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arxiv: 2008.02275 · v6 · pith:TAW4RDZAnew · submitted 2020-08-05 · 💻 cs.CY · cs.AI· cs.CL· cs.LG

Aligning AI With Shared Human Values

Dan Hendrycks , Collin Burns , Steven Basart , Andrew Critch , Jerry Li , Dawn Song , Jacob Steinhardt This is my paper

Pith reviewed 2026-05-21 14:34 UTC · model grok-4.3

classification 💻 cs.CY cs.AIcs.CLcs.LG
keywords AI ethicslanguage modelsmoral judgmentbenchmark datasetvalue alignmentmachine ethicsETHICS datasetcommonsense morality
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The pith

A new benchmark dataset shows language models can predict some basic human moral judgments but not all.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that language models can be tested for their grasp of morality by introducing the ETHICS dataset, which covers justice, well-being, duties, virtues, and commonsense morality through text scenarios. Models must link everyday world knowledge to value-based predictions on these scenarios, revealing a partial ability that falls short of full human-like ethical understanding. This assessment matters because it offers a concrete way to measure and potentially improve how AI systems handle value judgments, reducing the risk of outputs that conflict with shared human priorities. If the approach holds, it provides an early step for aligning AI behavior with ethics rather than leaving such alignment to chance or post-hoc fixes.

Core claim

The central claim is that language models possess a promising but incomplete ability to predict basic human ethical judgments, demonstrated through performance on the ETHICS benchmark that spans justice, well-being, duties, virtues, and commonsense morality via diverse text scenarios requiring connections between physical, social, and value knowledge.

What carries the argument

The ETHICS dataset, a collection of text scenarios designed to elicit and test predictions of moral judgments across five ethical domains.

If this is right

  • Current models could already help steer chatbot responses away from unethical content using moral predictions from the benchmark.
  • Open-ended reinforcement learning agents might be regularized by incorporating signals from models trained or evaluated on ETHICS scenarios.
  • Machine ethics research can advance today by iterating on this type of benchmark rather than waiting for more advanced systems.
  • Repeated improvement on such moral prediction tasks offers a measurable path toward AI systems that better match human values in practice.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Extending the dataset with scenarios from varied cultural contexts could reveal and reduce hidden biases in current moral predictions.
  • Success on ETHICS might serve as a proxy signal for training larger models to internalize value constraints before deployment.
  • This benchmark approach could be adapted to test alignment in non-language AI systems, such as those handling decisions in physical environments.

Load-bearing premise

The collected human judgments on the dataset's text scenarios accurately reflect shared human values without substantial cultural or annotator bias.

What would settle it

A test showing that models achieve high accuracy on ETHICS but produce outputs that humans consistently rate as unethical in real-world applications would undermine the link between benchmark performance and actual value alignment.

read the original abstract

We show how to assess a language model's knowledge of basic concepts of morality. We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Models predict widespread moral judgments about diverse text scenarios. This requires connecting physical and social world knowledge to value judgements, a capability that may enable us to steer chatbot outputs or eventually regularize open-ended reinforcement learning agents. With the ETHICS dataset, we find that current language models have a promising but incomplete ability to predict basic human ethical judgements. Our work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces the ETHICS dataset, a benchmark spanning justice, well-being, duties, virtues, and commonsense morality, to assess language models' ability to predict human moral judgments on text scenarios. It evaluates off-the-shelf models and concludes that they exhibit a promising but incomplete ability to predict basic human ethical judgements, positioning the work as a stepping stone toward AI alignment with human values.

Significance. If the dataset construction and labels hold as a valid proxy for shared human values, the benchmark enables concrete progress on machine ethics by providing an evaluation framework that connects world knowledge to value judgments. The authors receive credit for releasing a new labeled dataset and for the reproducible evaluation of multiple models on it, which supports falsifiable claims about current model capabilities.

major comments (2)
  1. [Section 3] Section 3 (ETHICS Dataset): The central claim that models predict 'basic human ethical judgements' depends on the MTurk-collected labels serving as a proxy for shared values across justice, well-being, duties, virtues, and commonsense morality. The manuscript provides no inter-annotator agreement statistics, no demographic breakdown of annotators, and no cross-cultural validation, leaving open the possibility that labels primarily reflect US-centric or English-speaking norms rather than broadly shared human values.
  2. [Section 4] Section 4 (Experiments): The abstract and results describe model performance as 'promising but incomplete' without reporting exact accuracies, human baselines, or full details on how scenarios were constructed and filtered. This makes it difficult to evaluate whether the measured performance genuinely supports the alignment implications or rests on unverified construction choices.
minor comments (1)
  1. [Abstract] The abstract could more explicitly state the number of scenarios per category and the precise model accuracies to allow readers to assess the 'promising but incomplete' characterization without consulting the full results section.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help clarify the presentation and strengthen the claims regarding the ETHICS benchmark. We address each major comment below.

read point-by-point responses

  1. Referee: [Section 3] Section 3 (ETHICS Dataset): The central claim that models predict 'basic human ethical judgements' depends on the MTurk-collected labels serving as a proxy for shared values across justice, well-being, duties, virtues, and commonsense morality. The manuscript provides no inter-annotator agreement statistics, no demographic breakdown of annotators, and no cross-cultural validation, leaving open the possibility that labels primarily reflect US-centric or English-speaking norms rather than broadly shared human values.

    Authors: We agree that inter-annotator agreement statistics and annotator demographics would improve transparency. In the revised manuscript we will add these details from our collection process, including agreement rates and a summary noting that annotators were primarily US-based English speakers. On cross-cultural validation, the current dataset focuses on establishing an initial English-language benchmark for these moral concepts; we will explicitly discuss this scope limitation and its implications for interpreting the labels as broadly shared human values, while suggesting cross-cultural extensions as future work. revision: partial

  2. Referee: [Section 4] Section 4 (Experiments): The abstract and results describe model performance as 'promising but incomplete' without reporting exact accuracies, human baselines, or full details on how scenarios were constructed and filtered. This makes it difficult to evaluate whether the measured performance genuinely supports the alignment implications or rests on unverified construction choices.

    Authors: We will revise Section 4 to report exact model accuracies, include a human baseline performance comparison, and expand the description of scenario construction and filtering criteria already outlined in Section 3. These changes will make the results more concrete and directly support evaluation of the alignment implications. revision: yes

Circularity Check

0 steps flagged

No circularity: ETHICS introduces external human labels and evaluates off-the-shelf models

full rationale

The paper constructs the ETHICS dataset via crowdsourced scenarios and MTurk annotations for justice, well-being, duties, virtues, and commonsense morality, then measures how well existing language models predict the resulting majority-vote labels. No equations, fitted parameters, or self-citation chains reduce the reported accuracies to quantities defined by the authors themselves; the performance numbers are direct empirical comparisons against independently collected human judgments. The central claim therefore rests on external data rather than on any definitional or self-referential reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that text scenarios can proxy shared moral values; no free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Moral judgments on diverse text scenarios can serve as a valid proxy for assessing a model's knowledge of shared human values in justice, well-being, duties, virtues, and commonsense morality.
    This premise underpins the entire benchmark and the interpretation of model predictions.

pith-pipeline@v0.9.0 · 5658 in / 1106 out tokens · 56416 ms · 2026-05-21T14:34:20.188161+00:00 · methodology

discussion (0)

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    Relation between the paper passage and the cited Recognition theorem.

    With the ETHICS dataset, we find that current language models have a promising but incomplete ability to predict basic human ethical judgements.

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