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Found 10 papers, 1 papers with code
A Safe Harbor for AI Evaluation and Red Teaming
Independent evaluation and red teaming are critical for identifying the risks posed by generative AI systems.
On the Societal Impact of Open Foundation Models
To understand their risks of misuse, we design a risk assessment framework for analyzing their marginal risk.
Foundation Model Transparency Reports
Foundation models are critical digital technologies with sweeping societal impact that necessitates transparency.
Promises and pitfalls of artificial intelligence for legal applications
Is AI set to redefine the legal profession?
The Foundation Model Transparency Index
We score 10 major foundation model developers (e. g. OpenAI, Google, Meta) against the 100 indicators to assess their transparency.
REFORMS: Reporting Standards for Machine Learning Based Science
Machine learning (ML) methods are proliferating in scientific research.
Leakage and the Reproducibility Crisis in ML-based Science
To investigate the impact of reproducibility errors and the efficacy of model info sheets, we undertake a reproducibility study in a field where complex ML models are believed to vastly outperform older statistical models such as Logistic Regression (LR): civil war prediction.
The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning
We conclude by discussing risks that arise when sources of errors are misdiagnosed and the need to acknowledge the role of human inductive biases in learning and reform.
Balanced News Using Constrained Bandit-based Personalization
We present a prototype for a news search engine that presents balanced viewpoints across liberal and conservative articles with the goal of de-polarizing content and allowing users to escape their filter bubble.
An Algorithmic Framework to Control Bias in Bandit-based Personalization
Personalization is pervasive in the online space as it leads to higher efficiency and revenue by allowing the most relevant content to be served to each user.
