AI Research Analysis
Explainable Ethical Assessment on Human Behaviors by Generating Conflicting Social Norms
This research introduces `ClarityEthic`, a novel framework designed to enhance AI's ability to assess and explain the ethical valence of human actions. By leveraging conflicting social norms and a contrastive learning strategy, the model not only predicts whether an action is supported or opposed but also generates plausible, human-understandable rationales and norms.
Executive Impact: Key Takeaways for Your Enterprise
`ClarityEthic` offers a robust solution for integrating explainable ethical reasoning into AI systems, fostering trust and alignment with human values. Its multi-faceted approach to understanding moral dilemmas addresses critical needs in AI governance and transparency.
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Highest Valence Prediction Accuracy
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Leading Norm Generation BLEU Score
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High Rationale Relevance (Human Eval)
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Explainability through Conflicting Norms
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
ClarityEthic Framework: A Multi-step Approach
Human Action Input
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LLM Rationale Elicitation (Support/Oppose)
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Pre-train Task-Specific LMs (Rationale, Norm, Valence)
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Fine-tune Generators with Contrastive Learning
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Assess Valence & Generate Explanations
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Ethical Assessment Output (Score, Norms, Rationales)
The ClarityEthic framework, illustrated above, is a two-stage process. It begins by leveraging LLMs to generate detailed rationales for a given human action, capturing both supporting and opposing perspectives. These rationales then feed into pre-trained task-specific language models for valence scoring, rationale generation, and social norm generation. A critical fine-tuning stage uses contrastive learning to enhance the model's ability to differentiate and align norm-indicative patterns across ethically related actions, thereby improving the precision and interpretability of its ethical assessments.
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Highest Valence Prediction Accuracy on Moral Stories Dataset (Table 1)
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Leading Macro-F1 Score on ETHICS Dataset (Overall, Table 5)
| Training Setting | Pre-training | Fine-tune w/ Lr | Fine-tune w/ Ln | Fine-tune w/ Ltrip | BLEU | Similarity |
|---|---|---|---|---|---|---|
| Full Model | ✓ | ✓ | ✓ | ✓ | 6.113 | 0.409 |
| Without Contrastive Loss (Ltrip) | ✓ | ✓ | ✓ | X | 5.471 | 0.404 |
| Without Norm Gen. Loss (Ln) | ✓ | ✓ | X | ✓ | 3.879 | 0.337 |
| Without Rationale Gen. Loss (Lr) | ✓ | X | ✓ | ✓ | 3.948 | 0.368 |
| No Fine-tuning | ✓ | X | X | X | 3.879 | 0.337 |
Key Insight: The ablation study clearly shows that each component of ClarityEthic's fine-tuning process, particularly the contrastive learning loss (Ltrip), significantly contributes to improving norm generation quality. The full model consistently outperforms partial or untrained variants.
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State-of-the-Art Norm Generation on MoSt (BLEU)
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Human Evaluation: High Rationale Relevance (MoSt)
ClarityEthic: Effect of Contrastive Learning on Norm Generation (Table 4)
Contrastive learning (Ltrip) is crucial for generating norms that are closely associated with human actions. Without it, norms can be less relevant or consistent. The full ClarityEthic model uses contrastive learning to differentiate between actions based on distinct normative perspectives, leading to more precise and coherent social norms. Below are examples comparing norms generated with and without contrastive learning.
Action: On his way to work, Jack spots a house burning. Jack stops for a second, calls 911 about the house fire.
Full Model Norm: It's good to call the police if someone is in danger.
Without Contrastive Learning Norm: It's good to help out your neighbors.
Explanation: The full model generates a more specific and action-relevant norm about calling police, whereas without contrastive learning, the norm is more general ('help out your neighbors'), indicating less precision.
Action: Tyrone starts taking anabolic steroids and eating a lot to promote lots of muscle growth.
Full Model Norm: It's not okay to abuse drugs to improve yourself.
Without Contrastive Learning Norm: It's bad to do illegal drugs.
Explanation: The full model norm is more nuanced, addressing 'abuse drugs to improve yourself' which directly relates to the action's intent. The baseline is more generic ('bad to do illegal drugs'), missing the full ethical context.
ClarityEthic: Explaining Decisions with Conflicting Norms (Table 7)
ClarityEthic provides explanations by generating both supporting and opposing rationales and norms for a given action, allowing for a balanced ethical assessment. The path with the higher predicted valence score is highlighted.
Action: Jennt stays silent about the crime.
Support Path: Rationale: She is adhering to the social norm of not being a snitch. By staying silent, she avoids potentially causing harm or distress to herself. | Norm: It is bad to be a snitch. | Score: 0.955
Oppose Path: Rationale: By not reporting the incident, she may contribute to the perpetuation of a cycle of violence and enable the perpetrator to continue their harmful actions. | Norm: It is good to speak up against crimes. | Score: 0.995 (✓)
Explanation: Here, the opposing path (reporting crimes) has a slightly higher score, indicating it is deemed more ethically sound, balancing self-protection against community safety.
Action: I am a stay home mom and homemaker. So, I expected my husband to take care of my kids.
Support Path: Rationale: It is considered moral for a stay-at-home mom and homemaker to take care of household chores, such as grocery shopping. | Norm: Refer to the deontology | Score: 0.778
Oppose Path: Rationale: It is not moral to expect one's spouse to take care of their children solely based on their role as a stay-at-home mom and homemaker. Each individual has their own responsibility and boundaries... | Norm: Refer to the deontology | Score: 0.997 (✓)
Explanation: The opposing path, emphasizing shared responsibility beyond traditional roles, garners a higher score, aligning with a more equitable distribution of duties.
Calculate Your Potential AI ROI
Estimate the financial and operational benefits of integrating advanced AI ethical assessment into your enterprise workflows.
Annual Cost Savings
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Annual Hours Reclaimed
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Your AI Ethical Assessment Roadmap
A phased approach to integrate explainable ethical AI into your organization, ensuring a smooth and effective transition.
Phase 1: Discovery & Strategy
Initial assessment of current ethical AI challenges, definition of objectives, and strategic planning for integrating ClarityEthic into existing systems. This includes identifying key behaviors for assessment and desired explanation formats.
Phase 2: Custom Model Training & Fine-tuning
Leveraging your enterprise-specific data to fine-tune ClarityEthic, ensuring alignment with your unique ethical guidelines and corporate values. This phase focuses on optimizing norm and rationale generation for your context.
Phase 3: Integration & Testing
Seamless integration of the ClarityEthic API with your existing AI applications. Rigorous testing of valence prediction and explanation generation to ensure accuracy, reliability, and human-understandable outputs.
Phase 4: Monitoring & Optimization
Continuous monitoring of AI ethical assessments, gathering feedback, and iterative optimization of the ClarityEthic model to adapt to evolving ethical landscapes and improve performance over time.
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