概括
对算法决策系统（AD）的不受监管的使用 - 系统会严重大量的个人数据并得出数据点之间的关系，对数百万美国人产生了负面影响。这些系统会影响公平访问教育,housing,就业， 和卫生保健, with life-altering effects. For example, commercial algorithms used to guide health decisions for approximately每年在美国2亿人被发现systematically discriminate against Black patients，将被确定为需要额外注意的黑人患者数量减少了一半以上。
打击算法危害的一种方法是进行系统审核，但是目前尚无必要规模审核AI系统的标准，以确保它们在合法，安全和符合公共利益的情况下运作。根据一个研究性学习检查AI审核的生态系统，只有1％的AI审计师认为当前的监管就足够了。
To address this problem, the National Institute of Standards and Technology (NIST) should invest in the development of comprehensive AI auditing tools, and federal agencies with the charge of protecting civil rights and liberties should collaborate with NIST to develop these tools and push for comprehensive system audits.
这些审核工具将有助于这些联邦机构的执法部门节省时间和金钱，同时履行其法定职责。此外，现在有迫切需要开发这些工具行政命令13985指示机构“将其民权机构和办公室集中在新兴威胁上，例如自动化技术中的算法歧视”。
Challenge and Opportunity
The use of AI systems across all aspects of life has become commonplace as a way to improve decision-making and automate routine tasks. However, their unchecked use can永久性的历史不平等, such as discrimination and bias, while also potentially侵犯美国公民权利.
算法决策系统通常以大量自动化的方式用于优先级，分类，关联和过滤任务。当人们不批判地依靠系统的产出，将其用作人类决策的替代或使用不了解其开发方式的系统时，广告就会成为威胁。这些系统虽然在许多情况下都非常有用和节省成本，但必须以公平和安全的方式创建。
确保广告的法律和安全使用始于认识到联邦政府面临的挑战。一方面，政府希望避免投入过多的资源来管理这些系统。随着新的AI系统每天都会发行，密切监督每个系统变得不合理。另一方面，我们不能盲目相信所有开发人员和用户都可以通过广告做出适当的选择。
这是AI开发生命周期的工具开始发挥作用的地方，在持续监控和盲目信任之间提供了第三个选择。通过实施审计工具和签署实践，AI开发人员将能够证明符合先前和定义明确的标准，同时增强其系统的安全性和公平性。
Due to the extensive scope and diverse applications of AI systems, it would be difficult for the government to create a centralized body to oversee all systems or demand each agency develop solutions on its own. Instead, some responsibility should be shifted to AI developers and users, as they possess the specialized knowledge and motivation to maintain proper functioning systems. This allows the enforcement arms of federal agencies tasked with protecting the public to focus on what they do best, safeguarding citizens’ civil rights and liberties.
Plan of Action
为了确保整个AI开发生命周期中的安全性和验证，必须使用一套审核工具。这些工具应有助于实现我们关心的结果，公平，公平和合法性。这些审核的结果应报告（例如，在不可授权的开发人员和执法机构才能访问的不变分类帐中）或通过可验证的代码签名机制。我们将报告的细节留给了所涉利益相关者，因为每个机构可能都有不同的报告结构和需求。其他可能的选项，例如在不使用工具的情况下进行的手动审核或审核，可能无法提供相同水平的效率，可扩展性，透明度，准确性或安全性。
The federal government’s role is to provide the necessary tools and processes for self-regulatory practices. Heavy-handed regulations or excessive government oversight are not well-received in the tech industry, which argues that they tend to stifle innovation and competition. AI developers also have concerns about safeguarding their proprietary information and users’ personal data, particularly in light of data protection laws.
审计工具通过使AI开发人员能够以透明的方式共享和报告信息，同时仍保护敏感信息，从而为这一挑战提供了解决方案。这允许在透明度和隐私之间保持平衡，为自我调节的生态系统提供必要的信任。
A general machine learning lifecycle. Examples of what system developers at each stage would be responsible for signing off on the use of the security and equity tools in the lifecycle. These developers represent companies, teams, or individuals.
