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 "name": "Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction",
 "author": [
  {
   "@type": "Person",
   "name": "Auste Simkute"
  },
  {
   "@type": "Person",
   "name": "Lev Tankelevitch"
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  {
   "@type": "Person",
   "name": "Viktor Kewenig"
  },
  {
   "@type": "Person",
   "name": "Ava Elizabeth Scott"
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  {
   "@type": "Person",
   "name": "Abigail Sellen"
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 "datePublished": "2024-10",
 "doi": "10.1080/10447318.2024.2405782",
 "url": "https://seanrintel.com/publications/ironies-of-generative-ai-understanding-and-mitigating-productivity-loss-in-human-ai-interaction.html",
 "citation": "Auste Simkute, Lev Tankelevitch, Viktor Kewenig, Ava Elizabeth Scott, Abigail Sellen, and Sean Rintel. 2024. Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2024.2405782",
 "bibtex": "@article{simkute2024ironies,\nauthor = {Simkute, Auste and Tankelevitch, Lev and Kewenig, Viktor and Scott, Ava Elizabeth and Sellen, Abigail and Rintel, Sean},\ntitle = {Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction},\nyear = {2024},\nmonth = {October},\nurl = {https://doi.org/10.1080/10447318.2024.2405782},\njournal = {International Journal of Human–Computer Interaction},\ndoi = {10.1080/10447318.2024.2405782},\n}",
 "abstract": "Generative AI (GenAI) systems offer opportunities to increase user productivity in many tasks, such as programming and writing. However, while they boost productivity in some studies, many others show that users are working ineffectively with GenAI systems and losing productivity. Despite the apparent novelty of these usability challenges, these ‘ironies of automation’ have been observed for over three decades in Human Factors research on the introduction of automation in domains such as aviation, automated driving, and intelligence. We draw on this extensive research alongside recent GenAI user studies to outline four key reasons for productivity loss with GenAI systems: a shift in users’ roles from production to evaluation, unhelpful restructuring of workflows, interruptions, and a tendency for automation to make easy tasks easier and hard tasks harder. We then suggest how Human Factors research can also inform GenAI system design to mitigate productivity loss by using approaches such as continuous feedback, system personalization, ecological interface design, task stabilization, and clear task allocation. Thus, we ground developments in GenAI system usability in decades of Human Factors research, ensuring that the design of human-AI interactions in this rapidly moving field learns from history instead of repeating it.",
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 "takeaway": "Decades of automation research predict where generative AI costs productivity. People shift from producing work to checking output they didn't create, which demands understanding they no longer have.",
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  "thesis": "Generative AI moves people's thinking into stating what they want and judging what they get. The easiest way to use it can help least, so good tools ask people to think first.",
  "claimsThisPaperFeeds": [
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    "claim": "AI shifts effort from doing work to overseeing it",
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  ]
 },
 "whyItMatters": {
  "stateOfTheArt": {
   "summary": "Generative AI is expected to raise productivity, and some studies show gains, but users, especially programmers, also report extra cognitive load, frustration and lost time.",
   "quote": "This suggests that the potential of GenAI systems to boost productivity may not be guaranteed, evenly distributed, or fully exploited.",
   "location": "§1, p. 2"
  },
  "contribution": {
   "summary": "Four named causes of productivity loss with GenAI, each matched with design remedies from Human Factors research.",
   "quote": "Extrapolating from over 30 years of Human Factors research on the \"ironies\" of human-automation and productivity loss, we synthesize an overview of the usability and productivity challenges observed in recent GenAI user studies.",
   "location": "§1, p. 3"
  },
  "whoCares": {
   "summary": "Designers and builders of GenAI tools and copilots, and HCI and Human Factors researchers.",
   "quote": "ensuring that the design of human-AI interactions in this rapidly moving field learns from history instead of repeating it",
   "location": "Abstract, p. 2"
  }
 },
 "researchQuestions": [
  "Why do users of generative AI systems sometimes lose rather than gain productivity, and how do these challenges parallel those found in Human Factors research on automation?",
  "Which Human Factors design approaches could mitigate productivity loss in human-GenAI interaction?"
