毕业设计外文资料翻译-使用大数据管理人力资源

1、毕业设计外文资料翻译 学 院： 信息工程学院 专业班级： 软件工程12（3） 学生姓名： 学 号： 指导教师： 外文出处：(外文)JamesR.Kalyvas/Michael R.Overly BIG DATA A BUSINESS AND LEGAL GUIDE 附 件：1.外文资料翻译译文； 2.外文原文 指导教师评语：该同学的英文专业资料术语翻译较准确，体现了一定的专业英语水平。翻译材料能够与原文保持一致，能正确表达出原文意思。翻译字、词数满足要求。翻译材料的格式符合要求。该同学较好的完成了外文文献翻译工作。 签名： 2015年10月14日1外文资料翻译译文 使用大数据管理人力资源10.

2、3在人力资源管理中调节大数据的使用许多规范就业过程的联邦法律可能会对应用于人力资源管理的大数据的使用产生影响。虽然必须考虑到法律框架，但这些法律本身并没有禁止使用大数据。管理者在使用大数据以协助作出雇佣决策的时候，需要警惕潜在的法律上的有关大数据调查的见解、分析的结构和参考的决策的使用的限制。10.4在第七反歧视下1964的民权法第七章是反歧视立法的鼻祖。它禁止一切基于种族、肤色、宗教、民族起源和性别形式的就业歧视。在标题七下，两个理论的歧视已经发展到通过法院判决：不同的对待和不同的或不利的影响。第一，不同的对待更容易理解。因为他们的种族，肤色，或国籍而区别对待他们是非法的。我们都知道，只是因

3、为这个人是这些受保护组织中的一员而拒绝雇用某人是违法的。另一方面，不同的影响提出了一个更微妙的歧视理论。它认为，如果他们对受保护的成员有一个不相称的，或更不利的影响，则这个看似中立的就业做法可能是非法的。这一理论禁止雇主使用表面上中性的做法对保护类成员有不公正或不利的影响的就业惯例。当一个表面上中立的政策显示对法律保护组成员有不同的影响时，雇主可以通过证明其政策是合理的和所需要的工作相关的条例来为其辩护。不同的影响理论已经被用来发现非法歧视的一个典型的例子是在身高方面的要求。在标题七通过之前，警察部门要对警察学院的入学条件强加一个具体的最低身高要求是很常见的。表面上，这一政策对每个人都一样。不

4、过，据统计女性和某些少数民族的成员也许不太可能符合标准。因此他们会筛选出在更高的比例，或不同的影响。这是一个表面上非法歧视的例子。然而，在某种程度上，如果雇主能够证明为什么符合身高条件的人更有可能成为表现更加突出的警官的理论依据，他们可以反驳这个表面上的例子。大多数使用这种标准的警察部门不能做出这样的显示，所以在今天，这样的要求不再被使用。大数据的使用可以与不同的歧视的影响理论相结合的一个领域是通过增加测试的使用作出与雇佣相关的决定。任何时候，雇主都可以开发一个像测试的选择设备，所使用的设备应该经过在人力测量装置的使用方面接受过培训的专业人士的合理评估。培训的专业人员在使用的进行适当的评估。为

5、了承受不可避免的测试使用上受到的法律挑战，雇主必须准备制定原告的律师或起诉像EEOC（平等就业机会委员会）这样的政府机构必要的统计分析。至少，分析必须包括一个显示，在每个特定的组织的测试的方式方法统计显示是有效的测试者而且可靠（意味着它每次测试都用曾使用过的相同的东西）。通常，这种统计分析的开发将需要聘请专业的心理测试专家。从一些案例研究中可以发现，使用大数据的雇主可能会越来越多地寻求基于这样的作为识别个性特征的因素来做出决定。这可能会推动雇主使用测试和其他选择设备来隔离候选人的期望特征。通常，这样的测试表明不同的影响。事实上，在本节前面引用的格里格斯案件（见注12）中，杜克电力公司历史上被发

6、现隔离黑人雇员，让他们去做等级最低的工作。在标题七通过后，该公司消除了明显的种族隔离，但施加的智商测试作为跳到一个更高等级的工作的一个先决条件。据统计，在北卡罗莱纳州的黑人雇员在60年代没有在这样的测试中表现优异，因此他们的职业发展受到限制。当他们被起诉时，公司无法证明高智商是他们未来在电厂的工作表现的一个成功的预测者。结果发现这些测试的使用违反标题七。通过以上案件我们得到的教训就是，并不是所有的测试都是歧视性的。相反，如果一个雇主要使用测试，雇主必须合理使用（见表10.1）。要做到这一规定，雇主的做法必须符合一套完整的被称为员工选择程序统一的指南的法规。在1978年，包括平等就业机会委员会和

