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Found 10 papers, 2 papers with code
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs).
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Traditional recommender systems (RS) have used user-item rating histories as their primary data source, with collaborative filtering being one of the principal methods.
Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation
Data annotation remains the sine qua non of machine learning and AI.
Two Types of AI Existential Risk: Decisive and Accumulative
This involves a gradual accumulation of critical AI-induced threats such as severe vulnerabilities and systemic erosion of econopolitical structures.
ChatGPT, Large Language Technologies, and the Bumpy Road of Benefiting Humanity
The allure of emerging AI technologies is undoubtedly thrilling.
In conversation with Artificial Intelligence: aligning language models with human values
Furthermore, we explore how these norms can be used to align conversational agents with human values across a range of different discursive domains.
Ethical and social risks of harm from Language Models
We discuss the points of origin of different risks and point to potential mitigation approaches.
User Tampering in Reinforcement Learning Recommender Systems
In this paper, we introduce new formal methods and provide empirical evidence to highlight a unique safety concern prevalent in reinforcement learning (RL)-based recommendation algorithms -- 'user tampering.'
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence
The societal and ethical implications of the use of opaque artificial intelligence systems for consequential decisions, such as welfare allocation and criminal justice, have generated a lively debate among multiple stakeholder groups, including computer scientists, ethicists, social scientists, policy makers, and end users.
Mathematical decisions and non-causal elements of explainable AI
In particular, I offer a multi-faceted conceptual framework for the explanations and the interpretations of algorithmic decisions, and I claim that this framework can lay the groundwork for a focused discussion among multiple stakeholders about the social implications of algorithmic decision-making, as well as AI governance and ethics more generally.
