关键词: artificial intelligence auditing bias explainability machine learning transparency

来  源:   DOI:10.1098/rsos.230859   PDF(Pubmed)

Abstract:
-Business reliance on algorithms is becoming ubiquitous, and companies are increasingly concerned about their algorithms causing major financial or reputational damage. High-profile cases include Google\'s AI algorithm for photo classification mistakenly labelling a black couple as gorillas in 2015 (Gebru 2020 In The Oxford handbook of ethics of AI, pp. 251-269), Microsoft\'s AI chatbot Tay that spread racist, sexist and antisemitic speech on Twitter (now X) (Wolf et al. 2017 ACM Sigcas Comput. Soc. 47, 54-64 (doi:10.1145/3144592.3144598)), and Amazon\'s AI recruiting tool being scrapped after showing bias against women. In response, governments are legislating and imposing bans, regulators fining companies and the judiciary discussing potentially making algorithms artificial \'persons\' in law. As with financial audits, governments, business and society will require algorithm audits; formal assurance that algorithms are legal, ethical and safe. A new industry is envisaged: Auditing and Assurance of Algorithms (cf. data privacy), with the remit to professionalize and industrialize AI, ML and associated algorithms. The stakeholders range from those working on policy/regulation to industry practitioners and developers. We also anticipate the nature and scope of the auditing levels and framework presented will inform those interested in systems of governance and compliance with regulation/standards. Our goal in this article is to survey the key areas necessary to perform auditing and assurance and instigate the debate in this novel area of research and practice.
摘要:
-业务对算法的依赖正变得无处不在,公司越来越担心他们的算法会造成重大的财务或声誉损害。备受瞩目的案例包括谷歌的人工智能算法在2015年错误地将一对黑人夫妇标记为大猩猩(Gebru2020在牛津人工智能伦理手册中,pp.251-269),微软的AI聊天机器人Tay传播种族主义,推特上的性别歧视和反犹太言论(现为X)(Wolf等人。2017ACMSigcasComput。Soc.47,54-64(doi:10.1145/3144592.3144598)),亚马逊的人工智能招聘工具在显示出对女性的偏见后被废弃。作为回应,政府正在立法和实施禁令,监管机构对公司和司法机构进行罚款,讨论可能使算法在法律上人为“人”。与财务审计一样,政府,商业和社会将需要算法审计;正式保证算法是合法的,道德和安全。设想了一个新的行业:算法的审计和保证(参见数据隐私),具有使AI专业化和产业化的职权范围,ML和相关算法。利益相关者从从事政策/法规工作的人到行业从业者和开发人员。Wealsoexpectedthenatureandscopeoftheauditlevelsandframeworkpresentedwillinformthoseinterestedinsystemsofgovernanceandcompliancewithregulations/standards.我们在本文中的目标是调查执行审计和保证所必需的关键领域,并在这个新颖的研究和实践领域引发辩论。
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