Research progress on risk analysis for artificial intelligence

Qun CHEN; Zhaoqiang CHEN; Boyi HOU; Lijuan WANG; Yuchen LUO; Zhanhuai LI

doi:10.11959/j.issn.2096-0271.2020005

您当前的位置：

首页 >

文章列表页 >

Research progress on risk analysis for artificial intelligence

STUDY | 更新时间：2024-06-03

- Research progress on risk analysis for artificial intelligence
- Big Data Research Vol. 6, Issue 1, Pages: 2020005-1(2020)
- 作者机构：
  
  1. 西北工业大学计算机学院，陕西西安 710129
  2. 西北工业大学大数据存储与管理工业和信息化部重点实验室，陕西西安 710129
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2020005
  CLC： TP391
- Online First：2020-01，
  
  Published：15 January 2020
- 稿件说明：
移动端阅览
Qun CHEN, Zhaoqiang CHEN, Boyi HOU, et al. Research progress on risk analysis for artificial intelligence[J]. Big Data Research, 2020, 6(1): 2020005-1.
DOI：

Qun CHEN, Zhaoqiang CHEN, Boyi HOU, et al. Research progress on risk analysis for artificial intelligence[J]. Big Data Research, 2020, 6(1): 2020005-1. DOI： 10.11959/j.issn.2096-0271.2020005.

摘要

目前基于深度学习模型的预测在真实场景中具有不确定性和不可解释性，给人工智能应用的落地带来了不可避免的风险。首先阐述了风险分析的必要性以及其需要具备的3个基本特征：可量化、可解释、可学习。接着，分析了风险分析的研究现状，并重点介绍了笔者最近提出的一个可量化、可解释和可学习的风险分析技术框架。最后，讨论风险分析的现有以及潜在的应用，并展望其未来的研究方向。

Abstract

The predictions of the deep learning models are still uncertain and uninterpretable.As a result

their deployments bring unavoidable risk to business decision making.Firstly

the study on risk analysis was motivated

and the three desirable properties of risk analysis techniques were described:quantifiability

interpretability and learnability.Then the existing work on risk analysis was reviewed

and the newly proposed framework to enable quantifiable

interpretable and learnable risk analysis was introduced.Finally

the existing and potential applications of risk analysis

and its future research direction were discussed.

关键词

Keywords

references

齐治昌 , 谭庆平 , 宁洪 . 软件工程 [M ] . 北京 : 高等教育出版社 , 2001 ： 304 - 331 .

QI Z C , TAN Q P , NING H . Software engineering [M ] . Beijing : Higher Education PressPress , 2001 : 304 - 331 .

AMODEI D , OLAH C , STEINHARDT J , et al . Concrete problems in AI safety [J ] . Computer Science , 2016 ,arXiv:1606.06565.

HENDRYCKS D , GIMPEL K . A baseline for detecting misclassified and outof-distribution examples in neural networks [C ] // The 5th International Conference on Learning Representations (ICLR),April 24-26,2017,Toulon,France.[S.l.:s.n] . 2017 : 1 - 21 .

CORBETT-DAVIES S , GOEL S . The measure and mismeasure of fairness:a critical review of fair machine learning [J ] . Computer Science , 2018 ,arXiv:1808.00023.

MORLEY J , FLORIDI L , KINSEY L , et al . From what to how:an overview of AI ethics tools,methods and research to translate principles into practices [J ] . Computer Science , 2019 ,arXiv:1905.06876.

JIANG H , KIM B , GUAN M , et al . To trust or not to trust a classifier [C ] // Advances in Neural Information Processing Systems,December 3,2018,Montreal,Canada.[S.l.:s.n] . 2018 : 5541 - 5552 .

KOHAVI R , . A study of cross-validation and bootstrap for accuracy estimation and model selection [C ] // The 14th International Joint Conference on Artificial Intelligence,August 20-25,1995,Montreal,Canada.San Francisco:Morgan Kaufmann Publishers Inc . 1995 : 1137 - 1143 .

RIBEIRO M T , SINGH S , GUESTRIN C . Why should I trust you? explaining the predictions of any classifier [C ] // The 22nd ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD),August 24-27,2016,San Francisco,USA . New York:ACM Press , 2016 : 1135 - 1144 .

GUIDOTTI R , MONREALE A , RUGGIERI S , et al . A survey of methods for explaining black box models [J ] . ACM Computing Surveys , 2019 , 51 ( 5 ):93.

RIBEIRO M T , SINGH S , GUESTRIN C . Anchors:high-precision model-agnostic explanations [C ] // The 32nd AAAI Conference on Artificial Intelligence,February 2-7,2018,New Orleans,USA.[S.l.:s.n] . 2018 : 1527 - 1535 .

BAEHRENS D , SCHROETER T , HARMELING S , et al . How to explain individual classification decisions [J ] . Journal of Machine Learning Research , 2010 , 11 ( 11 ): 1803 - 1831 .

KONONENKO I , STRUMBELJ E . An efficient explanation of individual classifications using game theory [J ] . Journal of Machine Learning Research , 2010 , 11 ( 1 ): 1 - 18 .

AMERSHI S , CHICKERING M , DRUCKER S , et al . Modeltracker:redesigning performance analysis tools for machine learning [C ] // The 33rd Annual ACM Conference on Human Factors in Computing Systems,April 18-23,2015,Seoul,Korea . New York:ACM Press , 2015 : 337 - 346 .