The equity tool and process, funded and developed by government agencies such as NIST, would consist of a combination of (1) AI auditing tools for security and fairness (which could be based on or incorporate open source tools such asAI Fairness 360and the对抗性鲁棒性工具箱），以及（2）整合这些支票的标准化过程和指南（这可能基于或不可逆的指导，例如美国政府问责办公室的人工智能：联邦机构和其他实体的问责框架）。1
Dioptra, a recentNIST与国家网络安全卓越中心（NCCOE）之间的努力（NCCOE）为安全性和鲁棒性建立机器学习测试床，是理想情况下可以开发的生命周期管理应用程序类型的一个很好的例子。未能保护公民权利并确保公平的结果必须像安全缺陷一样认真对待，因为这既影响我们的国家安全和生活质量。
Equity considerations should be applied across the entire lifecycle; training data is not the only possible source of problems. Inappropriate data handling, model selection, algorithm design, and deployment, also contribute to unjust outcomes. This is why tools combined with specific guidance is essential.
As一些学者注意，“目前尚无有关哪种工具有用或适用于哪种目的或受众的可用通用和比较指南。这限制了工具包的可访问性和可用性，并会导致从业人员为其用例选择一个最佳或不合适的工具，或者简单地使用第一个发现而不意识到他们选择了他们选择的方法。透明
Companies utilizing the various packaged tools on their ADS could sign off on the results using code signing. This would create a record that these organizations ran these audits along their development lifecycle and received satisfactory outcomes.
我们设想了一套审核工具，每个工具都适用于特定机构和执法任务。这种技术的先例已经存在。很像security became a part of the software development lifecycle在NIST开发的指导下，公平和公平也应集成到AI生命周期中。NIST可以为审核AI工具，领先的指导，分发和维护此类工具的范围启用政府范围内的计划。考虑到其历史，NIST是一个适当的选择评估技术and providing指导在开发和使用特定的AI达成ications such as the NIST-led面部识别供应商测试（FRVT）.
影响和机构 /部门涉及的领域
安全与正义
美国国土安全部特别诉讼部门的美国司法部，美国海关和边境保护局美国元帅服务部
公共和社会部门
美国住房和城市发展部的公平住房办公室和机会均等
教育
The U.S. Department of Education
Environment
美国农业部，民权助理部长办公室联邦能源监管委员会环境保护署
Crisis Response
联邦应急管理机构
Health & Hunger
美国卫生与公共服务部，疾病控制和预防公民权利中心办公室食品和药物管理局
Economic
The Equal Employment Opportunity Commission, The U.S. Department of Labor, Office of Federal Contract Compliance Programs
Infrastructure
The U.S. Department of Transportation, Office of Civil RightsThe Federal Aviation AdministrationThe Federal Highway Administration
信息验证和验证
联邦贸易委员会，联邦通讯委员会，美国证券交易委员会。
这些工具中有许多是开源的，对公众免费。第一步可能是将这些工具与特定于代理商的标准结合在一起，并对其实施过程进行简单的语言解释。
好处
These tools would provide several benefits to federal agencies and developers alike. First, they allow organizations to protect their data and proprietary information while performing audits. Any audits, whether on the data, model, or overall outcomes, would be run and reported by the developers themselves. Developers of these systems are the best choice for this task since ADS applications vary widely, and the particular audits needed depend on the application.
其次，尽管许多开发人员可能会选择自愿使用这些工具，但标准化和要求使用它们的使用将允许评估任何被认为违反法律的系统。这样，联邦政府将能够更有效地管理标准。
第三，尽管该工具将是为导致广告的AI生命周期设计的，但也可以应用于传统的审计过程。需要根据现有的法律标准和评估过程制定指标和评估标准；一旦将这些指标蒸馏成特定工具，该工具也可以应用于非ADS数据，例如结果或传统审核的最终指标。
第四，我们认为，政府的强烈信号表明，广告中的股权考虑因素很重要，并且很容易执行会更广泛地影响AI应用程序，从而使这些考虑因素正常化。
机会的示例
可能使用此工具的机构是住房和城市发展部（HUD），其目的是确保住房提供者不会根据种族，颜色，宗教，民族来源，性，性别，家庭地位或残疾来歧视。
为了执行这些标准，HUD负责21,000auditsa year, investigates and audits housing providers to assess compliance with the公平住房法, the同等信用机会法和其他相关法规。在这些审核期间，HUD可能会审查提供商的政策，程序和记录，以及进行现场检查和测试以确定合规性。
使用AI审核工具可以简化和增强HUD的审核过程。如果使用广告并怀疑受到损害，HUD可能会要求验证审计过程已完成并满足特定的指标，或者要求经过此类过程并向他们报告。
不遵守非歧视法律标准也将适用于ADS开发人员，我们设想保护机构的执法部门将在这些情况下与非ADS案件相同的罚款。
研发
为了使这种方法可行，NIST将需要资金和政策支持来实施此计划。最近的《芯片与科学法》已有支持NIST角色的规定在开发“值得信赖的人工智能和数据科学”时，包括上述测试台。研究与开发可以部分签给大学和其他国家实验室，或通过与私人公司和组织的合伙企业/合同。
