Scholarlink

<p>The increasing reliance on artificial intelligence (AI) for societal needs presents a growing environmental concern, particularly regarding user perceptions and accountability. This study examines how Young Indian Adults (YIAs; aged 18–24) reconcile their environmental values with their reliance on AI and assesses their perceived locus of accountability for the environmental repercussions of large language models (LLMs) within hybrid human–AI systems. A cross-sectional, open-ended survey (<i>N</i> = 349) was administered to students at an Indian technical university. The survey featured a three-phase inquiry, including an informational prompt detailing the environmental costs of LLMs. The Machine-Assisted Topic Analysis (MATA) framework was used to analyze the data by combining dictionary-based lexicon-matching with Latent Dirichlet Allocation (LDA) topic modeling. Baseline discourse showed an epistemic gap over material costs, which is consistent with the theory of sociotechnical abstraction, where the material reality of data is obscured. However, the informational stimulus was followed by&#xa0;a discursive split, changing the focus toward concerns pertaining to energy consumption and carbon emissions of LLMs. This study identified a&#xa0;discursive inversion of accountability. YIAs attribute the environmental costs of LLMs exclusively to the tech industry, despite acknowledging personal and civic responsibility for climate change in general. This challenges Western-centric AI ethics by demonstrating that environmental awareness among tech-educated youth in India manifests as calls for systemic efficiency rather than behavioral abstinence. To minimize computational opacity, the study argues for shifting policy from digital to ecological sovereignty and shifting education toward material AI literacy.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The shifting locus of accountability: the discourse of young Indian adults (YIAs) on the environmental impact of artificial intelligence

  • 期刊论文

  • 发表日期：

    2026年4月24日

查看全文
  • Bashir Ansari,
  • Ahmed Sameer

摘要

The increasing reliance on artificial intelligence (AI) for societal needs presents a growing environmental concern, particularly regarding user perceptions and accountability. This study examines how Young Indian Adults (YIAs; aged 18–24) reconcile their environmental values with their reliance on AI and assesses their perceived locus of accountability for the environmental repercussions of large language models (LLMs) within hybrid human–AI systems. A cross-sectional, open-ended survey (N = 349) was administered to students at an Indian technical university. The survey featured a three-phase inquiry, including an informational prompt detailing the environmental costs of LLMs. The Machine-Assisted Topic Analysis (MATA) framework was used to analyze the data by combining dictionary-based lexicon-matching with Latent Dirichlet Allocation (LDA) topic modeling. Baseline discourse showed an epistemic gap over material costs, which is consistent with the theory of sociotechnical abstraction, where the material reality of data is obscured. However, the informational stimulus was followed by a discursive split, changing the focus toward concerns pertaining to energy consumption and carbon emissions of LLMs. This study identified a discursive inversion of accountability. YIAs attribute the environmental costs of LLMs exclusively to the tech industry, despite acknowledging personal and civic responsibility for climate change in general. This challenges Western-centric AI ethics by demonstrating that environmental awareness among tech-educated youth in India manifests as calls for systemic efficiency rather than behavioral abstinence. To minimize computational opacity, the study argues for shifting policy from digital to ecological sovereignty and shifting education toward material AI literacy.