7、美国劳工部的四个联邦机构发出这一联合法规。指南适用于用作任何类型的就业决定基础的测试和其他选择程序，包括雇用，提拔，降级、保留和补偿。该指南确立使用一个像测试的选择设备的雇主如何证明（1）所采用的测试在他所管理的组织中既可靠又一致。（2）事实上，对它打算评估的表现是一个有效的预测者。例如，SAT和ACT大学入学考试一直验证预测一件事，只有一件事：作为一个一年级的大学生的表现。因此，在一个组织内，使用SAT或者ACT雇用或者促进个人的雇主，由受过训练的测试专家缺席一些额外的研究和分析，将被视为无效的使用，因此不恰当的防御对抗不利影响的发生。预测指标的测试可能是一个非常有价值的选择和留住员工的工具

8、。从一些例子中看到，雇主可以进行分析研究来隔离在其特定的组织中的成功的预测指标的技能标准。在这样的情况下，他们可以在候选人和他们现有的劳动力中测试这些技能。然而，使用的标准，无法统计验证支持的工作准则，并利用测试还没有被科学证明是可靠的测量如何测试标准，预测工作绩效可能会触犯的统一指南。雇主考虑使用的测试，以帮助在任何方面的就业过程中的决定，如果他们要做决定，他们需要做的是，他们需要做的是正确的。这就要求与测试和法律专家咨询，而不是从架子上拔下，这样做会有什么直觉。毕竟，正如我们所看到的，那么多关于大数据的使用涉及揭穿神话和超越传统的智慧。10.5 2007遗传信息和非歧视法案2007遗传信息

9、和非歧视法案（GINA）由委员会管理。在遗传信息和非歧视法案下，雇主由于雇员或申请者的基因信息而歧视他们是违法的。该法案还禁止雇主要求或购买他们的员工的遗传信息。根据遗传信息和非歧视法案，遗传信息是非常广泛的定义，它不仅包括个人的基因测试，而且也包括他们的家庭成员的基因测试。这些基因里包括员工或者他们的家庭成员可能会经历的潜在的疾病或障碍的信息。家族病史也包括在法律的定义中的信息，因为历史上，它已被用来确定雇员是否会在未来的情况下有增加疾病或障碍的风险。遗传信息和非歧视法案禁止任何遗传信息在就业方面的的使用，禁止雇主因为个人反对他们的遗传信息而歧视和骚扰或报复一个人。因此，使用遗传信息来预测一

10、个员工是否可能更容易受到疾病的积累，或未来的可性能问题都是违反该法案的。由于雇主难以控制提供给员工的医疗保险费用，而且难以保持老员工的健康，因此出现了一个爆炸性的员工健康计划。该健康计划是在公共卫生服务法的第2705（j）（1）节中被定义为任何雇主提供用来促进健康或预防疾病的计划。某些通过就业为基础的团体健康计划的覆盖面提供的健康计划的类型，现在必须符合负担得起的医疗法的标准。这是一个名副其实的混合的职场健康计划，从旨在促进健康的免费或打折的健身房会员利益，饮食教育或戒烟计划，到早期发现和更好地管理如糖尿病或癫痫这样的慢性疾病。为了更有效率，健康计划通常包括数据收集以预先确定员工的健康风险，然

11、后可用于工艺干预，以减少这些风险。当在员工健康领域中使用大数据时，它可能会触碰到这本书其他地方的隐私问题和法律的变化以及遗传信息和非歧视法案。然而，遗传信息和非歧视法案为雇主提供了一个安全的港湾：雇主提供健康或基因服务作为健康计划的一部分，员工提供事先知道，自愿和书面授权；只有参与提供这些服务的雇员（或家庭成员，如果家庭成员接受基因服务的话）和持牌的保健方面的专业人士或董事会认证的遗传咨询师会单独收到与这样的服务结果有关的可识别信息；而且任何单独提供的可识别的遗传信息没有透露给雇主，也没有透露具体员工身份的汇总的信息给雇主。因此，像目前讨论的其他与就业相关的法律一样，遗传信息和非歧视法案不会阻

