Related papers: Artificial intelligence and the skill premium

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We argue that human participation may persist even with highly capable AI systems.<n>In some activities, the target is not fully specified in advance but instead emerges through the interaction itself.<n>This perspective has important implications for the limits of automation.
arXiv Detail & Related papers (2026-07-23T17:33:43Z)
Managing the Human Fallback: Skill Investment Under Improving AI and Worker Mobility [0.0]
We show that when firms deploy autonomous AI, they must decide how much work to leave to the system and how much to keep workers engaged.<n>We develop a parsimonious two-period model in which AI may outperform the worker when it functions, but may fail with positive probability.<n>A firm chooses worker engagement; engagement lowers current output for below-benchmark workers, but changes future skill through learning and erosion.
arXiv Detail & Related papers (2026-06-27T23:34:23Z)
CollabSkill: Evaluating Human-Agent Collaboration On Real-World Tasks [55.84031315625504]
CollabSkill is a framework for evaluating human-agent collaboration on real-world occupational tasks.<n>It pairs real human workers with AI agents on tasks matched to their occupational background.<n>Our analysis reveals that practical experience emerges as the primary driver of collaboration skill.
arXiv Detail & Related papers (2026-04-20T03:03:25Z)
A roadmap for AI in robotics [55.87087746398059]
We are witnessing growing excitement in robotics at the prospect of leveraging the potential of AI to tackle some of the outstanding barriers to the full deployment of robots in our daily lives.<n>This article offers an assessment of what AI for robotics has achieved since the 1990s and proposes a short- and medium-term research roadmap listing challenges and promises.
arXiv Detail & Related papers (2025-07-26T15:18:28Z)
The Potential Impact of Disruptive AI Innovations on U.S. Occupations [4.616985737735413]
We calculate the disruption index of 3,237 U.S. AI patents (2015-2022)<n>Our analysis reveals that consolidating AI primarily targets physical, routine, and solo tasks.<n>By contrast, disruptive AI affects unpredictable and mental tasks, particularly in coastal science and technology sectors.
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We introduce a novel framework to assess which occupational tasks workers want AI agents to automate or augment.<n>Our framework features an audio-enhanced mini-interview to capture nuanced worker desires.<n>We construct the WORKBank database to capture preferences from 1,500 domain workers and capability assessments from AI experts.
arXiv Detail & Related papers (2025-06-06T23:05:52Z)
Superintelligence Strategy: Expert Version [64.7113737051525]
Destabilizing AI developments could raise the odds of great-power conflict. Superintelligence -- AI vastly better than humans at nearly all cognitive tasks -- is now anticipated by AI researchers. We introduce the concept of Mutual Assured AI Malfunction.
arXiv Detail & Related papers (2025-03-07T17:53:24Z)
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This paper examines whether artificial intelligence (AI) acts as a substitute or complement to human labour. It draws on 12 million online job vacancies from the United States spanning 2018-2023. Results show that AI-focused roles are nearly twice as likely to require skills like resilience, agility, or analytical thinking.
arXiv Detail & Related papers (2024-12-27T17:26:30Z)
Augmenting Minds or Automating Skills: The Differential Role of Human Capital in Generative AI's Impact on Creative Tasks [4.39919134458872]
Generative AI is rapidly reshaping creative work, raising critical questions about its beneficiaries and societal implications. This study challenges prevailing assumptions by exploring how generative AI interacts with diverse forms of human capital in creative tasks. While AI democratizes access to creative tools, it simultaneously amplifies cognitive inequalities.
arXiv Detail & Related papers (2024-12-05T08:27:14Z)
Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We argue that shortcomings stem from one overarching failure: AI systems lack wisdom. While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
arXiv Detail & Related papers (2024-11-04T18:10:10Z)
Raising the Stakes: Performance Pressure Improves AI-Assisted Decision Making [57.53469908423318]
We show the effects of performance pressure on AI advice reliance when laypeople complete a common AI-assisted task. We find that when the stakes are high, people use AI advice more appropriately than when stakes are lower, regardless of the presence of an AI explanation.
arXiv Detail & Related papers (2024-10-21T22:39:52Z)
The Model Mastery Lifecycle: A Framework for Designing Human-AI Interaction [0.0]
The utilization of AI in an increasing number of fields is the latest iteration of a long process. There is an urgent need for methods to determine how AI should be used in different situations.
arXiv Detail & Related papers (2024-08-23T01:00:32Z)
Towards the Terminator Economy: Assessing Job Exposure to AI through LLMs [10.844598404826355]
One-third of U.S. employment is highly exposed to AI, primarily in high-skill jobs. This exposure correlates positively with employment and wage growth from 2019 to 2023.
arXiv Detail & Related papers (2024-07-27T08:14:18Z)
Robotic Control via Embodied Chain-of-Thought Reasoning [86.6680905262442]
Key limitation of learned robot control policies is their inability to generalize outside their training data. Recent works on vision-language-action models (VLAs) have shown that the use of large, internet pre-trained vision-language models can substantially improve their robustness and generalization ability. We introduce Embodied Chain-of-Thought Reasoning (ECoT) for VLAs, in which we train VLAs to perform multiple steps of reasoning about plans, sub-tasks, motions, and visually grounded features before predicting the robot action.
arXiv Detail & Related papers (2024-07-11T17:31:01Z)
Brief for the Canada House of Commons Study on the Implications of Artificial Intelligence Technologies for the Canadian Labor Force: Generative Artificial Intelligence Shatters Models of AI and Labor [1.0878040851638]
As with past technologies, generative AI may not lead to mass unemployment. generative AI is creative, cognitive, and potentially ubiquitous. As AI's full set of capabilities and applications emerge, policy makers should promote workers' career adaptability.
arXiv Detail & Related papers (2023-11-06T22:58:24Z)
Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity [3.899855581265355]
We introduce the Hierarchical Trial and Error algorithm, which uses a hierarchical behavioural repertoire to learn diverse skills. We show that the hierarchical decomposition of skills enables the robot to learn more complex behaviours while keeping the learning of the repertoire tractable.
arXiv Detail & Related papers (2022-10-18T15:02:41Z)
The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence [1.9143819780453073]
The benefits of human-like artificial intelligence include soaring productivity, increased leisure, and perhaps most profoundly, a better understanding of our own minds. But not all types of AI are human-like. In fact, many of the most powerful systems are very different from humans. As machines become better substitutes for human labor, workers lose economic and political bargaining power. In contrast, when AI is focused on augmenting humans rather than mimicking them, then humans retain the power to insist on a share of the value created.
arXiv Detail & Related papers (2022-01-11T21:07:17Z)
Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z)
Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z)
Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork [54.309495231017344]
We argue that AI systems should be trained in a human-centered manner, directly optimized for team performance. We study this proposal for a specific type of human-AI teaming, where the human overseer chooses to either accept the AI recommendation or solve the task themselves. Our experiments with linear and non-linear models on real-world, high-stakes datasets show that the most accuracy AI may not lead to highest team performance.
arXiv Detail & Related papers (2020-04-27T19:06:28Z)

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