计算机科学 ›› 2021, Vol. 48 ›› Issue (9): 1-8.doi: 10.11896/jsjkx.210600034

• 智能数据治理技术与系统* 上一篇    下一篇

人工智能治理理论及系统的现状与趋势

朝乐门, 尹显龙   

  1. 数据工程与知识工程教育部重点实验室(中国人民大学) 北京100872中国人民大学信息资源管理学院 北京100872
  • 收稿日期:2021-05-03 修回日期:2021-06-28 出版日期:2021-09-15 发布日期:2021-09-10
  • 通讯作者: 朝乐门(chaolemen@ruc.edu.cn)

AI Governance and System:Current Situation and Trend

CHAO Le-men, YIN Xian-long   

  1. Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China),Beijing 100872,China School of Information Resource Management,Renmin University of China,Beijing 100872,China
  • Received:2021-05-03 Revised:2021-06-28 Online:2021-09-15 Published:2021-09-10
  • About author:CHAO Le-men,born in 1979,Ph.D,asso-ciate professor,Ph.D supervisor.His main research interests include data science and big data analysis.

摘要: 人工智能(Artificial Intelligence,AI)治理是解决AI挑战的主要手段。AI治理的主要目的是充分发挥人工智能带来的优势和有效降低人工智能导致的风险,并通过整合技术、法律、政策、标准、伦理、道德、安全、经济、社会等多个方面的影响因素,最终建设负责任的人工智能(Responsible Artificial Intelligence,RAI)。AI治理可以从智能个体治理、智能群体治理以及人机合作与共生系统的治理等3个方面,分技术层、伦理层、社会及法律层等3个层面进行。AI治理的主要关键技术有4种:可理解性人工智能、防御对抗性攻击技术、建模及仿真技术和实时审计技术。从谷歌、IBM和微软等公司的AI治理实践来看,产业界主要关注的是RAI研发,在AI系统的可解释性、隐私保护和公平性检查等方面已出现一些专用组件工具。目前,AI治理需要研究的科学问题有:软件定义的AI治理、AI治理关键技术、大规模机器学习中的AI治理评价、基于联邦学习的AI治理、AI治理的标准制定、增强人工智能与人在回路型AI训练等。

关键词: 人工智能, 可理解性人工智能, 负责任人工智能, 人工智能治理

Abstract: The main purpose of AI governance is to take advantage of AI and reduce the risk.AI governance also aims to build a responsible AI via embracing the influencing factors such as technology,law,policy,standard,ethics,morality,safety,economy,as well as society.AI governance has three aspects:individual intelligent governance,group intelligent governance,human-computer cooperation and symbiotic system governance,which can be divided into three levels:technical level,ethical level,social and legal level.There are four key technologies for AI governance,which are intelligible AI,defense against adversarial attacks,modeling and simulation,and real-time audit.The industry is mostly concerned about developing a responsible AI in that by studying the actual practice of AI governance from leading companies like Google,IBM and Microsoft.Furthermore,tools like interpretability,privacy protection and fairness check for AI systems are already in use.At present,the main research topics on AI governance includes software-defined AI governance,key technologies of AI governance,AI governance evaluation in large-scale machine lear-ning,AI governance based on federated learning,standardization of AI governance,enhancement on artificial intelligence and human-in-the-loop AI training.

Key words: Artificial intelligence, Explainable artificial intelligence, Responsible artificial intelligence, AI governance

中图分类号: 

