Modeling Pluralistic Human Values, Rights, and Duties for Ethical AI

核心概念

Pluralistic human values, rights, and duties are crucial for ethical AI decision-making, but current AI systems tend to wash out this nuance. This work introduces a large-scale dataset and model to explicitly represent and reason about value pluralism.

要約

The content discusses the importance of modeling pluralistic human values, rights, and duties for ethical AI decision-making. It introduces two key contributions:

1. VALUEPRISM dataset: A large-scale dataset of 218k values, rights, and duties connected to 31k human-written situations. The dataset is validated to be high-quality, with 91% of the data deemed accurate by human annotators.

2. KALEIDO model: A language-based multi-task system that generates, explains, and assesses the relevance and valence (support or oppose) of pluralistic human values, rights, and duties within a given context. KALEIDO outperforms the teacher model (GPT-4) in generating relevant and accurate value sets, and can help explain variability in human decision-making.

The work also demonstrates that KALEIDO's representations transfer to other philosophical frameworks and datasets, confirming the benefit of an explicit, modular, and interpretable approach to value pluralism. The authors hope this will serve as a step towards making the implicit values behind human decision-making more explicit, and steering AI systems to make decisions more aligned with them.

統計

"Human values are crucial to human decision-making."
"Two people in the same situation may make opposing decisions if they value different things or the same things but to varying extents."
"GPT-4 and its like have been shown to match human crowdworker annotation performance in some domains."
"91% of the distilled data in VALUEPRISM is deemed high quality by human annotators."
"Humans rate the outputs of the largest KALEIDO model as more correct and complete than the teacher's (GPT-4)."

引用

"Value pluralism is the view that multiple correct values may be held in tension with one another (e.g., when considering lying to a friend to protect their feelings, how does one balance honesty with friendship?)."
"As statistical learners, AI systems fit to averages by default, washing out these potentially irreducible value conflicts."
"We hope that our work will serve as a step to making more explicit the implicit values behind human decision-making and to steering AI systems to make decisions that are more in accordance with them."

抽出されたキーインサイト

Value Kaleidoscope

by Taylor Soren... 場所 arxiv.org 04-03-2024

https://arxiv.org/pdf/2309.00779.pdf

深掘り質問

How can the VALUEPRISM dataset and KALEIDO model be further expanded and refined to better capture the diversity of human values, rights, and duties across different cultures and demographics?

To enhance the representation of diverse human values, rights, and duties across various cultures and demographics, the VALUEPRISM dataset and KALEIDO model can be expanded and refined in several ways:

Inclusion of Culturally Specific Values: Collaborate with experts from different cultural backgrounds to identify and incorporate culturally specific values, rights, and duties into the dataset. This can help ensure a more comprehensive representation of values across diverse populations.

Demographic Diversity: Collect data from a more diverse set of annotators representing various demographics to ensure that the dataset captures a wide range of perspectives. This can help in addressing biases and ensuring inclusivity in the dataset.

Multilingual Support: Expand the dataset to include values, rights, and duties in multiple languages to cater to a global audience. This can help in capturing cultural nuances and variations in values across different linguistic communities.

Fine-Grained Annotations: Provide detailed annotations for each value, right, and duty to capture the subtle differences in interpretation and application across cultures. This can help in refining the model's understanding of context-specific values.

Continuous Iteration and Validation: Regularly update and validate the dataset with feedback from experts and diverse stakeholders to ensure that it remains relevant and reflective of evolving societal values and norms.

Cross-Cultural Validation: Conduct cross-cultural studies to compare how values, rights, and duties are perceived and prioritized in different cultural contexts. This can help in identifying commonalities and differences in value systems.

By incorporating these strategies, the VALUEPRISM dataset and KALEIDO model can be expanded and refined to better capture the diversity of human values, rights, and duties across different cultures and demographics.

How can the insights from value pluralism be applied to other areas of AI development, such as multi-stakeholder decision-making, policy design, or the governance of AI systems?

The insights from value pluralism can be valuable in various areas of AI development to promote ethical decision-making, inclusive policy design, and responsible governance of AI systems:

Multi-Stakeholder Decision-Making: Incorporating value pluralism can help AI systems consider a diverse range of values held by different stakeholders. By recognizing and balancing conflicting values, AI systems can make decisions that align with the collective interests of various stakeholders, leading to more equitable outcomes.

Policy Design: Value pluralism can inform the design of AI policies that account for the diverse ethical perspectives and values present in society. Policies can be crafted to accommodate different value systems, ensuring that AI technologies adhere to ethical standards that reflect the values of the communities they serve.

Ethical AI Development: By integrating value pluralism into the development process, AI systems can be designed to respect and uphold a broad spectrum of human values. This approach can help mitigate biases, promote fairness, and enhance transparency in AI decision-making processes.

Governance of AI Systems: Value pluralism can guide the governance of AI systems by emphasizing the importance of ethical considerations and value alignment. Regulatory frameworks can be designed to ensure that AI technologies operate in accordance with diverse human values, rights, and duties, fostering trust and accountability.

Ethical Impact Assessments: Incorporating value pluralism in ethical impact assessments can help evaluate the potential implications of AI systems on different value systems and societal norms. This can enable stakeholders to anticipate and address ethical challenges proactively.

By applying the principles of value pluralism in AI development, decision-making, policy design, and governance, stakeholders can foster a more ethical, inclusive, and human-centric approach to the advancement of AI technologies.

What are the potential risks and limitations of using large language models to generate and reason about pluralistic values, and how can these be mitigated?

Using large language models (LLMs) to generate and reason about pluralistic values comes with several risks and limitations that need to be addressed to ensure ethical and responsible AI development:

Bias and Representation: LLMs may perpetuate biases present in the training data, leading to underrepresentation or misrepresentation of certain values, rights, and duties. Mitigation involves diverse dataset curation, bias detection, and mitigation techniques to ensure fair and inclusive representation.

Interpretability and Transparency: LLMs' decision-making processes are often opaque, making it challenging to understand how values are generated and reasoned about. Enhancing model interpretability through explainable AI techniques can help increase transparency and accountability.

Context Sensitivity: LLMs may struggle to capture the nuanced context in which values operate, leading to misinterpretations or oversimplifications. Fine-tuning models on context-specific data and incorporating contextual cues can help improve the accuracy of value reasoning.

Ethical Considerations: LLMs may generate values that conflict with ethical norms or societal standards, posing ethical dilemmas. Ethical guidelines, oversight mechanisms, and robust ethical review processes can help address these concerns and ensure ethical AI development.

Generalization to Diverse Cultures: LLMs trained on specific datasets may not generalize well to diverse cultural contexts, resulting in cultural biases. Incorporating diverse cultural perspectives in training data and evaluation can help mitigate these biases and improve cross-cultural applicability.

Human-in-the-Loop Validation: Human validation and oversight are crucial to ensure that LLM-generated values align with human values and ethical principles. Continuous human-in-the-loop validation can help identify and rectify potential errors or biases in the generated values.

By addressing these risks and limitations through proactive measures such as bias mitigation, interpretability enhancement, context sensitivity, ethical considerations, cultural diversity, and human validation, the use of LLMs to generate and reason about pluralistic values can be more responsible and aligned with ethical standards.