 ],
 "concepts": [
  {
   "term": "Ironies of automation",
   "definition": "Bainbridge's term from automation research, which the authors apply to GenAI:",
   "quote": "the more advanced an automated system is, the more important the human operator may be",
   "location": "§1, p. 2"
  },
  {
   "term": "Production-to-evaluation shift",
   "definition": "The authors' name for the first of four GenAI productivity challenges:",
   "quote": "users' roles have shifted from producing output to evaluating it, often with little contextual information and situational awareness",
   "location": "§1, p. 2"
  },
  {
   "term": "Situational awareness",
   "definition": "Endsley's definition, central to why evaluating AI output is hard:",
   "quote": "their perception of data and elements of the situation, comprehension of the situation, and the projection of future status",
   "location": "§3.1.1, p. 6"
  },
  {
   "term": "Unhelpful workflow restructuring",
   "definition": "The second challenge, in the words of automation research:",
   "quote": "Automation can restructure workflows in unhelpful ways by introducing new challenging tasks, disrupting familiar task sequences, and removing informative feedback",
   "location": "§3.2, p. 8"
  },
  {
   "term": "Task-complexity polarization",
   "definition": "The authors' more precise term for what Human Factors research calls 'clumsy automation':",
   "quote": "Automation often makes easy tasks easier but fails to reduce the workload during cognitively demanding tasks, and in fact, often makes them harder",
   "location": "§3.4, p. 11"
  }
 ],
 "method": {
  "design": "Narrative review in two stages. It first distilled challenges from Bainbridge's 'Ironies of Automation' and related Human Factors research, then searched the generative AI literature for studies reporting productivity losses or ineffective use.",
  "setting": "GenAI user studies, mainly programming (e.g., GitHub Copilot), plus writing, design, data science, law, consulting and healthcare.",
  "sample": "Google Scholar and ACM Digital Library searches (generative AI, large language model, LLM, ChatGPT), cut-off 2024-01-20, including pre-prints. Included GenAI articles are listed in Table 1 with domain, task, method and challenge theme.",
  "analysis": "Thematic synthesis aligning GenAI findings with four Human Factors challenge themes, then mapping Human Factors solutions to each challenge."
 },
 "findings": [
  {
   "headline": "From producing to checking.",
   "summary": "Across the studies the authors reviewed, mostly of programmers using AI coding assistants, generative AI turns people from producers of work into evaluators of AI output. Checking is demanding because people lack the understanding of the situation they would have had if they had written the work themselves.",
   "quote": "Some programmers have said that working with Copilot felt like a \"proofreading task\"",
   "location": "§3.1, p. 4",
   "url": "https://seanrintel.com/publications/ironies-of-generative-ai-understanding-and-mitigating-productivity-loss-in-human-ai-interaction.html#at-s3-1-p-4",
   "cite": "Auste Simkute, Lev Tankelevitch, Viktor Kewenig, Ava Elizabeth Scott, Abigail Sellen, and Sean Rintel. 2024. Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2024.2405782 (§3.1, p. 4)"
  },
  {
   "headline": "Over-trust follows.",
   "summary": "As automation research predicts, the shift to checking, combined with reduced awareness and extra workload, can breed complacency and over-reliance. Early studies of AI-assisted writing already show people accepting AI drafts without revising them thoroughly.",
   "quote": "both expert and non-expert writers showed overconfidence in the quality of AI-generated drafts, failing to thoroughly revise them",
   "location": "§3.1.3, p. 8",
   "url": "https://seanrintel.com/publications/ironies-of-generative-ai-understanding-and-mitigating-productivity-loss-in-human-ai-interaction.html#at-s3-1-3-p-8",
   "cite": "Auste Simkute, Lev Tankelevitch, Viktor Kewenig, Ava Elizabeth Scott, Abigail Sellen, and Sean Rintel. 2024. Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2024.2405782 (§3.1.3, p. 8)"
  },
  {
   "headline": "New tasks get in the way.",
   "summary": "Prompting the AI and adapting its output are new tasks that compete with the main work and break up familiar ways of working, as a designer in a study by Kulkarni and colleagues described.",
   "quote": "I had to massage every word and select every character very carefully not to upset it",
   "location": "§3.2.1, p. 9",
   "url": "https://seanrintel.com/publications/ironies-of-generative-ai-understanding-and-mitigating-productivity-loss-in-human-ai-interaction.html#at-s3-2-1-p-9",
   "cite": "Auste Simkute, Lev Tankelevitch, Viktor Kewenig, Ava Elizabeth Scott, Abigail Sellen, and Sean Rintel. 2024. Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2024.2405782 (§3.2.1, p. 9)"
  },
  {
   "headline": "Suggestions break concentration.",
