Dataset Biases: Institutionalized Discrimination or Adequate Transparency?

数据集偏见：制度化的歧视还是充分的透明度？

Dataset Biases: Institutionalized Discrimination or Adequate Transparency?

数据集偏见：制度化的歧视还是充分的透明度？

A review of the efforts performed by the US Mortgage Disclosure Act

对美国《抵押贷款披露法案》实施成效的回顾

[…] Why would we need such a law? Prior to Congress’s enacting HMDA in 1975, the public raised considerable concerns about mortgages — or, more importantly, the lack thereof — in some urban, often minority, neighborhoods. Certain areas seemed to decline, in part because their residents were not able to obtain home mortgages. (ClevelandFed)

[…] 我们为什么需要这样一部法律？在国会于1975年颁布《住房抵押贷款披露法案》（HMDA）之前，公众对某些城市（通常是少数族裔聚居区）的抵押贷款问题——或者更准确地说，抵押贷款的缺乏——表示了相当大的担忧。某些地区似乎正在衰退，部分原因是其居民无法获得住房抵押贷款。（克利夫兰联储）

This was one of the sad realities of certain American population centers in the 1970s. Access to capital remained difficult, and social mobility for at-risk neighborhoods was quasi-non-existent. This difficulty was accentuated by some believed to be institutionalized racism in the banking system. “Congress believed that some financial institutions had contributed to the decline of some geographic areas by their failure to provide adequate home financing to qualified applicants on reasonable terms and conditions.” (Wikipedia)

这是20世纪70年代美国某些人口中心的悲哀现实之一。获得资本的途径仍然困难，风险社区的社会流动性几乎不存在。一些人认为银行系统中存在制度化的种族主义，加剧了这种困境。“国会认为，一些金融机构未能以合理的条款和条件向合格申请人提供足够的住房融资，从而导致了某些地理区域的衰落。”（维基百科）

Therefore, a motion was brought forward to Congress to support transparency across all lending practices. They passed the Home Mortgage Disclosure Act of 1975 for mandatory reporting of all loan applications, and then the Community Reinvestment Act of 1977 for encouraging financial institutions to help meet the credit needs of their local communities.

因此，一项动议被提交给国会，以支持所有贷款业务的透明度。他们通过了1975年的《住房抵押贷款披露法案》，要求强制报告所有贷款申请，随后又通过了1977年的《社区再投资法案》，鼓励金融机构帮助满足当地社区的信贷需求。

There is a very clear mandate to the HMDA, as explained by Investopedia:

正如Investopedia所解释的那样，HMDA有一个非常明确的使命：

In general the primary purposes of the Home Mortgage Disclosure Act and Regulation C are to monitor the geographic targets of mortgage lenders, provide an identification mechanism for any predatory lending practices and to provide reporting statistics on the mortgage market to the government.

总体而言，《住房抵押贷款披露法案》和条例C的主要目的是监测抵押贷款机构的地理目标，提供识别任何掠夺性贷款行为的机制，并向政府提供抵押贷款市场的报告统计数据。

The HMDA helps to support the community investment initiatives sponsored by government programs, with HMDA contributing to the oversight of the initiatives through statistical reporting. HMDA also helps government officials to identify any predatory lending practices which may be affecting mortgage loan issuance.

HMDA有助于支持政府项目发起的社区投资计划，通过统计报告为计划的监督做出贡献。HMDA还帮助政府官员识别任何可能影响抵押贷款发放的掠夺性贷款行为。

HMDA submissions also provide a means for analyzing government resource allocations and ensuring that resources are appropriately allocated to fund community initiatives.

HMDA的提交还为分析政府资源分配和确保资源适当分配给社区计划提供了手段。

Therefore, financial institutions must report on their lending practices specifically by reporting not only all of the loans issued but also every loan application with the associated metadata of the applicant(s), such as race, gender, and neighborhood, as well as if the loan was approved or denied.