  • TP391
[1]SHARMA G D,YADAV A,CHOPRA R.Artificial intelligence and effective governance:A review,critique and research agenda[J].Sustainable Futures,2020,2:100004.
[2]WIRTZ B W,WEYERER J C,GEYER C.Artificial Intelligence and the Public Sector-Applications and Challenges[J].International Journal of Public Administration,2019,42(7):596-615.
[3]AI governance:Ensuring your AI is transparent,compliant,and trustworthy [EB/OL].[2021-05-15].https://www.ibm.com/analytics/common/smartpapers/ai-governance-smartpaper/#ai-governance-delivers.
[4]AI Governance:The path to responsibleadoption of artificial intelligence [R/OL].[2021-05-15].https://www.asianscientist.com/wp-content/uploads/2020/07/AI-Governance-Whitepaper-Basis-AI.pdf.
[5]ULNICANE I,KNIGHT W,LEACH T,et al.Framing Gover-nance for a Contested Emerging Technology:Insights from AI Policy[J].Policy and Society,2020,40(2):1-20.
[6]DURANTON S,MILLS S.Responsible AI:Leading by Example [EB/OL].(2021-02-03) [2021-05-15].https://medium.com/bcggamma/responsible-ai-leading-by-example-c25a8a0a98ea.
[7]Responsible Machine Learning [EB/OL].[2021-05-15].https://www.h2o.ai/responsible-ai/.
[8]WEARN O R,FREEMAN R,JACOBY D M.Responsible AIfor conservation[J].Nature Machine Intelligence,2019,1(2):72-73.
[9]DAFOE A.AI governance:a research agenda[EB/OL].(2018-08-27)[2021-05-15].https://www.fhi.ox.ac.uk/wp-content/uploads/GovAI-Agenda.pdf.
[10]KUZIEMSKI M,PALKA P.AI governance post-GDPR:lessons learned and the road ahead[J/OL].2019.http://diana-n.iue.it:8080/handle/1814/64146.
[11]LI T,SAHU A K,TALWALKAR A,et al.Federated learning:Challenges,methods,and future directions[J].IEEE Signal Processing Magazine,2020,37(3):50-60.
[12]GHALLAB M.Responsible AI:requirements and challenges[J].AI Perspectives,2019,1(1):1-7.
[13]WIRTZ B W,WEYERER J C,STURM B J.The dark sides of artificial intelligence:An integrated AI governance framework for public administration[J].International Journal of Public Administration,2020,43(9):818-829.
[14]GASSER U,ALMEIDA V A.A layered model for AI gover-nance[J].IEEE Internet Computing,2017,21(6):58-62.
[15]ADLER S.From Data Governance to AI Governance:How to successfully make the shift? [EB/OL].[2021-05-15].https://www.aidataanalytics.network/data-science-ai/whitepapers/fromdata--governance-to-ai-governance-how-to-successfully-make-the-shift.
[16]LEI Y,DUAN Y,SONG M.Technical Implementation Framework of AI Governance Policies for Cross-Modal Privacy Protection[C]//International Conference on Collaborative Computing:Networking,Applications and Worksharing.Springer,Cham,2020:431-443.
[17]SCHIFF D,BIDDLE J,BORENSTEIN J,et al.What's Next for AI Ethics,Policy,and Governance? A Global Overview[C]//Proceedings of the AAAI/ACM Conference on AI,Ethics,and Society.2020.
[18]THEODOROU A,DIGNUM V.Towards ethical and socio-legal governance in AI[J].Nature Machine Intelligence,2020,2(1):10-12.
[19]WACHTER S,MITTELSTADT B,FLORIDI L.Transparent,explainable,and accountable AI for robotics[J].Science (Robo-tics),2017,2(6):1-5.
[20]REDDY S,ALLAN S,COGHLAN S,et al.A governance model for the application of AI in health care[J].Journal of the American Medical Informatics Association,2020,27(3):491-497.
[21]POMARES J,ABDALA M B.The future of AI governance [J/OL].[2021-05-15].https://www.global-solutions-initiative.org/wp-content/uploads/2020/04/GSJ5_Pomares_Abdala.pdf.
[22]CIHON P,MAAS M M,KEMP L.Fragmentation and the Future:Investigating Architectures for International AI Gover-nance[J].Global Policy,2020,11(5):545-556.
[23]CIHON P.Standards for AI governance:international standards to enable global coordination in AI research & development [R].Future of Humanity Institute University of Oxford.2019:1-41.
[24]A practical guide to Responsible Artificial Intelligence (AI) [R/OL].[2021-05-15].https://www.pwc.com/gx/en/issues/data-and-analytics/artificial-intelligence/what-is-responsible-ai/responsible-ai-practical-guide.pdf.
[25]GUNNING D,AHA D.DARPA's explainable artificial intelligence (XAI) program[J].AI Magazine,2019,40(2):44-58.
[26]GUNNING D,STEFIK M,CHOI J,et al.XAI-Explainable artificial intelligence[J].Science Robotics,2019,4(37):1-2.
[27]JIMÉNEZ-LUNA J,GRISONI F,SCHNEIDER G.Drug disco-very with explainable artificial intelligence[J].Nature Machine Intelligence,2020,2(10):573-584.
[28]CHAKRABORTY A,ALAM M,DEY V,et al.Adversarial attacks anddefences:A survey[J].arXiv:181000069,2018.
[29]YEUNG K,HOWES A,POGREBNA G.AI governance by human rights-centred design,deliberation and oversight:An end to ethics washing [M].The Oxford Handbook of AI Ethics,Oxford University Press,2019:1-27.