   "summary": "Unrequested AI suggestions interrupt people's flow. Even a suggestion they reject has already pulled their thinking away, as an experienced programmer in a study by Barke and colleagues put it.",
   "quote": "I was about to write the code, and I knew what I wanted to write. But now I'm sitting here, seeing if somehow Copilot came up with something better",
   "location": "§3.3, p. 11",
   "url": "https://seanrintel.com/publications/ironies-of-generative-ai-understanding-and-mitigating-productivity-loss-in-human-ai-interaction.html#at-s3-3-p-11",
   "cite": "Auste Simkute, Lev Tankelevitch, Viktor Kewenig, Ava Elizabeth Scott, Abigail Sellen, and Sean Rintel. 2024. Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2024.2405782 (§3.3, p. 11)"
  },
  {
   "headline": "Easy tasks get easier, hard ones harder.",
   "summary": "Generative AI helps most with simple tasks and can make demanding ones harder, through subtle errors, overwhelming suggestions and disrupted workflows. A study of legal work by Choi and Schwarcz supports the first half:",
   "quote": "AI helps with simple legal analysis but stumbles over complex legal reasoning",
   "location": "§3.4, p. 11",
   "url": "https://seanrintel.com/publications/ironies-of-generative-ai-understanding-and-mitigating-productivity-loss-in-human-ai-interaction.html#at-s3-4-p-11",
   "cite": "Auste Simkute, Lev Tankelevitch, Viktor Kewenig, Ava Elizabeth Scott, Abigail Sellen, and Sean Rintel. 2024. Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2024.2405782 (§3.4, p. 11)"
  }
 ],
 "questions": [
  {
   "question": "Why does AI help more with easy tasks than hard ones?",
   "answer": "Automation research calls this task-complexity polarization. Easy work is easiest to automate, so hard work stays with people, now with added monitoring in a fragmented workflow. Generative AI studies agree. The hardest part, such as breaking a coding problem down, gets little help, and subtle errors make debugging harder.",
   "source": "Auste Simkute, Lev Tankelevitch, Viktor Kewenig, Ava Elizabeth Scott, Abigail Sellen, and Sean Rintel. 2024. Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2024.2405782",
   "basedOn": [
    {
     "location": "§3.4, p. 11",
     "url": "https://seanrintel.com/publications/ironies-of-generative-ai-understanding-and-mitigating-productivity-loss-in-human-ai-interaction.html#at-s3-4-p-11"
    },
    {
     "location": "§3.1, p. 4",
     "url": "https://seanrintel.com/publications/ironies-of-generative-ai-understanding-and-mitigating-productivity-loss-in-human-ai-interaction.html#at-s3-1-p-4"
    }
   ]
  },
  {
   "question": "Do people really over-rely on AI?",
   "answer": "Automation research predicts it, and early generative AI studies already show it. When people shift to checking AI output, with less awareness of how it was made and more to do, they tend to accept it without enough scrutiny.",
   "source": "Auste Simkute, Lev Tankelevitch, Viktor Kewenig, Ava Elizabeth Scott, Abigail Sellen, and Sean Rintel. 2024. Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2024.2405782",
   "basedOn": [
    {
     "location": "§3.1.3, p. 8",
     "url": "https://seanrintel.com/publications/ironies-of-generative-ai-understanding-and-mitigating-productivity-loss-in-human-ai-interaction.html#at-s3-1-3-p-8"
    }
   ]
  },
  {
   "question": "What are the 'ironies of automation', and do they apply to AI?",
   "answer": "They are a pattern from decades of automation research. The more advanced the automation, the more important the human's remaining role may be, and in some cases the harder it becomes. The same pattern is appearing in studies of generative AI tools.",
   "source": "Auste Simkute, Lev Tankelevitch, Viktor Kewenig, Ava Elizabeth Scott, Abigail Sellen, and Sean Rintel. 2024. Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2024.2405782",
   "basedOn": [
    {
     "location": "§3.1, p. 4",
     "url": "https://seanrintel.com/publications/ironies-of-generative-ai-understanding-and-mitigating-productivity-loss-in-human-ai-interaction.html#at-s3-1-p-4"
    },
    {
     "location": "§3.4, p. 11",
     "url": "https://seanrintel.com/publications/ironies-of-generative-ai-understanding-and-mitigating-productivity-loss-in-human-ai-interaction.html#at-s3-4-p-11"
    }
   ]
  }
 ],
 "cites": [
  {
   "url": "https://seanrintel.com/publications/the-metacognitive-demands-and-opportunities-of-generative-ai.html",
   "title": "The Metacognitive Demands and Opportunities of Generative AI",
   "location": "p. 22"
  }
 ],
 "quantitative": [],
 "implications": [
  {
   "summary": "Continuous feedback and explainability keep users in the loop by showing what the system relies on, its confidence, alternatives and how prompts map to outputs.",
   "quote": "We propose that feedback about system behavior is a key strategy to keep users engaged and in the loop of GenAI system performance.",
   "location": "§4.1, p. 12"
  },
  {
   "summary": "System personalization lets users choose inputs, the type of assistance and its timing to fit their way of working."