12、碍雇主使用大数据来衡量和评估员工的健康，但它是有限制的，必须谨慎实施，仔细分析和使用法案包含的各种“安全港”。一种越来越流行的员工健康计划是雇主可能会碰上遗传信息和非歧视法案的常见领域。因此，通过确保雇主只看经过鉴定的有关其雇员健康状况的汇总数据来确保雇员的权利在遗传信息和非歧视法案下被保护。10.6国家劳动关系法与传统智慧相反，国家劳动关系法（NLRA）不只是适用于有工会的雇主。事实上，国家劳动关系法的法律保护延伸到那些被它掩盖的雇主的所有员工，也就是那些在私营部门从事州际贸易的雇主，不包括铁路部和航空公司。国家劳动关系法允许所有的员工，无论是否由工会代表，搞所谓的“保护协调一致的活动。”被

13、保护的协调一致的活动通常被定义为两个或更多的员工在他们的工作时间、工资、薪酬以及其他方面的工作条款和条件中采取的行动。2.外文原文10 Using Big Data to Manage Human ResourcesMark J. Neuberger10.3REGULATING THE USE OF BIG DATA IN HUMAN RESOURCE MANAGEMENTA number of federal laws that regulate the employment process may have an impact on the use of Big Data when app

14、lied to human resource management.Although due consideration must be given to the legal framework, none of these laws in and of themselves prevents the use of Big Data. Managers contemplating using Big Data to assist in employment decision making need to be wary of potential legal limitations on the

15、 use of the insights from their searches and structure their analysis and decisions accordingly.10.4 ANTIDISCRIMINATION UNDER TITLE VIITitle VII of the Civil Rights Act of 1964 is the granddaddy of antidiscrimination legislation.It prohibits all forms of employment discrimination on the basis of rac

16、e, color, religion, national origin, and sex.Under Title VII, two theories of discrimination have evolved through court decisions:disparate treatment and disparate or adverse impact. The first, disparate treatment, is much easier to understand. Treating someone differently because of their race, col

17、or, or national origin is illegal. We all know refusing to hire anyone simply because the person is a member of one of these protected groups is illegal. Disparate impact, on the other hand, presents a more nuanced theory of discrimination. It holds that seemingly neutral mployment practices may be

18、illegal if they have a disproportionate, or more adverse, impact on members of a protected group. This theory prohibits an employer from using a facially neutral employment practice that has an unjustified adverse impact on the members of the protected category.Whenever a facially neutral policy is

19、shown to have such a dis-parate impact on members of a legally protected group, an employer can defend its actions by proving that the policy is reasonably and rationally related to the job for which it is being required.A classic example in which the disparate impact theory has been used to find il

20、legal discrimination is in height requirements. Before the passage of Title VII, it was common for police departments to impose a specific minimum height requirement as a condition for admission to the police academy. On its face,the policy treats everyone the same. However,statistically women, and

21、perhaps members of certain minority groups,are less likely to meet the standard.They will therefore be screened out at much higher rates, or disparately impacted. That makes for a prima facie case of illegal discrimination. However, to the extent the employer can demonstrate a rational basis for why

22、 people of that height are more likely to be betterperforming police officers, they can rebut the prima facie case.Most police departments that used such standards could not make such a showing, and today, such requirements are no longer used.One area in which use of Big Data could run up against th

23、e disparate impact theory of discrimination is through increased use of tests in making employmentrelated decisions.Any time an employer develops a selection device like a test, the device being used should be properly assessed by professionals trained in the use of human measurement devices. To wit

24、hstand the inevitable legal challenge to the use of tests,employers must be prepared to produce to either plaintiffs counsel or a prosecuting governmental agency like the EEOC (Equal Employment Opportunity Commission) the necessary statistical analysis. At a minimum, that analysis must include a sho

25、wing that the manner and method of testing in each particular organization can be statistically shown to be both reliable (meaning it tests the same thing each time it is used) and a valid predictor. Typically, the development of such statistical analysis will require engaging a professional psychom

26、etrician. As seen in some of the case studies presented, employers who use Big Data may increasingly seek to make decisions based on such factors as identifiable personality traits. This may drive employers to the use of tests and other selection devices to isolate candidates with the desired traits

27、. Often, such tests have demonstrated disparate impact. In fact, in the Griggs case cited previously in this section (see Note 12), the Duke Power Company was found to have historically segregated Black employees into the lowest classification of jobs. After the passage of Title VII, the company eli

28、minated overt segregation but imposed passing an IQ test as a prerequisite for moving to a higherclassified job. Statistically, Black employees in North Carolina in the 1960s did not perform as well on such tests and therefore were limited in their career progression. When they were sued, the compan

