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Found 13 papers, 1 papers with code
Accounting for Offensive Speech as a Practice of Resistance
Tasks such as toxicity detection, hate speech detection, and online harassment detection have been developed for identifying interactions involving offensive speech.
Sociotechnical Safety Evaluation of Generative AI Systems
First, we propose a three-layered framework that takes a structured, sociotechnical approach to evaluating these risks.
Model evaluation for extreme risks
Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities.
Manifestations of Xenophobia in AI Systems
Xenophobia is one of the key drivers of marginalisation, discrimination, and conflict, yet many prominent machine learning (ML) fairness frameworks fail to comprehensively measure or mitigate the resulting xenophobic harms.
A Human Rights-Based Approach to Responsible AI
Research on fairness, accountability, transparency and ethics of AI-based interventions in society has gained much-needed momentum in recent years.
Improving alignment of dialogue agents via targeted human judgements
We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines.
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.
Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models
Large language models produce human-like text that drive a growing number of applications.
Ethical and social risks of harm from Language Models
We discuss the points of origin of different risks and point to potential mitigation approaches.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Ranked #1 on
Language Modelling
on StackExchange
Alignment of Language Agents
For artificial intelligence to be beneficial to humans the behaviour of AI agents needs to be aligned with what humans want.
Modelling Cooperation in Network Games with Spatio-Temporal Complexity
Such systems have local incentives for individuals, whose behavior has an impact on the global outcome for the group.
The Challenge of Value Alignment: from Fairer Algorithms to AI Safety
This paper addresses the question of how to align AI systems with human values and situates it within a wider body of thought regarding technology and value.
Fairness
Computers and Society