  },
  {
   "summary": "Ecological interface design fits GenAI to domain-specific workflows, perceptual constraints and explainability needs."
  },
  {
   "summary": "Main-task stabilization and interruption timing rely on attention guidance, pre-interruption alerts, and suggestions timed to users' flow states."
  },
  {
   "summary": "Clear task allocation between user and system, which users can set themselves.",
   "quote": "the allocation of tasks between the user and GenAI system should be clearly defined and supported by GenAI systems",
   "location": "§4.5, p. 17"
  }
 ],
 "limitations": [
  {
   "summary": "A narrative, non-exhaustive review that may have missed articles.",
   "quote": "As our approach was a narrative review, our search was not systematic or exhaustive, and there may be articles we have missed.",
   "location": "§2, p. 4"
  },
  {
   "summary": "It deliberately focuses on productivity losses, while acknowledging that gains have also been observed."
  },
  {
   "summary": "Skill development and expertise demands were left out because they were too early to observe in GenAI research."
  },
  {
   "summary": "The proposed solutions come from Human Factors research and need testing in GenAI contexts."
  }
 ],
 "figures": [
  {
   "src": "https://seanrintel.com/publications/figures/ironies-of-generative-ai-understanding-and-mitigating-productivity-loss-in-human-ai-interaction/fig-1.webp",
   "caption": "Figure 1. Productivity challenges of Generative AI automation: (a) the production-to-evaluation shift, in which users' situational awareness of their working environment is reduced, increasing the cognitive demand required to evaluate AI outputs; (b) unhelpful workflow restructuring, including the addition of new challenging tasks of prompting systems and adapting outputs, a loss of task sequence due to AI suggestions or other changes, and a loss of feedback when AI suggestions are presented without the relevant context; (c) task interruptions from automated AI suggestions; and (d) task-complexity polarization, in which automation tends to make easy tasks easier and hard tasks harder when implemented in practice.",
   "alt": "Four icon panels. (a) A user shifts from writing code to inspecting AI output. (b) Prompting, output adaptation, lost task sequence, lost feedback. (c) An AI suggestion interrupts a user. (d) Easy tasks succeed while complex ones gain errors.",
   "location": "Figure 1, p. 6",
   "credit": "Figure 1 from Auste Simkute, Lev Tankelevitch, Viktor Kewenig, Ava Elizabeth Scott, Abigail Sellen, and Sean Rintel. 2024. Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2024.2405782"
  },
  {
   "src": "https://seanrintel.com/publications/figures/ironies-of-generative-ai-understanding-and-mitigating-productivity-loss-in-human-ai-interaction/fig-2.webp",
   "caption": "Figure 2. Examples of approaches to providing continuous feedback and increasing explainability which can help address the challenges of the [production-to-evaluation] shift (Section 3.1). Examples include providing alternative solutions, adding links to relevant documentation or other information sources, providing relevant usage examples, conveying information based on confidence, highlighting prompt changes and resulting output changes, and providing information linking errors to inputs.",
   "alt": "Six icons for feedback features addressing the production-to-evaluation shift: alternative solutions, usage examples, highlighted prompt and output changes, links to documentation, confidence estimates, and errors mapped to inputs.",
   "location": "Figure 2, p. 13",
   "credit": "Figure 2 from Auste Simkute, Lev Tankelevitch, Viktor Kewenig, Ava Elizabeth Scott, Abigail Sellen, and Sean Rintel. 2024. Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2024.2405782"
  }
 ],
 "pageNumbersReferTo": "https://seanrintel.com/publications/ironies-of-generative-ai-understanding-and-mitigating-productivity-loss-in-human-ai-interaction.pdf",
 "verified": "2026-10-25",
 "studyFigure": {
  "about": "The opening figure on this paper's page, made for the site with AI assistance from this verified brief. It is not the paper's own figure. Evidence entries index this file's quantitative, findings and limitations arrays.",
  "study": {
   "kind": "single",
   "heading": "The paper",
   "summary": "A narrative review linking three decades of automation research, in fields such as aviation and driving, to recent studies of people using generative AI tools such as GitHub Copilot. Its literature search ran to January 2024.",
   "steps": [
    {
     "label": "Automation research",
     "detail": "Challenges drawn from Bainbridge's Ironies of Automation and related Human Factors research on how people work with automated systems."
