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Found 21 papers, 9 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?
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.
Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers
Machine learning datasets have elicited concerns about privacy, bias, and unethical applications, leading to the retraction of prominent datasets such as DukeMTMC, MS-Celeb-1M, and Tiny Images.
T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems
Simulation has emerged as a popular method to study the long-term societal consequences of recommender systems.
Virtual Classrooms and Real Harms: Remote Learning at U.S. Universities
Universities have been forced to rely on remote educational technology to facilitate the rapid shift to online learning.
Cryptography and Security
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
Machine learning models are known to perpetuate and even amplify the biases present in the data.
Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites
Dark patterns are user interface design choices that benefit an online service by coercing, steering, or deceiving users into making unintended and potentially harmful decisions.
Human-Computer Interaction Computers and Society
Endorsements on Social Media: An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterest
Based on our findings, we make various design and policy suggestions to help improve advertising disclosure practices on social media platforms.
Human-Computer Interaction Computers and Society Social and Information Networks
An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterest
While disclosures relating to various forms of Internet advertising are well established and follow specific formats, endorsement marketing disclosures are often open-ended in nature and written by individual publishers.
Social and Information Networks Computers and Society Human-Computer Interaction
BlockSci: Design and applications of a blockchain analysis platform
We present BlockSci, an open-source software platform for blockchain analysis.
Cryptography and Security Databases
Spying on the Smart Home: Privacy Attacks and Defenses on Encrypted IoT Traffic
The growing market for smart home IoT devices promises new conveniences for consumers while presenting new challenges for preserving privacy within the home.
Cryptography and Security
Obfuscation in Bitcoin: Techniques and Politics
In the cryptographic currency Bitcoin, all transactions are recorded in the blockchain - a public, global, and immutable ledger.
Computers and Society Cryptography and Security
The Future of Ad Blocking: An Analytical Framework and New Techniques
Based on our state-space model, our new techniques, and this systematization, we offer insights into the likely "end game" of the arms race.
Cryptography and Security
Semantics derived automatically from language corpora contain human-like biases
Here we show for the first time that human-like semantic biases result from the application of standard machine learning to ordinary language---the same sort of language humans are exposed to every day.
When Coding Style Survives Compilation: De-anonymizing Programmers from Executable Binaries
Many distinguishing features present in source code, e. g. variable names, are removed in the compilation process, and compiler optimization may alter the structure of a program, further obscuring features that are known to be useful in determining authorship.
Cryptography and Security
How To Break Anonymity of the Netflix Prize Dataset
We present a new class of statistical de-anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on.
Cryptography and Security Databases
