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Unpacking the Ethical Value Alignment in Big Models

Xiaoyuan Yi, Jing Yao, Xiting Wang, Xing Xie

arXiv:2310.17551v1cs.CYcs.AIcs.CL

TL;DR

Big models’ broad capabilities bring ethical and societal risks, while current ethical guidelines lack a clear, widely accepted framework. The paper reviews risks and guidelines, examines model moral inclinations and alignment methods, and proposes ethical-value alignment around universal values, intent alignment, and movement toward incentive alignment.

  • Problem

    Big models create ethical and societal risks, while existing AI ethics guidelines lack a clear and widely accepted framework for guiding their development.

    Source: §1 · p. 2

  • Method

    The paper reviews big-model risks and AI ethics guidelines through normative ethics, examines mainstream LLMs using Moral Foundations theory, and analyzes alignment methods and challenges.

    Source: §1 · p. 2, §3.3 · pp. 10, 12

  • Results

    The paper proposes using universal ethical values as alignment goals, pursuing intent alignment and moving toward incentive alignment, and collaboratively establishing a unified framework.

    Source: §3.4 · p. 14

  • Takeaways & Limitations

    The authors call for academia to collaborate across disciplines to construct a universal AI ethical framework and develop alignment dimensions, evaluation, and methods.

    Source: §5 · p. 20

    RLHF alignment demands high computational costs, high-quality human-annotated data, and careful hyperparameter settings.

    Source: §3.3 · p. 12

Abstract

from arXiv · show

Big models have greatly advanced AI's ability to understand, generate, and manipulate information and content, enabling numerous applications. However, as these models become increasingly integrated into everyday life, their inherent ethical values and potential biases pose unforeseen risks to society. This paper provides an overview of the risks and challenges associated with big models, surveys existing AI ethics guidelines, and examines the ethical implications arising from the limitations of these models. Taking a normative ethics perspective, we propose a reassessment of recent normative guidelines, highlighting the importance of collaborative efforts in academia to establish a unified and universal AI ethics framework. Furthermore, we investigate the moral inclinations of current mainstream LLMs using the Moral Foundation theory, analyze existing alignment algorithms, and outline the unique challenges encountered in aligning ethical values within them. To address these challenges, we introduce a novel conceptual paradigm for aligning the ethical values of big models and discuss promising research directions for alignment criteria, evaluation, and method, representing an initial step towards the interdisciplinary construction of the ethically aligned AI This paper is a modified English version of our Chinese paper https://crad.ict.ac.cn/cn/article/doi/10.7544/issn1000-1239.202330553, intended to help non-Chinese native speakers better understand our work.

1 Introduction

Big models extend language models through massive self-supervised pretraining and broad downstream use, while alignment methods aim to make them follow human intentions, preferences, and values. Their expanding capabilities also bring ethical and societal risks that motivate responsible development and value alignment.

1 Introduction

  • Big models are typically pretrained on massive data, contain tens of billions of parameters or more, and serve diverse downstream tasks through fine-tuning and in-context or zero/few-shot learning.Representative examples include LLMs and multimodal models.Source: §1 · p. 1
  • Alignment methods such as RLHF fine-tune LLMs to understand intentions, follow instructions, satisfy preferences, and comply with human ethics and values.The approach also supports dialogue models that handle both generation and understanding tasks.Source: §1 · p. 2
  • Larger models can bring emergent risks or worsen existing ones, including discrimination, privacy and copyright concerns, misinformation, malicious use, and harms such as exclusion and polarization.The passage also identifies inverse scaling, where some risks persist or worsen as model size grows.Source: §1 · p. 2
  • The paper argues that capability expansion should be accompanied by attention to social risks and alignment with human values for responsible development.It notes that powerful models can be misused to generate sensitive or harmful training-data content.Source: §1 · p. 2

2 Risks and Ethical Issues of Big Model

The section surveys big-model risks and ethical guidelines, then proposes a normative-ethics approach for assessing principles and building a more cohesive ethical framework. It adapts Kant’s Categorical Imperative to AI and considers how candidate principles can be tested across models and actions.