因此，金融机构必须报告其贷款业务，具体来说不仅要报告所有已发放的贷款，还要报告每一份贷款申请以及申请人的相关元数据，如种族、性别和社区，以及贷款是否被批准或拒绝。

From a regulatory perspective, there was now a lens through which violations to social equality can be tracked and penalties applied. This also means that there is an intentional bias in the data — that of institutionalized racism across the banking sector, and all of its manifestations.

从监管的角度来看，现在有了一面透镜，可以追踪违反社会平等的行为并施加惩罚。这也意味着数据中存在一种有意的偏见——即银行部门制度化的种族主义及其所有表现形式。

Exploring the Data

探索数据

Let’s explore the Home Mortgage Disclosure Data Files (1981–2014) from the US Archives to get a sense of what has been reported, and why.

让我们探索来自美国档案馆的住房抵押贷款披露数据文件（1981–2014），以了解报告了什么以及为什么报告。

(If you want to explore this dataset at home, you’ll also need a few more datasets, such as the relevant census data. Luckily, the superstar team of librarians at the US Archives packaged everything up for us in a single page. Thank your local librarian.)

（如果你想在家探索这个数据集，你还需要一些额外的数据集，如相关的人口普查数据。幸运的是，美国档案馆的超级图书管理员团队在一个页面上为我们打包了所有内容。感谢你当地的图书管理员。）

1981–1990

1981–1990

By exploring the data, we see that the 1981–1990 period was primarily focused on tracking veterans, many of them returning from Vietnam. A lot more of the questions and data pertains to VA applicants, and if they had requested financial support to apply for the loan.

通过探索数据，我们看到1981–1990年期间主要侧重于追踪退伍军人，其中许多人刚从越南返回。更多的问题和数据与VA（退伍军人事务部）申请人有关，以及他们是否曾要求经济支持来申请贷款。

The encoded format is quite straightforward, just needs a bit of mapping:

编码格式相当直接，只需要一些映射：

Overall, lots of VA considerations.

总体而言，大量涉及VA方面的考虑。

1990–2014

1990–2014

A big change was introduced in 1989 to the tracking priorities. From the FFIEC:

1989年对追踪优先事项进行了一次重大变更。来自FFIEC（联邦金融机构检查委员会）：

“[…] In 1989, the Federal Reserve Board revised Regulation C, to incorporate amendments contained in the Financial Institutions Reform, Recovery and Enforcement Act (FIRREA). The FIRREA amendments accomplished the following: expanded the coverage of HMDA to include mortgage lenders not affiliated with depository institutions or holding companies; required reporting of data regarding the disposition of applications for mortgage and home improvement loans in addition to data regarding loan originations and purchases; and required most lenders to identify the race, sex, and income of loan applicants and borrowers.”

“[…] 1989年，联邦储备委员会修订了条例C，以纳入《金融机构改革、恢复和执法法案》（FIRREA）中的修正案。FIRREA修正案完成了以下事项：扩大了HMDA的覆盖范围，将非存款机构或控股公司附属的抵押贷款机构也包括在内；要求在贷款发放和购买数据之外，还要报告抵押贷款和房屋改善贷款申请的处理情况数据；并要求大多数贷款机构识别贷款申请人和借款人的种族、性别和收入。”

As expected, the file mappings changes significantly, bt now follows a pattern usable until 2014:

如预期的那样，文件映射发生了显著变化，但现在遵循一种可用的模式直到2014年：

This means that we can now tell, for every application:

这意味着我们现在可以对每一份申请获知：

The race and gender of the applicant;

申请人的种族和性别；

the race and gender of the co-applicant;

共同申请人的种族和性别；

the neighborhood that they were in;

他们所在的社区；

the purpose of the loan (house or repair);

贷款目的（购房或维修）；

the list of reasons why a loan might’ve been denied;

贷款可能被拒绝的原因列表；

the total number of forms required to be submitted; and

需要提交的表格总数；以及

each and every one of the dates of the application process.

申请过程中每一天的日期。

Was a loan application intentionally slowplayed? Was a home repair denied that would’ve otherwise been approved? What about same-sex co-applicants? All visible.

