Related papers: Reversing the Paradigm: Building AI-First Systems with Human Guidance

Reversing the Paradigm: Building AI-First Systems with Human Guidance

URL: http://arxiv.org/abs/2506.12245v1
Date: Fri, 13 Jun 2025 21:48:44 GMT
Title: Reversing the Paradigm: Building AI-First Systems with Human Guidance
Authors: Cosimo Spera, Garima Agrawal,
Abstract summary: The relationship between humans and artificial intelligence is no longer science fiction.<n>Rather than replacing humans, AI augments tasks, enhancing decisions with data.<n>The future of work is toward AI agents handling tasks autonomously.<n>This paper examines the technological and organizational changes needed to enable responsible adoption of AI-first systems.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The relationship between humans and artificial intelligence is no longer science fiction -- it's a growing reality reshaping how we live and work. AI has moved beyond research labs into everyday life, powering customer service chats, personalizing travel, aiding doctors in diagnosis, and supporting educators. What makes this moment particularly compelling is AI's increasing collaborative nature. Rather than replacing humans, AI augments our capabilities -- automating routine tasks, enhancing decisions with data, and enabling creativity in fields like design, music, and writing. The future of work is shifting toward AI agents handling tasks autonomously, with humans as supervisors, strategists, and ethical stewards. This flips the traditional model: instead of humans using AI as a tool, intelligent agents will operate independently within constraints, managing everything from scheduling and customer service to complex workflows. Humans will guide and fine-tune these agents to ensure alignment with goals, values, and context. This shift offers major benefits -- greater efficiency, faster decisions, cost savings, and scalability. But it also brings risks: diminished human oversight, algorithmic bias, security flaws, and a widening skills gap. To navigate this transition, organizations must rethink roles, invest in upskilling, embed ethical principles, and promote transparency. This paper examines the technological and organizational changes needed to enable responsible adoption of AI-first systems -- where autonomy is balanced with human intent, oversight, and values.

Related papers

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)
A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy [36.74150011862134]
We show how human-AI teamwork can handle complex tasks better than AI working alone.<n>This paper argues that progress in AI should not be measured by how independent systems become, but by how well they can work with humans.
arXiv Detail & Related papers (2025-06-11T06:08:13Z)
Modeling AI-Human Collaboration as a Multi-Agent Adaptation [0.0]
We develop an agent-based simulation to formalize AI-human collaboration as a function of a task.<n>We show that in modular tasks, AI often substitutes for humans - delivering higher payoffs unless human expertise is very high.<n>We also show that even "hallucinatory" AI - lacking memory or structure - can improve outcomes when augmenting low-capability humans by helping escape local optima.
arXiv Detail & Related papers (2025-04-29T16:19:53Z)
Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We examine what is known about human wisdom and sketch a vision of its AI counterpart.<n>We argue that AI systems particularly struggle with metacognition.<n>We discuss how wise AI might be benchmarked, trained, and implemented.
arXiv Detail & Related papers (2024-11-04T18:10:10Z)
Explainable Human-AI Interaction: A Planning Perspective [32.477369282996385]
AI systems need to be explainable to the humans in the loop. We will discuss how the AI agent can use mental models to either conform to human expectations, or change those expectations through explanatory communication. While the main focus of the book is on cooperative scenarios, we will point out how the same mental models can be used for obfuscation and deception.
arXiv Detail & Related papers (2024-05-19T22:22:21Z)
Intent-aligned AI systems deplete human agency: the need for agency foundations research in AI safety [2.3572498744567127]
We argue that alignment to human intent is insufficient for safe AI systems. We argue that preservation of long-term agency of humans may be a more robust standard.
arXiv Detail & Related papers (2023-05-30T17:14:01Z)
Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z)
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)
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges [31.382000425295885]
Technologists and AI researchers have a responsibility to develop trustworthy AI systems. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness.
arXiv Detail & Related papers (2021-01-01T17:34:42Z)
Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles [95.58955174499371]
We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains. Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom.
arXiv Detail & Related papers (2020-08-07T21:00:13Z)
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)

This list is automatically generated from the titles and abstracts of the papers in this site.