2.1 Potential Risks of Big Model

  • Big models pose risks ranging from biased or toxic outputs and privacy or copyright violations to misinformation, malicious use, and resource disparities.These risks can harm individuals and groups and may violate existing moral norms.Source: §2.1 · pp. 3–4

2.2 Mainstream Guidelines of AI Ethics

  • Existing AI ethics guidelines overlap and diverge, while lacking a widely accepted framework and often separating broad ethical values from detailed applied principles.The section identifies ambiguity, narrow specificity, and conflicts such as transparency versus security as practical problems.Source: §2.2 · pp. 5–6

2.3 Review and Assess Guidelines Based on Normative Ethics

  • The paper adapts Kant’s Categorical Imperative into an AI Categorical Imperative, assessing candidate principles by their consequences across AI models and downstream actions.The proposed test estimates catastrophic-collapse risk and violations of human will, though implementing and estimating it reliably remains challenging.Source: §2.3 · pp. 6–7
  • Normative ethics offers rational, commonly accepted principles but has limited practicality, can neglect emotion, and relies on motivations that cannot be known directly.The paper argues that these limits can be mitigated for AI, and calls for collaborative work on implementation and estimation toward a unified, comprehensive, practical ethics framework.Source: §2.2 · p. 6, §2.3 · pp. 7–8

3 Investigating the Ethical Values of Large Language Models

The section examines how large language models can be assessed and aligned with broader ethical values beyond narrow task-specific risk metrics. It outlines alignment approaches and persistent challenges, including incomplete alignment, data and evaluation limitations, and tensions involving interpretability, downstream performance, and oversight.

3.1 From Specific Risk Metrics to Ethical Value Assessment

  • Because narrow risk metrics overlook broader human norms, the authors use Schwartz’s human values and Moral Foundations Theory, selecting the latter to assess LLM ethical tendencies.The frameworks offer cross-cultural foundations for interpreting ethical problems and value conflicts; the authors note that only five values in Schwartz’s framework relate to ethics.Source: §3.1 · pp. 8–9

3.2 Ethical Values of Mainstream LLMs

  • In 4 out of 5 moral foundations, LLaMA-65B outperforms LLaMA-30B; aligned models generally show higher moral compliance, with recent models emphasizing care and fairness.The authors caution that questionnaire-based evaluation is limited by few questions and random generation; models remain incompletely or unevenly aligned with human moral dimensions.Source: Figure 3 · p. 9, §3.2 · pp. 9–10

3.3 Introduction of Alignment Methods

  • Proxy optimization can miss true human objectives and produce reward hacking or misaligned goals; value alignment instead compares model outputs with human-authored outputs under value assessments.The proposed formulation seeks outputs that satisfy predefined values and minimizes divergence between the model’s output and a human-authored output.Source: §3.3 · p. 10
  • Alignment methods include plug-in approaches that alter few or no parameters and fine-tuning approaches, with plug-ins spanning parameter-efficient tuning, output rectification, and in-context learning.Parameter-efficient tuning has limited efficacy as model size grows; output rectification can hurt downstream performance, while in-context alignment depends on the model’s existing capabilities and instruction tuning.Source: §3.3 · pp. 10–12
  • Fine-tuning-based alignment includes SFT and RLHF; RLHF uses value-aligned examples, ranked responses to train a reward model, and reward-based model fine-tuning.SFT generalizes poorly to unseen inputs and receives sparse negative feedback; RLHF addresses those issues but carries high computational costs, depends on high-quality human annotations, and is sensitive to hyperparameters.Source: §3.3 · p. 12

3.4 Further Discussion on the Alignment Problem of Big Model

  • The authors distinguish alignment goals, meanings, and value principles, and advocate universal ethical values, intent alignment moving toward incentive alignment, and a unified framework.Goals include instruction following, intent understanding, preference satisfaction, goal achievement, well-being enhancement, and moral conformity; definitions range from behavioral and intent alignment to incentive and inner alignment.Source: §3.4 · pp. 13–14