首先需要与有兴趣将审计工具集成到其流程中的机构合作开发。NIST开发的特定工具和指导必须适用于每个代理机构的用例。
审计过程将包括审核数据，模型和其他对了解系统的影响和使用至关重要的信息，并由现有法规/准则告知。如果发现系统不合格，则执法机构有权对系统施加惩罚或需要进行更改。
试点计划
NIST应该制定试点计划来测试AI审计的可行性。它应该在较小的系统上进行，以测试AI审核工具和指导的有效性，并确定任何潜在的问题或改进领域。金博宝正规网址NIST应使用试点计划的结果来告知AI审计前进的标准和准则的制定。
Collaborative efforts
实现自我调节的生态系统需要协作。联邦政府应与行业专家和利益相关者合作，开发自我调节的必要工具和实践。
在开发和测试工具期间，应建立NIST，联邦机构发行专家和广告开发人员的多方利益相关者团队。协作努力将有助于划定责任，AI创建者和用户负责执行和维持遵守标准和准则的遵守，以及负责确保继续遵守的机构执法武器机构。
定期监视和更新
The enforcement agencies will continuously monitor and update the standards and guidelines to keep them up to date with the latest advancements and to ensure that AI systems continue to meet the legal and ethical standards set forth by the government.
透明度和记录保存
Code-signing technology can be used to provide transparency and record-keeping for ADS. This can be used to store information on the auditing outcomes of the ADS, making reporting easy and verifiable and providing a level of accountability to users of these systems.
结论
Creating auditing tools for ADS presents a significant opportunity to enhance equity, transparency, accountability, and compliance with legal and ethical standards. The federal government can play a crucial role in this effort by investing in the research and development of tools, developing guidelines, gathering stakeholders, and enforcing compliance. By taking these steps, the government can help ensure that ADS are developed and used in a manner that is safe, fair, and equitable.
代码签名用于建立对通过Internet或其他网络分布的代码的信任。通过以数字方式签署代码，代码签名者为其身份提供了认证，并对其内容负责。当用户下载已签署的代码时，他们的计算机或设备可以验证该代码尚未被篡改，并且它来自可信赖的来源。
Code signing can be extended to all parts of the AI lifecycle as a means of verifying the authenticity, integrity, and function of a particular piece of code or a larger process. After each step in the auditing process, code signing enables developers to leave a well-documented trail for enforcement bodies/auditors to follow if a system were suspected of unfair discrimination or unsafe operation.
Code signing is not essential for this project’s success, and we believe that the specifics of the auditing process, including documentation, are best left to individual agencies and their needs. However, code signing could be a useful piece of any tools developed.
此外，工具设计可能还会有阻塞。重要的是要记住，目前，工程师经常在开发过程结束时使用公平工具作为检查的最后一个框，而不是作为AI开发生命周期的集成部分。可以通过强调采取的全面方法并制定伴随这些工具的必要指导来解决这些问题，而这些工具目前不存在。
纽约监管机构正在呼吁联合健康集团停止使用或证明研究人员说，公司制造的算法没有任何问题。该算法因评估患者的健康风险而出售给医院，但尽管黑人患者病得多，但对白人患者和黑人患者的风险得分却相似。
在这种情况下，研究人员发现，仅更改一个参数可以产生“偏见减少84％”。如果我们拥有有关进入模型的参数以及如何加权的特定信息，我们将拥有一个记录保存系统，以查看某些干预措施如何影响该模型的输出。
Bias in AI systems used in healthcare could potentially violate the Constitution’s Equal Protection Clause, which prohibits discrimination on the basis of race. If the algorithm is found to have a disproportionately negative impact on a certain racial group, this could be considered discrimination. It could also potentially violate the Due Process Clause, which protects against arbitrary or unfair treatment by the government or a government actor. If an algorithm used by hospitals, which are often funded by the government or regulated by government agencies, is found to exhibit significant racial bias, this could be considered unfair or arbitrary treatment.
