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Found 15 papers, 6 papers with code
Large Language Models Overcome the Machine Penalty When Acting Fairly but Not When Acting Selfishly or Altruistically
We design three types of LLMs: (i) Cooperative, aiming to assist its human associate; (ii) Selfish, focusing solely on maximizing its self-interest; and (iii) Fair, balancing its own and collective interest, while slightly prioritizing self-interest.
Hack Me If You Can: Aggregating AutoEncoders for Countering Persistent Access Threats Within Highly Imbalanced Data
We evaluated our tool on a suite of provenance trace databases produced by the DARPA Transparent Computing program, where APT-like attacks constitute as little as 0. 004% of the data.
Self-Reflection Outcome is Sensitive to Prompt Construction
Some propose that such capabilities can be improved through self-reflection, i. e., letting LLMs reflect on their own output to identify and correct mistakes in the initial responses.
AI-generated faces influence gender stereotypes and racial homogenization
Text-to-image generative AI models such as Stable Diffusion are used daily by millions worldwide.
Coupled-Space Attacks against Random-Walk-based Anomaly Detection
In addition, we conduct transfer attack experiments in a black-box setting, which show that our feature attack significantly decreases the anomaly scores of target nodes.
HowkGPT: Investigating the Detection of ChatGPT-generated University Student Homework through Context-Aware Perplexity Analysis
As the use of Large Language Models (LLMs) in text generation tasks proliferates, concerns arise over their potential to compromise academic integrity.
Perception, performance, and detectability of conversational artificial intelligence across 32 university courses
Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to their propensity to classify human-written answers as AI-generated, as well as the ease with which AI-generated text can be edited to evade detection.
Human intuition as a defense against attribute inference
Attribute inference - the process of analyzing publicly available data in order to uncover hidden information - has become a major threat to privacy, given the recent technological leap in machine learning.
China and the U.S. produce more impactful AI research when collaborating together
Given AI's massive potential, as well as the fierce geopolitical tensions between the two nations, a number of policies have been put in place that discourage AI scientists from migrating to, or collaborating with, the other country.
Attacking Similarity-Based Link Prediction in Social Networks
We offer a comprehensive algorithmic investigation of the problem of attacking similarity-based link prediction through link deletion, focusing on two broad classes of such approaches, one which uses only local information about target links, and another which uses global network information.
Social and Information Networks Cryptography and Security
Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network
Link prediction is one of the fundamental research problems in network analysis.
Social and Information Networks Cryptography and Security 91D30 (Primary) 68T20 (Secondary) G.2.2; J.4
Game-theoretic Network Centrality: A Review
Game-theoretic centrality is a flexible and sophisticated approach to identify the most important nodes in a network.
Hiding Individuals and Communities in a Social Network
With this in mind, we ask the question: Can individuals or groups actively manage their connections to evade social network analysis tools?
Social and Information Networks Physics and Society 91D30 (Primary) 68T20 (Secondary) G.2.2; J.4
An Anytime Algorithm for Optimal Coalition Structure Generation
The algorithm is anytime, and if interrupted before it would have normally terminated, it can still provide a solution that is guaranteed to be within a bound from the optimal one.
Bounding the Estimation Error of Sampling-based Shapley Value Approximation
While this algorithm provides a bound on the approximation error, this bound is \textit{asymptotic}, meaning that it only holds when the number of samples increases to infinity.
Computer Science and Game Theory