贷款申请是否被故意拖延？本来会获批的房屋维修贷款是否被拒绝？同性共同申请人呢？全部可见。

(Note: we haven’t made our processed data available yet, as we’re still processing and investigating it. There are many reporting errors, like state acronyms instead of state codes, erroneous census tracks, and many other issues that still require a tremendous amount of preprocessing of the data to make sense of decades-long shifts in social norms. It would be unwise to come to any conclusions with it at this time. )

（注意：我们尚未公开处理后的数据，因为我们仍在处理和研究它。存在许多报告错误，如用州缩写代替州代码、错误的人口普查轨道，以及许多其他问题，仍需大量数据预处理才能理解数十年来的社会规范变迁。目前就此得出任何结论都是不明智的。）

Over the years, there’s more and more adherence to the program across most banks. The digitization wave that spread across the US can be verified year over year. There’s also a distinct shift downwards of less and less mortgage loan applications, stemming most likely from the subprime mortgage crisis:

多年来，大多数银行越来越遵守该计划。席卷美国的数字化浪潮可以逐年验证。还有一个明显的下降趋势，抵押贷款申请越来越少，很可能源于次贷危机：

Great so far, isn’t it? Now, here’s the double-edged sword about this data:

到目前为止还不错，对吧？现在，关于这些数据有一把双刃剑：

The same data that can be used to protect citizens against predatory bank practices can be used as a discriminatory weapon against them if data scientists do not understand the data they are using.

如果数据科学家不理解他们正在使用的数据，那么同样的数据，既可以被用来保护公民免受银行掠夺性行为的侵害，也可以被用作针对他们的歧视性武器。

How so? This dataset now has within it every bias, preferential treatment, and erroneously or maliciously declined loan application. Every possible discriminatory event is permanently recorded. There is no sanitizing possible of this data.

怎么会这样？这个数据集现在包含了每一种偏见、优待，以及错误或恶意拒绝的贷款申请。每一个可能的歧视事件都被永久记录。对这些数据进行任何清洗都是不可能的。

The HMDA dataset is biased — absolutely.

HMDA数据集是有偏见的——绝对如此。

Should this data even exist?

这些数据甚至应该存在吗？

By any measure, this data set can be considered invasive and discriminatory. Canada, usually considered a (slightly) more tolerant country, has a different approach to tracking race and racism. As explained in The Conversation:

无论如何衡量，这个数据集都可以被视为具有侵入性和歧视性的。加拿大——通常被认为是一个（稍微）更宽容的国家——在追踪种族和种族主义方面采取了不同的方法。正如《The Conversation》所解释的：

Canada’s anti-racism strategy, which draws on decades’ worth of research, states that race is a social construct. There is no basis for classifying people according to race, but racial bias and discrimination have very real effects. The question is: How do we get relevant data from the census and other surveys on the impact of systemic racism?

加拿大的反种族主义战略，借鉴了数十年的研究成果，指出种族是一种社会建构。按种族对人进行分类是没有根据的，但种族偏见和歧视却有非常真实的影响。问题是：我们如何从人口普查和其他调查中获取关于系统性种族主义影响的相关数据？

Statistics Canada tries to gather this information without directly asking about race. Race-based data is needed, says Jean-Pierre Corbeil, a diversity specialist at Statistics Canada. But he wonders whether that actually requires referring to race on the census.

加拿大统计局试图在不直接询问种族的情况下收集这些信息。加拿大统计局的多元化专家Jean-Pierre Corbeil表示，需要基于种族的数据。但他怀疑这是否真的需要在人口普查中提及种族。

Historically, the government has been reluctant to ask directly about race, which has led to a lack of disaggregated data. After the Second World War, the census used indirect methods of estimating the non-white, non-Indigenous population through racial proxies like language or ethnocultural origin.

