71 citations · 291 across the 40 of their papers we have counts for
14 papers · 1 filter
The Only Way is Ethics: A Guide to Ethical Research with Large Language Models
Eddie L. Ungless, Nikolas Vitsakis, Zeerak Talat +5
There is a significant body of work looking at the ethical considerations of large language models (LLMs): critiquing tools to measure performance and harms; proposing toolkits to…
A Taxonomy of Systemic Risks from General-Purpose AI
Risto Uuk, Carlos Ignacio Gutierrez, Daniel Guppy +5
Through a systematic review of academic literature, we propose a taxonomy of systemic risks associated with artificial intelligence (AI), in particular general-purpose AI. Followin…
Democratic AI is Possible. The Democracy Levels Framework Shows How It Might Work
Aviv Ovadya, Kyle Redman, Luke Thorburn +11
This position paper argues that effectively "democratizing AI" requires democratic governance and alignment of AI, and that this is particularly valuable for decisions with systemi…
Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models
Eddie L. Ungless, Nikolas Vitsakis, Zeerak Talat +5
This whitepaper offers an overview of the ethical considerations surrounding research into or with large language models (LLMs). As LLMs become more integrated into widely used app…
Measurement challenges in AI catastrophic risk governance and safety frameworks
Atoosa Kasirzadeh
Safety frameworks represent a significant development in AI governance: they are the first type of publicly shared catastrophic risk management framework developed by major AI comp…
Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning
Andrew Smart, Atoosa Kasirzadeh
What is it to interpret the outputs of an opaque machine learning model. One approach is to develop interpretable machine learning techniques. These techniques aim to show how mach…
The Future of Open Human Feedback
Shachar Don-Yehiya, Ben Burtenshaw, Ramon Fernandez Astudillo +17
Human feedback on conversations with language language models (LLMs) is central to how these systems learn about the world, improve their capabilities, and are steered toward desir…
Epistemic Injustice in Generative AI
Jackie Kay, Atoosa Kasirzadeh, Shakir Mohamed
This paper investigates how generative AI can potentially undermine the integrity of collective knowledge and the processes we rely on to acquire, assess, and trust information, po…
Explanation Hacking: The perils of algorithmic recourse
Emily Sullivan, Atoosa Kasirzadeh
We argue that the trend toward providing users with feasible and actionable explanations of AI decisions, known as recourse explanations, comes with ethical downsides. Specifically…
CIVICS: Building a Dataset for Examining Culturally-Informed Values in Large Language Models
Giada Pistilli, Alina Leidinger, Yacine Jernite +3
This paper introduces the "CIVICS: Culturally-Informed & Values-Inclusive Corpus for Societal impacts" dataset, designed to evaluate the social and cultural variation of Large Lang…
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
Usman Anwar, Abulhair Saparov, Javier Rando +39
This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are organized into three different categories…
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Yashar Deldjoo, Zhankui He, Julian McAuley +7
Traditional recommender systems (RS) typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample…
Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation
Andrew Smart, Ding Wang, Ellis Monk +4
Data annotation remains the sine qua non of machine learning and AI. Recent empirical work on data annotation has begun to highlight the importance of rater diversity for fairness,…
Two Types of AI Existential Risk: Decisive and Accumulative
Atoosa Kasirzadeh
The conventional discourse on existential risks (x-risks) from AI typically focuses on abrupt, dire events caused by advanced AI systems, particularly those that might achieve or s…