sustainability,and the need for stronger algorithmic accountability.
文摘This paper delves into the intricate interplay between artificial intelligence(AI)systems and the perpetuation of Anti-Black racism within the United States medical industry.Despite the promising potential of AI to enhance healthcare outcomes and reduce disparities,there is a growing concern that these technologies may inadvertently/advertently exacerbate existing racial inequalities.Focusing specifically on the experiences of Black patients,this research investigates how the following AI components:medical algorithms,machine learning,and natural learning processes are contributing to the unequal distribution of medical resources,diagnosis,and health care treatment of those classified as Black.Furthermore,this review employs a multidisciplinary approach,combining insights from computer science,medical ethics,and social justice theory to analyze the mechanisms through which AI systems may encode and reinforce racial biases.By dissecting the three primary components of AI,this paper aims to present a clear understanding of how these technologies work,how they intersect,and how they may inherently perpetuate harmful stereotypes resulting in negligent outcomes for Black patients.Furthermore,this paper explores the ethical implications of deploying AI in healthcare settings and calls for increased transparency,accountability,and diversity in the development and implementation of these technologies.Finally,it is important that I prefer the following paper with a clear and concise definition of what I refer to as Anti-Black racism throughout the text.Therefore,I assert the following:Anti-Black racism refers to prejudice,discrimination,or antagonism directed against individuals or communities of African descent based on their race.It involves the belief in the inherent superiority of one race over another and the systemic and institutional practices that perpetuate inequality and disadvantage for Black people.Furthermore,I proclaim that this form of racism can be manifested in various ways,such as unequal access to opportunities,resources,education,employment,and fair treatment within social,economic,and political systems.It is also pertinent to acknowledge that Anti-Black racism is deeply rooted in historical and societal structures throughout the U.S.borders and beyond,leading to systemic disadvantages and disparities that impact the well-being and life chances of Black individuals and communities.Addressing Anti-Black racism involves recognizing and challenging both individual attitudes and systemic structures that contribute to discrimination and inequality.Efforts to combat Anti-Black racism include promoting awareness,education,advocacy for policy changes,and fostering a culture of inclusivity and equality.
文摘The integration of artificial intelligence(AI)in education has revolutionized teaching and learning methodologies,offering personalized experiences and efficient resource management.However,this technological advancement has also surfaced a plethora of ethical concerns that necessitate careful consideration.This paper delves into the ethical issues arising from AI applications in education,such as data privacy,algorithmic bias,educational equity,and the evolving role of teachers.Through a comprehensive analysis,we identify the challenges and propose strategic countermeasures to mitigate these ethical dilemmas.Case studies from both domestic and international contexts are employed to illustrate real-world applications and the associated ethical decision-making processes.The paper concludes with a summary of findings,policy recommendations,and an outlook on future research directions,emphasizing the need for a balanced approach that respects both technological innovation and ethical standards in educational AI deployment.
文摘Background:The research problem addresses the need for accurate and efficient detection of retina diseases using artificial intelligence(AI)technologies.The specific aim is to evaluate the performance,ethical considerations,and clinical implementation of AI-driven retina disease detection systems.Methods:This study is a systematic review.Data sources assessed included various electronic databases searched up to July 31,2023.The prespecified criteria for study inclusion were studies involving AI algorithms for retina disease detection,including those focused on diabetic retinopathy,age-related macular degeneration,and glaucoma.Participant eligibility criteria encompassed human subjects of all ages,and the interventions assessed were AI-based diagnostic tools compared to traditional diagnostic methods.Only randomized controlled trials and observational studies set in clinical environments were included,covering a time span from the inception of AI technology.Findings:The search identified 145 studies,of which 61 met the inclusion criteria and were eligible for analysis.The narrative summary of findings indicated that AI algorithms generally demonstrated high accuracy,sensitivity,and specificity in detecting retinal diseases.Deep learning algorithms showed a sensitivity of 90%and specificity of 98%for diabetic retinopathy detection.However,several studies highlighted concerns about algorithmic bias,data privacy,and the need for diverse and representative datasets to ensure generalizability across different populations.Interpretation:The AI-driven retina disease detection systems have significant potential to improve diagnostic accuracy and efficiency in clinical practice.Ethical considerations regarding patient privacy,the risk of algorithmic bias,and the challenges of integrating AI into existing healthcare workflows must be addressed.The study underscores the importance of ongoing validation,ethical scrutiny,and interdisciplinary collaboration to harness the benefits of AI while mitigating its risks,ensuring responsible and equitable implementation in clinical settings.
文摘There have been widespread concerns about two aspects of the current explosion of predictive text models and other algorithm-based computational tools.On one hand,it is often insisted that Artificial Intelligence(AI)should be made“ethical”,and software providers take this seriously,attempting to make sure that their tools are not used to facilitate grossly criminal or widely condemned activities.On the other hand,it is also widely understood that those who create these tools have a responsibility to ensure that they are“unbiased”,as opposed to simply helping one side in political contestation define their perspectives as reality for all.Unfortunately,these two goals cannot be jointly satisfied,as there are perhaps no ethical prescriptions worthy of notice that are not contested by some.Here I investigate the current ethico-political sensibility of ChatGPT,demonstrating that the very attempt to give it an ethical keel has also given it a measurably left position in the political space and a concomitant position in social space among the privileged.
文摘The transformative potential of Artificial Intelligence(AI)in the realm of smart cities is an evolving landscape of innovation and challenges.This research undertook a comprehensive exploration of AI's impact,harnessing both quantitative and qualitative methodologies.Detailed analyses were performed on data from select smart cities globally,focusing on sectors such as energy,traffic,health services,and waste management.Additionally,perceptions and experiences of urban stakeholders were captured through interviews.The results solidified AI's tangible benefits in enhancing urban life quality,while also bringing forth concerns about data privacy,algorithmic biases,and socio-economic implications.The study concludes with a call for holistic AI frameworks,heightened public engagement,and interdisciplinary collaborations to ensure the ethical and sustainable evolution of AI-integrated smart cities.