    },
    {
     "label": "Generative AI studies",
     "detail": "Searches for studies reporting productivity loss or ineffective use of generative AI, mostly in programming, including pre-prints."
    },
    {
     "label": "Matching challenges",
     "detail": "Findings on generative AI were grouped under four challenge themes from automation research."
    },
    {
     "label": "Matching solutions",
     "detail": "Design approaches from automation research were mapped to each of the four challenges."
    }
   ]
  },
  "findings": {
   "heading": "What the paper argues",
   "legend": null,
   "stages": [
    {
     "title": "Why productivity can fall",
     "sub": "four challenges argued from automation research and generative AI studies",
     "items": [
      {
       "dir": "none",
       "text": "The paper argues generative AI shifts people from producing work to checking it.",
       "detail": "Checking is hard because people lack the understanding they would have had from doing the work. Some programmers said an AI coding assistant felt like proofreading.",
       "evidence": [
        "findings[0]"
       ]
      },
      {
       "dir": "none",
       "text": "The paper argues this shift to checking can breed over-trust and over-reliance.",
       "detail": "Early studies of AI-assisted writing found expert and non-expert writers overconfident in AI drafts and failing to revise them thoroughly.",
       "evidence": [
        "findings[1]"
       ]
      },
      {
       "dir": "none",
       "text": "The paper argues prompting and adapting AI output add tasks that compete with the main work.",
       "detail": "These new tasks also break up familiar ways of working.",
       "evidence": [
        "findings[2]"
       ]
      },
      {
       "dir": "none",
       "text": "The paper argues unrequested AI suggestions interrupt people's flow.",
       "detail": "Even a suggestion a person rejects has already pulled that person's thinking away from the task.",
       "evidence": [
        "findings[3]"
       ]
      },
      {
       "dir": "none",
       "text": "The paper argues generative AI tends to make easy tasks easier and hard tasks harder.",
       "detail": "Subtle errors, overwhelming suggestions and disrupted workflows weigh most on demanding work. A study of legal work found AI helped with simple analysis but stumbled on complex reasoning.",
       "evidence": [
        "findings[4]"
       ]
      }
     ]
    },
    {
     "title": "What could help",
     "sub": "design approaches proposed from automation research",
     "items": [
      {
       "dir": "none",
       "text": "The paper proposes design approaches from automation research to reduce productivity loss.",
       "detail": "The approaches include continuous feedback, personalisation, interfaces fitted to each field's work, steadier main tasks and clear task allocation. These still need testing with generative AI.",
       "evidence": [
        "abstract",
        "limitations[3]"
       ]
      },
      {
       "dir": "none",
       "text": "The paper notes its review was narrative and may have missed studies.",
       "detail": "The review deliberately focused on productivity losses, while acknowledging that some studies have observed gains.",
       "evidence": [
        "limitations[0]",
        "limitations[1]"
       ]
      }
     ]
    }
   ]
  },
  "keyTerm": "ironies of automation",
  "takeaway": "Decades of research on the ironies of automation can guide generative AI design that keeps people productive. When AI turns people from makers into checkers, feedback and clear roles may restore the understanding that checking needs."
 }
}