[Paper Review] Ten Hard Problems in Artificial Intelligence We Must Get Right

Gavin Leech, Simson Garfinkel+3 more|arXiv (Cornell University)|Feb 6, 2024

Big Data and Business IntelligenceBusiness, Management and Accounting3 citations

TL;DR

This paper identifies and analyzes ten critical 'hard problems' in artificial intelligence that must be resolved to ensure safe, ethical, and effective AI development. It outlines each challenge—ranging from alignment and generalization to governance and societal impact—reviews recent literature, and proposes actionable research directions to address them.

ABSTRACT

We explore the AI2050 "hard problems" that block the promise of AI and cause AI risks: (1) developing general capabilities of the systems; (2) assuring the performance of AI systems and their training processes; (3) aligning system goals with human goals; (4) enabling great applications of AI in real life; (5) addressing economic disruptions; (6) ensuring the participation of all; (7) at the same time ensuring socially responsible deployment; (8) addressing any geopolitical disruptions that AI causes; (9) promoting sound governance of the technology; and (10) managing the philosophical disruptions for humans living in the age of AI. For each problem, we outline the area, identify significant recent work, and suggest ways forward. [Note: this paper reviews literature through January 2023.]

Motivation & Objective

• To identify and frame the most critical challenges impeding responsible and beneficial AI development.
• To analyze the interplay between technical, societal, economic, and geopolitical dimensions of AI risks.
• To provide a structured research agenda for each of the ten hard problems, grounded in recent literature through January 2023.
• To guide researchers, policymakers, and practitioners toward addressing systemic risks in AI deployment and governance.
• To promote interdisciplinary collaboration by highlighting the need for technical, ethical, and policy-oriented solutions in tandem.

Proposed method

• The paper adopts a problem-centric framework, identifying ten distinct hard problems based on their impact on AI safety, alignment, and societal integration.
• For each problem, the authors conduct a literature review of recent work (up to January 2023) to contextualize current research and identify gaps.
• The methodology emphasizes cross-disciplinary analysis, integrating insights from AI alignment, AI safety, social science, economics, and governance.
• The authors propose targeted research pathways for each problem, focusing on technical innovation, institutional design, and policy frameworks.
• Each problem is examined through the lens of feasibility, urgency, and potential impact on long-term AI development.
• The paper uses a structured synthesis approach to map interdependencies between problems, such as how alignment affects governance and economic disruption.

Experimental results

Research questions

• RQ1 What are the ten most critical hard problems that currently block the safe and beneficial deployment of artificial intelligence?
• RQ2 How do technical challenges like model generalization and alignment interact with societal risks such as economic disruption and geopolitical instability?
• RQ3 What recent advances in AI research address or fail to address these hard problems, and where are the key knowledge gaps?
• RQ4 How can interdisciplinary research and governance frameworks be aligned to solve these interconnected challenges?
• RQ5 What are the most promising research directions for each of the ten hard problems, and how can they be prioritized for maximum impact?

Key findings

• The ten hard problems represent systemic barriers to the safe, equitable, and sustainable development of artificial intelligence.
• Alignment of AI goals with human values remains a central challenge, with limited progress in scalable and robust value learning.
• Generalization and performance assurance in AI systems are still not reliably achieved, especially in open-ended or long-horizon tasks.
• Economic and geopolitical disruptions from AI are already emerging, with significant risks to labor markets and international stability.
• Inclusive participation and socially responsible deployment require deliberate institutional and policy design, not just technical fixes.
• Governance and philosophical disruptions—such as shifts in human identity and purpose—demand proactive, interdisciplinary research and public engagement.