29、ies could not demonstrate that increased IQ was a successful predictor of future job performance for the jobs in the power plant in question. As a result,theuse of these tests was found to violate Title VII.The lesson here is not that all tests are discriminatory. Rather, ifan employer wants to use

30、tests, the employer must do so properly (see Table10.1). To do that, employers must conform their practices to acomprehensive set of regulations known as the Uniform Guidelines on Employee Selection Procedures.In 1978, four federal agencies, including the EEOC and the US Department of Labor, issued

31、this joint regulation.The guidelines apply to tests and other selection procedures used as the basis for any type of employment decision, including hiring, promotion,demotion, retention, and compensation. The guidelines establish how an employer, using a selection device like testing, must demonstra

32、te that (1) the test adopted is both reliable and consistent among the parties to whom it is being administered, and (2) it is, in fact, a valid predictor of the performance it intends to assess. For example, the SAT and ACT college admission tests have been consistently validated to predict future

33、perfor-mance in one thing and one thing only: ones performance as a firstyear college student. Therefore, an employer using the SAT or ACT to hire or promote individuals within an organization, absent some additional study and analysis by trained testing experts, would be deemed an invalid use and t

34、herefore an improper defense against a showing of adverse impact.Testing for predictive indicators can be an extremely valuable tool in selecting and retaining engaged employees. As seen from some of the examples presented, employers can perform analytical research to isolate the skills criteria tha

35、t are predictive indicators for success in their particular organization. In such circumstances, they then can test for those skills, be it among candidate pools or among their current workforce. However, use of criteria that cannot be statistically validated to support the jobrelated criteria, and

36、the use of tests that have not been scientifically proven to be reliable measurements of how the test measures criteria which predict job performance will likely run afoul of the Uniform Guidelines. The takeaway for employers contemplating the use of tests to help make decisions in any aspect of the

37、 employment process is that if they are going to do it, they need to do it right. That requires consulting with testing and legal experts and not pulling tests off the shelf and doing what may intuitively make sense. After all, as we have seen, so much about the use of Big Data involves debunking my

38、ths and moving beyond conventional wisdom.10.5 THE GENETIC INFORMATION ANDNONDISCRIMINATION ACT OF 2007The Genetic Information and Nondiscrimination Act of 2007 (GINA) is also administered by the EEOC. Under GINA, it is illegal for employers to discriminate against either employees or applicants bec

39、ause of their genetic information. GINA also prohibits employers from requesting, requiring,or purchasing genetic information about their employees. Under GINA,genetic information is defined in very broad terms and includes genetic testing not only of the individual but also of their family members.

40、 This includes information about potential diseases or disorders the employee or their family members may experience. Family medical history is also included in the laws definition of information because, historically, it has been used to determine whether an employee has an increased risk of dis-ea

41、se, disorder, or condition in the future. GINA prohibits discrimination based on the use of genetic information in any aspect of employment and further prohibits employers from harassing or retaliating against an individual because the individual has objected to improper use of their genetic informa

42、tion. Thus, the accumulation of anything that constitutes genetic information to predict whether an employee may be more susceptible to disease, or future performance issues because of their genetic makeup,will run afoul of GINA.As employers struggle to contain the cost of providing medical insuranc

43、e to their employees and try to maintain the health of an aging workforce, there has been an explosion of employee wellness programs.Awellness program is defined in section 2705(j)(1)(A) of the Public Health Service Act as any program offered by an employer designed to promote health or prevent dise

44、ase. Certain types of wellness programs offered through employment-based group health plan coverage must now meet standards under the Affordable Care Act.There is a veritable potpourri of workplace wellness programs that run the gamut from benefits aimed to promote healthrelated behaviors such as fr

45、ee or discounted gym memberships, diet education or smoking cessation programs, to early identification and better management of chronic diseases like diabetes or epilepsy. To be effective, wellness programs typically include data collection to pre identify employee health risks, which can then be u

46、sed to craft interventions to reduce those risks.When used in the employee wellness area, Big Data may bump up against the variety of privacy concerns and laws described elsewhere in this book as well as GINA. GINA, however, provides a safe harbor for employers:Where health or genetic services are o

47、ffered by the employer . as part of a wellness program; the employee provides prior, knowing, voluntary, and written authorization; only the employee (or family member if the family member is receiving genetic services) and the licensed health care professional or board certified genetic counselor invo