Trusted artificial intelligence

In this paper we discuss the problem of creating trusted artificial intelligence (AI) technologies. Modern AI is based on machine learning and neural networks and is vulnerable to biases and errors. Efforts are made to establish standards for the development of trusted AI technologies, but they have not yet succeeded. AI technologies trust can only be achieved with the appropriate scientific and technological base and corresponding tools and techniques for countering attacks. We present the ISP RAS Trusted AI Research Center results and propose a work model that can ensure technological independence and long-term sustainable development in this area.

Authors

Institutions

Publication Details

Journal
Вестник Российской академии наук
Published
2024-08-15
DOI
https://doi.org/10.31857/s0869587324030039
Citations
2
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.69
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Trusted artificial intelligence

A. I. Avetisyana
2 citations
Вестник Российской академии наук
Adversarial Robustness in Machine Learning
0.69
article

Trusted artificial intelligence

A. I. Avetisyana
article en
2 citations

Abstract

In this paper we discuss the problem of creating trusted artificial intelligence (AI) technologies. Modern AI is based on machine learning and neural networks and is vulnerable to biases and errors. Efforts are made to establish standards for the development of trusted AI technologies, but they have not yet succeeded. AI technologies trust can only be achieved with the appropriate scientific and technological base and corresponding tools and techniques for countering attacks. We present the ISP RAS Trusted AI Research Center results and propose a work model that can ensure technological independence and long-term sustainable development in this area.

Вестник Российской академии наукVol. 94(3)
Institute for System Programming (RU)
Openalex Percentile: Top 25%
Adversarial Robustness in Machine Learning
0.69
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.