From Prediction to Foresight: The Role of AI in Designing Responsible Futures

• URL: http://arxiv.org/abs/2511.21570v1
• Date: Wed, 26 Nov 2025 16:42:10 GMT
• Title: From Prediction to Foresight: The Role of AI in Designing Responsible Futures
• Authors: Maria Perez-Ortiz,
• Abstract summary: We argue that AI will play a role as a supportive tool in responsible, human-centered foresight.<n>This paper advocates for the thoughtful integration of AI into foresight practices.
• Score: 0.2842794675894731
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In an era marked by rapid technological advancements and complex global challenges, responsible foresight has emerged as an essential framework for policymakers aiming to navigate future uncertainties and shape the future. Responsible foresight entails the ethical anticipation of emerging opportunities and risks, with a focus on fostering proactive, sustainable, and accountable future design. This paper coins the term "responsible computational foresight", examining the role of human-centric artificial intelligence and computational modeling in advancing responsible foresight, establishing a set of foundational principles for this new field and presenting a suite of AI-driven foresight tools currently shaping it. AI, particularly in conjunction with simulations and scenario analysis, enhances policymakers' ability to address uncertainty, evaluate risks, and devise strategies geared toward sustainable, resilient futures. However, responsible foresight extends beyond mere technical forecasting; it demands a nuanced understanding of the interdependencies within social, environmental, economic and political systems, alongside a commitment to ethical, long-term decision-making that supports human intelligence. We argue that AI will play a role as a supportive tool in responsible, human-centered foresight, complementing rather than substituting policymaker judgment to enable the proactive shaping of resilient and ethically sound futures. This paper advocates for the thoughtful integration of AI into foresight practices to empower policymakers and communities as they confront the grand challenges of the 21st century.

Related papers

• The AI Policy Module: Developing Computer Science Student Competency in AI Ethics and Policy [1.724936122482754]
  The prevailing post-secondary computing curriculum is ill-equipped to prepare future AI practitioners.<n>We develop an AI Policy Module to introduce discussions of AI policy into the computer science curriculum.<n>We present the findings from our pilot of the AI Policy Module 2.0, evaluating student attitudes towards AI ethics and policy.
  arXiv  Detail & Related papers  (2025-06-18T17:09:58Z)
• Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2) [49.142289900583705]
  We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability.<n>We discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI.<n>Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI.
  arXiv  Detail & Related papers  (2025-05-22T12:52:34Z)
• Mitigating Societal Cognitive Overload in the Age of AI: Challenges and Directions [0.9906787204170321]
  Societal cognitive overload, driven by the deluge of information and complexity in the AI age, poses a critical challenge to human well-being and societal resilience.<n>This paper argues that mitigating cognitive overload is not only essential for improving present-day life but also a crucial prerequisite for navigating the potential risks of advanced AI.
  arXiv  Detail & Related papers  (2025-04-28T17:06:30Z)
• Agentic AI: Autonomy, Accountability, and the Algorithmic Society [0.2209921757303168]
  Agentic Artificial Intelligence (AI) can autonomously pursue long-term goals, make decisions, and execute complex, multi-turn.<n>This transition from advisory roles to proactive execution challenges established legal, economic, and creative frameworks.<n>We explore challenges in three interrelated domains: creativity and intellectual property, legal and ethical considerations, and competitive effects.
  arXiv  Detail & Related papers  (2025-02-01T03:14:59Z)
• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• AI in ESG for Financial Institutions: An Industrial Survey [4.893954917947095]
  The paper surveys the industrial landscape to delineate the necessity and impact of AI in bolstering ESG frameworks. Our survey categorizes AI applications across three main pillars of ESG, illustrating how AI enhances analytical capabilities, risk assessment, customer engagement, reporting accuracy and more. The paper also addresses the imperative of responsible and sustainable AI, emphasizing the ethical dimensions of AI deployment in ESG-related banking processes.
  arXiv  Detail & Related papers  (2024-02-03T02:14:47Z)
• Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making [69.44075077934914]
  "Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
  arXiv  Detail & Related papers  (2024-01-13T14:07:09Z)
• Survey on AI Ethics: A Socio-technical Perspective [7.656863418949436]
  This work unifies the current and future ethical concerns of deploying AI into society.<n>Ethical concerns associated with AI are multifaceted, including challenging issues of fairness, privacy and data protection, responsibility and accountability, safety and robustness, transparency and explainability, and environmental impact.
  arXiv  Detail & Related papers  (2023-11-28T21:00:56Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• Predictable Artificial Intelligence [77.1127726638209]
  This paper introduces the ideas and challenges of Predictable AI.<n>It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems.<n>We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)
• 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)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.