示例＃2：警务
联合国关于消除种族歧视的小组提出了人们对在执法和移民中面部识别等技术越来越多的使用的关注，警告说，它会加剧种族主义和仇外心理，并可能导致侵犯人权行为。该小组指出，尽管AI可以在某些领域提高绩效，但它也可以产生相反的效果，因为它减少了暴露于歧视性执法的社区的信任与合作。此外，该小组强调了这些技术可以利用有偏见的数据的风险，从而在某些地区产生了过度杀人的“恶性循环”，并逮捕了更多。它建议在用于分析和实施用于处理投诉的独立机制的算法的设计和实施方面具有更高的透明度。
一项关于芝加哥警察局战略主题清单（SSL）的案例研究讨论了该算法驱动的技术，用于识别有参与枪支暴力的高风险的人，并为其警务策略提供了信息。但是，兰德公司对SSL的早期版本的一项研究发现，它在减少枪支暴力或减少受害的可能性方面没有成功，而将SSL的纳入仅直接影响逮捕。该研究还提出了重大的隐私和民权问题。此外，调查结果表明，SSL上超过三分之一的人，约有70％的同伙，从未被捕或犯罪的受害者，但获得了高风险得分。此外，芝加哥30岁以下的黑人中有56％的SSL风险得分。This demographic has also been disproportionately affected by the CPD’s past discriminatory practices and issues, including torturing Black men between 1972 and 1994, performing unlawful stops and frisks disproportionately on Black residents, engaging in a pattern or practice of unconstitutional use of force, poor data collection, and systemic deficiencies in training and supervision, accountability systems, and conduct disproportionately affecting Black and Latino residents.
预测警务,它使用数据和算法s to try to predict where crimes are likely to occur, has been criticized for reproducing and reinforcing biases in the criminal justice system. This can lead to discriminatory practices and violations of the Fourth Amendment’s prohibition on unreasonable searches and seizures, as well as the Fourteenth Amendment’s guarantee of equal protection under the law. Additionally, bias in policing more generally can also violate these constitutional provisions, as well as potentially violating the Fourth Amendment’s prohibition on excessive force.
Example #3: Recruiting
ADS in recruiting crunch large amounts of personal data and, given some objective, derive relationships between data points. The aim is to use systems capable of processing more data than a human ever could to uncover hidden relationships and trends that will then provide insights for people making all types of difficult decisions.
各个行业的招聘经理每天都使用广告来帮助决策过程。实际上，2020年的一项研究报告说，美国有55％的人力资源领导者在其商业实践中使用预测算法，包括招聘决定。
For example, employers use ADS to screen and assess candidates during the recruitment process and to identify best-fit candidates based on publicly available information. Some systems even analyze facial expressions during interviews to assess personalities. These systems promise organizations a faster, more efficient hiring process. ADS do theoretically have the potential to create a fairer, qualification-based hiring process that removes the effects of human bias. However, they also possess just as much potential to codify new and existing prejudice across the job application and hiring process.
在招聘中使用广告可能会违反几项宪法，包括歧视法，例如1964年《民权法》第七章和《美国残疾人法》。这些法律禁止在工作场所中基于种族，性别和残疾以及其他受保护的特征来歧视。此外，这些系统还可能侵犯隐私权和工作申请人的正当程序权利。如果发现这些系统具有歧视性或违反这些法律，则它们可能会对雇主提起法律行动。
A large portion of gig workers are people of color, and the nature of their temporary and largely unregulated work can leave them vulnerable to economic instability and workplace abuse. To increase protections for fair work, the Department of Labor should create an Office of the Ombudsman for Fair Work.
孕产妇死亡是美国的危机。Biden-Harris政府应建立一个AI卓越中心，以将数据源汇总在一起，然后分析，诊断和解决孕产妇的健康差异，同时证明值得信赖和负责任的AI原则。
为了解决算法危害的问题，国家标准技术研究所（NIST）应投资于全面的AI审计工具的开发，而联邦机构负责保护民权和自由，应与NIST合作开发这些工具并推动这些工具并推动推动用于全面的系统审核。
People working in the gig economy, tech supply chain, and other automation-adjacent roles face a huge barrier to unionizing their workplaces. These roles, which are among the fastest-growing segments of the U.S. economy, are overwhelmingly filled by BIPOC workers. The Biden-Harris Administration can promote racial equity and support low-wage BIPOC workers’ unionization efforts by creating a National Bargaining in Good Faith Fund.