[30]ZEIGLER B P,MUZY A,KOFMAN E.Theory of modeling and simulation:discrete event & iterative system computational foundations [M].Academic Press,2018.
[31]ROTHROCK L,NARAYANAN S.Human-in-the-loop simulations [M].Springer,2011.
[32]SALEIRO P,KUESTER B,HINKSON L,et al.Aequitas:A bias and fairness audit toolkit[J].arXiv:181105577,2018.
[33]TORRIE V.AI Governance in Canadian Banking:Fairness,Credit Models,and Equality Rights[J].Credit Models,and Equality Rights,2020,36(1):5-38.
[34]Implementation of the nationalAuroraAI programme [EB/OL].[2021-05-15].https://vm.fi/en/auroraai-en.
[35]REMOLINA N,SEAH J.How to Address the AI Governance Discussion? What Can We LearnFrom Singapore's AI Strategy?[J].SMU Centre for AI & Data Governance Research Paper,2019(8):1-18.
[36]SALEM F.A Smart City for public value:Digital transformation through agile governance-the case of ‘Smart Dubai'[J].World Government Summit Publications,Forthcoming,2020(5):1-70.
[37]LEE D.TAIGER featured as an exemplary model for AI Ethics and Governance practices by IMDA and PDPC [EB/OL] (2020-10-19) [2021-05-15].https://taiger.com/articles/taiger-featured-as-an-exemplary-model-for-ai-ethics-and-governance-practices-by-imda-and-pdpc/.
[38]AI PRINCIPLES OF TELEFÓNICA [EB/OL].[2021-05-15].https://www.telefonica.com/en/web/responsible -business/our-commitments/ai-principles.
[39]Perspectives on Issues in AI Governance[R/OL].[2021-05-15].https://ai.google/static/documents/perspectives -on-issues-in-ai-governance.pdf.
[40]AI Principles 2020 Progress update [R/OL].[2021-05-15].https://ai.google/static/documents/ai-principles-2020-progress-update.pdf.
[41]PROST F,QIAN H,CHEN Q,et al.Toward a better trade-off between performance and fairness with kernel-based distribution matching[J].arXiv:191011779,2019.
[42]WANG S,GUPTA M.Deontological ethics by monotonicityshape constraints[C]//International Conference on Artificial Intelligence and Statistics.PMLR,2020:2043-2054.
[43]LAGE I,CHEN E,HE J,et al.Human evaluation of modelsbuilt for interpretability[C]//Proceedings of the AAAI Confe-rence on Human Computation and Crowdsourcing.2019.
[44]KUCZMARSKI J.Reducing gender bias in Google Translate[EB/OL].(2018-12-6) [2021-05-15].https://blog.google/products/translate/reducing-gender-bias-google-translate/.
[45]PFISFER T.Google Cloud,Harvard Global Health Institute release improved COVID-19 Public Forecasts,share lessons lear-ned [EB/OL].(November 17,2020) [2021-05-15].https://cloud.google.com/blog/products/ai-machine-learning/google-and-harvard-improve-covid-19-forecasts.
[46]IBM,Artificial Intelligence [EB/OL].[2021-05-15].https://www.ibm.com/artificial-intelligence/ai-ethics-focus-areas.
[47]TUCKER E,VAIDYANATHAN R.AI Governance:Drivecompliance,efficiency and outcomes from your AI lifecycle [EB/OL].(2020-05-26) [2021-05-15].https://www.ibm.com/blogs/journey-to-ai/2020/05/ai-governance-drive-compliance-ef-ficiency-and-outcomes-from-your-ai-lifecycle/?mhsrc=ibmsea-rch_a&mhq=AI-Governance.
[48]VARSHNEY K R.Introducing AI Fairness 360 [EB/OL].(2018-09-19) [2021-05-15].https://www.ibm.com/blogs/research/2018/09/ai-fairness-360/.
[49]MOJSILOVIC A.Introucing AI Explainability 360 [EB/OL].(2019-08-08) [2021-05-15].https://www.ibm.com/blogs/research/2019/08/ai-explainability-360/?mhsrc=ibmsearch_a& mhq=IBM%20 Explainability%20360.
[50]HIND M.IBMFactSheets Further Advances Trust in AI[OL].(2020-07-09) [2021-05-15].https://www.ibm.com/blogs/research/2020/07/aifactsheets/?mhsrc=ibmsearch_a&mhq=AI%20Factsheet%20360.
[51]SOKALSKI M.Artificial Intelligence in Control with Wat-sonOpenScale [EB/OL].[2021-05-15].https://www.kpmg.us/alliances/kpmg-ibm/ai-in-control-watson-openscale.html.
[52]Research Collection:Research Supporting Responsible AI [EB/OL].(2020-04-13) [2021-05-15].https://www.microsoft.com/en-us/research/blog/research-collection-research-suppor-ting-responsible-ai/.
[53]MADAIO M A,STARK L,WORTMAN V J,et al.Co-desig-ning checklists to understand organizational challenges and opportunities around fairness in ai [C]//Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems.2020.
[54]GEBRU T,MORGENSTERN J,VECCHIONE B,et al.Data-sheets for datasets[J].arXiv:180309010,2018.
[55]Responsible bots:10 guidelines for developers of conversational AI [OL].(2018-09) [2021-05-15].https://www.microsoft.com/en-us/research/publication/responsible-bots/.
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