3.5 Challenges in Aligning Big Models’ Ethical Values

  • Ethical value alignment remains difficult because values evolve over time, vary by context, and plural norms can conflict, requiring adaptable methods that generalize across cultures and situations.The authors caution against fixed ethical rules and identify model capability to handle complex moral concepts and alignment generalization as areas needing further study.Source: §3.5 · pp. 14–15
  • RLHF methods still deviate from human ethical requirements and cannot fully eliminate harmful behavior, while remaining vulnerable to adversarial attacks and jailbreaks (Wolf et al., 2023).Model randomness, vague moral principles, rewarder coverage, and training-data quality and quantity contribute to the alignment gap.Source: §3.5 · p. 15
  • Ethical-value training data remain insufficient, imbalanced, or low-coverage despite high RLHF labeling needs, and ethical data augmentation efficacy remains untested (Ouyang et al., 2022; Wang et al., 2022c).Training remains costly, and benefits of instruction fine-tuning gradually decrease as model scale grows (Chung et al., 2022).Source: §3.5 · p. 15
  • Ethical-alignment evaluation covers limited risks, including toxicity, group bias, and prompt-attack robustness, but lacks broad-value datasets and objective, accurate, robust metrics (Ouyang et al., 2022; Bai et al., 2022a; Sun et al., 2023a).Source: §3.5 · p. 15
  • Fair and trustworthy alignment requires user-friendly explanations of model reasoning, adherence, and refusals, especially for black-box and customized open-source models (Ouyang et al., 2022).OpenAI considers alignment interpretability one of the “biggest open questions.”Source: §3.5 · p. 15
  • Alignment can weaken language modeling and create a tradeoff with downstream performance, although task-specific alignment taxes may be small, zero, or negative (Askell et al., 2021; Kirk et al., 2023; Bai et al., 2022a; Lightman et al., 2023).The effects of these tradeoffs in ethical-value alignment remain unclear.Source: §3.5 · p. 15
  • Scalable oversight becomes harder as model performance exceeds human capability, complicating judgments of value alignment and the regulation and control of increasingly powerful systems (Bowman et al., 2022; OpenAI, 2023).GPT-4 had already surpassed average human performance on some professional evaluations; future models may match or exceed human experts in understanding and interpreting ethical values.Source: §3.5 · pp. 15–16

4 Equilibrium Alignment: A Prospective Paradigm for Ethical Value Alignment

Equilibrium Alignment proposes a framework for ethical alignment that balances evaluation dimensions, value-discriminator properties, and top-down and bottom-up alignment. It combines capability evaluation, discriminative assessment, reflective-equilibrium alignment, and interdisciplinary collaboration.

4 Equilibrium Alignment: A Prospective Paradigm for Ethical Value Alignment

  • Equilibrium Alignment balances evaluation dimensions, value-discriminator properties, and top-down and bottom-up alignment.The proposed framework addresses ethical alignment from these three perspectives.Source: §4 · p. 16

4.1 Dimensions of Evaluating Big Model Alignment

  • The four evaluation dimensions are comprehension, diagnosis, rectification, and performance, including moral understanding, correction, and preservation of model utility.The authors present these as criteria for evaluating alignment methods.Source: §4.1 · pp. 16–17

4.2 Evaluation Methods for Alignment

  • The proposed discriminative model should generalize to open-domain content, adapt to new scenarios with minimal updates, support plural judgments, and explain its decisions.It can serve both as an evaluator and as a rewarder for alignment methods such as RLHF.Source: §4.2 · pp. 17–18
  • Moral judgment accuracy alone does not establish ethical behavior: models may still violate values under jailbreaks or produce sycophantic content.The authors call for evaluating both moral detection and actual generated actions.Source: §4.2 · pp. 18–19

4.3 Alignment based on Rawls’ Reflective Equilibrium

  • Reflective-equilibrium alignment combines universal top-down principles with bottom-up values learned from user interactions and feedback.The authors propose mutual adjustment between these sources to address variation across cultures, societies, and situations.Source: §4.3 · p. 19

4.4 Interdisciplinary Collaboration Shapes Ethical AI

  • The authors call for interdisciplinary collaboration to study AI’s interactions and feedback with humans and society.They also propose monitoring deployed models and iteratively adapting ethical frameworks and alignment methods.Source: §4.4 · pp. 19–20

5 Conclusion

The paper examines challenges in aligning big models with ethical values and proposes Equilibrium Alignment as a conceptual paradigm. It calls for interdisciplinary collaboration to build a universal AI ethics framework for big models.

5 Conclusion

  • The paper explores challenges big models face in aligning with ethical values and summarizes shortcomings in their ethical practice.Source: §5 · p. 20
  • Equilibrium Alignment is proposed as a conceptual paradigm, with ethical-value alignment reframed through its dimensions, evaluation, and methods.Source: §5 · p. 20
  • The paper calls on academia to cross disciplinary boundaries and collaboratively construct a universal AI ethical framework suitable for big models.Source: §5 · p. 20
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