Structural oppression is a pervasive characteristic of Indian society,disproportionately affecting underprivileged groups such as Dalits,women,religious minorities,and geographically segregated communities.Any develop...Structural oppression is a pervasive characteristic of Indian society,disproportionately affecting underprivileged groups such as Dalits,women,religious minorities,and geographically segregated communities.Any developments in the economy and technology therefore affect the lives of these marginalized groups unduly.While previous studies have explored the societal implications of intelligent systems,particularly prediction technologies such as recommender systems,ratings and reviews,there remains a significant gap in the literature concerning their impacts on these vulnerable social groups.Specifically,there is a lack of insights into the unique challenges faced by marginalized communities within the context of predictive technologies.This paper examines how the intelligent algorithms affect personal freedoms of weaker sections,particularly the right to choice,and whether they reinforce existing social biases within Indian society.By doing so,it seeks to inform policymakers and predict the implications of prediction technologies on liberty;thereby proposing insights on the similar other issues likely to affect underprivileged communities due to the widespread adoption of AI technologies.展开更多
This study introduces a Landscape Information Modeling±Stable Diffusion(LIM±SD)-based digital workflow for ecological engineered landscaping(EEL)design,focusing on urban river wetlands.It explores how studen...This study introduces a Landscape Information Modeling±Stable Diffusion(LIM±SD)-based digital workflow for ecological engineered landscaping(EEL)design,focusing on urban river wetlands.It explores how students from diverse academic backgrounds perform EEL tasks using the LIM±SD approach.A total of 30 participants,including industrial design postgraduates and landscape architecture undergraduates and postgraduates,completed the design tasks.The efficacy of their designs was assessed through expert evaluations on site appropriateness,aesthetics,spatial layout,and eco-engineering techniques of the design proposals,as well as the parametric simulation which calculated the vegetation coverage rate and proportion of riparian areas for each design.Moreover,evaluation of participants’subjective design experiences was conducted via questionnaires.Results indicated that landscape architecture postgraduates outperformed others applying ecological engineering principles.The study also elucidated discrepancies between LIM models and SD-generated renderings,as well as the uncertainty of SDgenerated renderings,suggesting improvements are needed to align digital outputs with ecological design criteria.展开更多
文摘Structural oppression is a pervasive characteristic of Indian society,disproportionately affecting underprivileged groups such as Dalits,women,religious minorities,and geographically segregated communities.Any developments in the economy and technology therefore affect the lives of these marginalized groups unduly.While previous studies have explored the societal implications of intelligent systems,particularly prediction technologies such as recommender systems,ratings and reviews,there remains a significant gap in the literature concerning their impacts on these vulnerable social groups.Specifically,there is a lack of insights into the unique challenges faced by marginalized communities within the context of predictive technologies.This paper examines how the intelligent algorithms affect personal freedoms of weaker sections,particularly the right to choice,and whether they reinforce existing social biases within Indian society.By doing so,it seeks to inform policymakers and predict the implications of prediction technologies on liberty;thereby proposing insights on the similar other issues likely to affect underprivileged communities due to the widespread adoption of AI technologies.
文摘This study introduces a Landscape Information Modeling±Stable Diffusion(LIM±SD)-based digital workflow for ecological engineered landscaping(EEL)design,focusing on urban river wetlands.It explores how students from diverse academic backgrounds perform EEL tasks using the LIM±SD approach.A total of 30 participants,including industrial design postgraduates and landscape architecture undergraduates and postgraduates,completed the design tasks.The efficacy of their designs was assessed through expert evaluations on site appropriateness,aesthetics,spatial layout,and eco-engineering techniques of the design proposals,as well as the parametric simulation which calculated the vegetation coverage rate and proportion of riparian areas for each design.Moreover,evaluation of participants’subjective design experiences was conducted via questionnaires.Results indicated that landscape architecture postgraduates outperformed others applying ecological engineering principles.The study also elucidated discrepancies between LIM models and SD-generated renderings,as well as the uncertainty of SDgenerated renderings,suggesting improvements are needed to align digital outputs with ecological design criteria.
