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

Source: arXiv - 2511.21570v1

Overview

Maria Perez‑Ortiz’s paper introduces “responsible computational foresight,” a framework that blends human‑centric AI with scenario‑building tools to help policymakers anticipate and shape sustainable futures. By positioning AI as a support rather than a replacement for human judgment, the work charts a path for more ethical, transparent, and resilient decision‑making in the face of complex global challenges.

Key Contributions

• Terminology & Conceptual Grounding: Coins “responsible computational foresight” and defines its core principles (human‑centeredness, transparency, accountability, sustainability).
• Principle‑Based Blueprint: Proposes a set of guiding principles that align AI‑driven foresight with ethical policy design.
• Tool‑Kit Survey: Catalogues existing AI‑enabled foresight tools (e.g., generative scenario generators, uncertainty quantification modules, multi‑agent simulations) and maps them to the proposed principles.
• Human‑AI Interaction Model: Outlines a workflow where AI augments, but never overrides, expert judgment throughout the foresight cycle.
• Roadmap for Integration: Offers actionable recommendations for governments, NGOs, and tech firms to embed responsible foresight into their strategic pipelines.

Methodology

1. Literature Synthesis: Reviews interdisciplinary work spanning AI ethics, computational modeling, and strategic foresight to identify gaps.
2. Principle Derivation: Uses normative analysis to distill five foundational principles from ethical AI guidelines and foresight best practices.
3. Tool Mapping: Conducts a systematic audit of 12 publicly available AI‑driven foresight platforms (e.g., climate scenario simulators, policy impact predictors), evaluating each against the principles.
4. Human‑AI Workflow Design: Sketches a step‑by‑step process—framing → data gathering → model generation → scenario exploration → deliberation → decision—highlighting where AI adds value (e.g., rapid scenario generation) and where human expertise is indispensable (e.g., value judgments).
5. Case Illustrations: Presents two brief case studies (climate‑resilient urban planning and pandemic policy response) that demonstrate the workflow in practice.

Results & Findings

• AI Boosts Scenario Breadth: Generative models can produce 10‑100× more plausible futures than manual brainstorming, expanding the decision space without sacrificing quality.
• Transparency Gaps Remain: Many tools lack explainability features, making it hard for policymakers to trace why a particular scenario was highlighted.
• Human Judgment Still Central: In both case studies, the most impactful decisions stemmed from expert deliberation on AI‑generated insights, confirming the “support‑only” role of AI.
• Principle Alignment Varies: Only 3 of the 12 surveyed tools satisfied all five responsible‑foresight principles; the rest fell short mainly on accountability and stakeholder inclusivity.

Practical Implications

• Policy Labs & GovTech: Agencies can adopt the proposed workflow to embed AI‑assisted scenario analysis into existing policy‑design cycles, improving speed and breadth of insight while retaining democratic oversight.
• Tool Development: Vendors are nudged to embed explainability dashboards, bias‑mitigation layers, and stakeholder‑feedback loops to meet the responsible‑foresight criteria.
• Risk Management: Companies can use the framework to anticipate regulatory or societal shifts (e.g., ESG trends) and proactively adjust product roadmaps.
• Education & Training: Curriculum designers can incorporate the human‑AI foresight loop into data‑science and public‑policy programs, preparing the next generation of “foresight engineers.”

Limitations & Future Work

• Empirical Validation Needed: The paper’s case studies are illustrative; large‑scale field trials are required to quantify impact on policy outcomes.
• Tool Coverage: The audit focuses on publicly documented platforms; many proprietary systems used by governments remain unexamined.
• Dynamic Adaptation: Future research should explore how the workflow copes with real‑time data streams and rapid‑changing crises (e.g., cyber‑attacks).
• Cross‑Cultural Ethics: Extending the principle set to accommodate diverse cultural norms and governance structures is an open challenge.

Authors

• Maria Perez‑Ortiz

Paper Information

• arXiv ID: 2511.21570v1
• Categories: cs.AI, cs.CY, cs.HC
• Published: November 26, 2025
• PDF: Download PDF