{"paper":{"title":"After the Interface: Relocating Human Agency in the Age of Conversational AI","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Human agency has not diminished in conversational AI but has relocated from interface controls to goal articulation and outcome evaluation.","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Mengke Wu, Mike Yao","submitted_at":"2026-05-14T16:54:57Z","abstract_excerpt":"As AI systems take on greater autonomy, a quiet anxiety has settled over the HCI community: human agency is eroding. Users no longer control execution, interfaces recede, and machines decide. We argue that this anxiety, while understandable, reflects a framing problem rather than an empirical finding. Agency has not diminished but has relocated. As interaction has shifted from command- and feature-based paradigms toward conversational, generative, and agentic AI, human agency migrates from interface affordances to interaction itself: articulating goals, evaluating outputs, and negotiating outc"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Agency has not diminished but has relocated. As interaction has shifted from command- and feature-based paradigms toward conversational, generative, and agentic AI, human agency migrates from interface affordances to interaction itself: articulating goals, evaluating outputs, and negotiating outcomes.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the process-control versus outcome-control distinction provides a sufficient diagnostic lens to demonstrate relocation, and that outcome-based agency remains meaningful rather than illusory even when AI outputs are plausible but unverifiable.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Human agency relocates from interface affordances to conversational processes of goal articulation, output evaluation, and outcome negotiation in AI interactions.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Human agency has not diminished in conversational AI but has relocated from interface controls to goal articulation and outcome evaluation.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"9acea00bdcdd6e853ba0a7e2f80bbc518bf32b88d88356ee1d732e4fcb671628"},"source":{"id":"2605.15064","kind":"arxiv","version":1},"verdict":{"id":"81253f9b-f9f9-4794-8f7c-063e8f84a326","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T03:10:47.898094Z","strongest_claim":"Agency has not diminished but has relocated. As interaction has shifted from command- and feature-based paradigms toward conversational, generative, and agentic AI, human agency migrates from interface affordances to interaction itself: articulating goals, evaluating outputs, and negotiating outcomes.","one_line_summary":"Human agency relocates from interface affordances to conversational processes of goal articulation, output evaluation, and outcome negotiation in AI interactions.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the process-control versus outcome-control distinction provides a sufficient diagnostic lens to demonstrate relocation, and that outcome-based agency remains meaningful rather than illusory even when AI outputs are plausible but unverifiable.","pith_extraction_headline":"Human agency has not diminished in conversational AI but has relocated from interface controls to goal articulation and outcome evaluation."},"references":{"count":56,"sample":[{"doi":"","year":2025,"title":"Deepak Bhaskar Acharya, Karthigeyan Kuppan, and B Divya. 2025. Agentic ai: Autonomous intelligence for complex goals–a comprehensive survey.IEEe Access (2025)","work_id":"839f092a-acb3-4a4b-8c18-b6c16bab4875","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Dan Bennett, Oussama Metatla, Anne Roudaut, and Elisa D Mekler. 2023. How does HCI understand human agency and autonomy?. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems","work_id":"0e97ad04-c410-46f1-af5f-114d0f80a61b","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Joanna Bergström, Jarrod Knibbe, Henning Pohl, and Kasper Hornbæk. 2022. Sense of agency and user experience: Is there a link?ACM Transactions on Computer-Human Interaction (TOCHI)29, 4 (2022), 1–22","work_id":"86467bbe-db2e-4416-9a31-f71a053bcf30","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Petter Bae Brandtzaeg, Yukun You, Xi Wang, and Yucong Lao. 2023. “Good” and “bad” machine agency in the context of human-AI communication: The case of ChatGPT. InInternational Conference on Human-Comp","work_id":"63d74195-b5ac-4aa3-9ca7-bd0a0137ca28","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Zana Buçinca, Maja Barbara Malaya, and Krzysztof Z Gajos. 2021. To trust or to think: cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making.Proceedings of the ACM on","work_id":"44a71cb4-d3c6-419b-a6a2-bd7df9b31684","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":56,"snapshot_sha256":"c433abe339ccd64a7cb38a67f8b7421336b75ebda5c279f0108c161b5c28a1e8","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}