TOLOMEI G , SILVESTRI F , HAINES A , et al . Interpretable predictions of treebased ensembles via actionable feature tweaking [C ] // The 23rd ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD),August 13-17,2017,Halifax,Canada . New York:ACM Press , 2017 : 465 - 474 .

KONONENKO I , . Semi-naive Bayesian classifier [C ] // The 6th European Working Session on Learning,March 6-8,1991,Porto,Portugal . London:SpringerVerlag , 1991 : 206 - 219 .

PLATT J . Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods [J ] . Advances in Large Margin Classifiers , 1999 , 10 ( 3 ): 61 - 74 .

HENDRYCKS D , MAZEIKA M , DIETTERICH T . Deep anomaly detection with outlier exposure [C ] // The 7th International Conference on Learning Representations (ICLR),May 6-9,2019,New Orleans,USA.[S.l.:s.n] . 2019 : 1 - 18 .

OVADIA Y , FERTIG E , REN J , et al . Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift [J ] . Computer Science , 2019 ,arXiv:1906.02530.

ZHANG P , WANG J L , FARHADI A , et al . Predicting failures of vision systems [C ] // The IEEE Conference on Computer Vision and Pattern Recognition,June 23-28,2014,Columbus,USA . Piscataway:IEEE Press , 2014 : 3566 - 3573 .

BANSAL A , FARHADI A , PARIKH D . Towards transparent systems:semantic characterization of failure modes [C ] // The 13th European Conference on Computer Vision,September 6-12,2014,Zurich,Switzerland . Heidelberg:Springer , 2014 : 366 - 381 .

HOU B Y , CHEN Q , CHEN Z Q , et al . r-HUMO:a risk-aware human-machine cooperation framework for entity resolution with quality guarantees [J ] . IEEE Transactions on Knowledge and Data Engineering , 2018 : 1 - 13 .

CHEN Z Q , CHEN Q , HOU B Y , et al . Improving machine-based entity resolution with limited human effort:a risk perspective [C ] // The International Workshop on Real-Time Business Intelligence and Analytics,August 27,2018,Rio de Janeiro,Brazil.[S.l.:s.n . ] , 2018 : 1 - 5 .

Chen Z Q , Chen Q , Hou B Y , et al . Towards interpretable and learnable risk analysis for entity resolution [R ] . 2019 : 1 - 32 .

VENTURA F , CERQUITELLI T . What’s in the box? explaining the black-box model through an evaluation of its interpretable features [J ] .,2019,arXiv:1908.04348. Computer Science , 2019 :arXiv:1908.04348.

ANIS K , ZAKIA H , MOHAMED D , et al . Detecting depression severity by interpretable representations of motion dynamics [C ] // The 13th IEEE International Conference on Automatic Face and Gesture Recognition (FG),May 15-19,2018,Xi’an,China . Piscataway:IEEE Press , 2018 : 739 - 745 .

LICON C , BOSC G , SABRI M , et al . Chemical features mining provides new descriptive structure-odor relationships [J ] . PLoS Computational Biology , 2019 , 15 ( 4 ):e1006945.

MARKOWITZ H M , BLAY K A . 风险‒收益分析:理性投资的理论与实践(第一卷) [M ] . 唐亮,武微,译.北京 : 机械工业出版社 , 2016 ： 100 - 121

MARKOWITZ H M , BLAY K A . Riskreturn analysis:the theory and practice of rational investing(Volume 1) [M ] . Translated by TANG L,WU W . Beijing : China Machine PressPress , 2016 : 100 - 121 .

LI G L , WANG J N , ZHENG Y D , et al . Crowdsourced data management:a survey [J ] . IEEE Transactions on Knowledge and Data Engineering , 2016 , 28 ( 9 ): 2296 - 2319 .

CHEN Z Q , CHEN Q , FAN F F , et al . Enabling quality control for entity resolution:a human and machine cooperation framework [C ] // IEEE 34th International Conference on Data Engineering (ICDE),April 16-19,2018,Paris,France . Piscataway:IEEE Press , 2018 : 1156 - 1167 .

GISSIN D,SHALEV-SHWARTZ S . Discriminative active learning [J ] . 2019 ,arXiv:1907.06347.

GUO C , PLEISS G , SUN Y , et al . On calibration of modern neural networks [C ] // The 34th International Conference on Machine Learning,August 6-11,2017,Sydney,Australia.[S.l.:s.n] . 2017 : 1321 - 1330 .

Views

830

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A survey of voice conversion based on non-parallel data

An efficient and robust multi-scenario artificial intelligent medical model based on metaverse

Research on interpretable legal judgment prediction method based on causal graph analysis

Big data and computing models

Edge-intelligence-based immersive metaverse:key technologies and prospects

Related Author

Jing XIAO

Ning CHENG

Jianzong WANG

Xulong ZHANG

Pengcheng LI

Liang XU

Hongbiao SI

Yubing ZHOU

Related Institution

University of Science and Technology of China

Ping An Technology (Shenzhen) Co., Ltd.

Shenzhen University

Tsinghua Shenzhen International Graduate School

School of Electronic and Information Engineering, Beijing Jiaotong University

Postal code：100079
Tel：（010）53879208、53859533 Email：bdr@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：273228 Visits Today：134

⁰