从历史上看，政府一直不愿直接询问种族问题，这导致了分类数据的缺乏。第二次世界大战后，人口普查使用间接方法，通过语言或族裔文化来源等种族代理来估计非白人、非土著人口。

(Note: one of the major complaints against Canadian multiculturalism as a pillar of civil society is that it allows for people to classify others by ethnicity or nationality, under the veil of a self-assigned permission structure, but that’s a topic for later.)

（注意：对加拿大多元文化主义作为公民社会支柱的主要批评之一在于，它允许人们在自我认同的许可结构下按族裔或国籍对他人进行分类，但这是后话。）

So, this means some countries refuse to track the race and gender of bank loan applicants, as measuring racism fundamentally amplifies its very divisive nature of bucketing people into categories. And so, looking back at the HMDA data, the question that can be asked is, “should this data even exist?”

因此，这意味着一些国家拒绝追踪银行贷款申请人的种族和性别，因为衡量种族主义从根本上放大了其将人归类的分裂性。于是，回顾HMDA数据，可以问的问题是："这些数据甚至应该存在吗？"

I believe the answer is a strong yes. The Act has yielded tremendous protection to the public, especially with a major investigation back in 2005. From the Buffalo News:

我相信答案是坚定的"是"。该法案为公众提供了巨大的保护，尤其是在2005年的一项重大调查中。来自《布法罗新闻报》：

New York Attorney General Eliot Spitzer has fired his latest salvo, launching a preliminary probe into mortgage lending practices at eight major banks in the state, including HSBC Bank USA. […]

纽约州总检察长Eliot Spitzer发出了最新一轮攻势，对该州八家主要银行（包括汇丰银行美国分行）的抵押贷款业务展开初步调查。[…]

Investigators are trying to determine how the banks price their loans, and if fees and interest rates are being applied fairly, or whether there’s racial discrimination. […]

调查人员正试图确定银行如何定价贷款，费用和利率是否被公平地应用，或者是否存在种族歧视。[…]

Citigroup already agreed in 2002 to pay $215 million to settle allegations by the Federal Trade Commission that Associates First Capital Corp. — which Citi bought in 2000 — had engaged in predatory lending. Associates’ rival Household International — acquired by HSBC Holdings Plc in 2003 — paid $484 million in 2002 to settle similar charges by all 50 states in the largest consumer settlement ever.

花旗集团已于2002年同意支付2.15亿美元，以了结联邦贸易委员会关于Associates First Capital Corp.（花旗于2000年收购）从事掠夺性贷款的指控。Associates的竞争对手Household International（于2003年被汇丰控股收购）于2002年支付了4.84亿美元，了结了所有50个州的类似指控，这是有史以来最大的消费者和解案。

However, although this data has yielded success, it’s up to the data scientists to understand what are the right questions to ask, and more importantly what not to ask. For instance, trying to build a mortgage loan approval pipeline from this data is a horrendous and terrible idea — you now have a racist, homophobic bot. However, if you wish to investigate the very nature of institutionalized discrimination by investigating outliers, then you have a tremendous weapon at your disposal.

然而，尽管这些数据取得了成功，但数据科学家有责任理解什么是正确的问题，更重要的是什么不该问。例如，试图从这些数据中构建抵押贷款审批流程是一个可怕而糟糕的想法——你会得到一个种族主义、恐同的机器人。然而，如果你想通过调查异常值来研究制度化歧视的本质，那么你就拥有了一件强大的武器。

How you wield that weapon is up to you.

如何使用这件武器取决于你。

Additional Reading

扩展阅读

Revisiting the CRA: Perspectives on the Future of the Community Reinvestment Act, A Joint Publication of the Federal Reserve Banks of Boston and San Francisco, February 2009.

《重访CRA：社区再投资法案未来的展望》，波士顿和旧金山联邦储备银行联合出版物，2009年2月。

Privacy Impact Assessment of the Home Mortgage Disclosure Act Data Repository System, Board of Governors of the US Federal Reserve, November 2020.

《住房抵押贷款披露法案数据存储库系统隐私影响评估》，美国联邦储备委员会理事会，2020年11月。

I wrote the law Bloomberg blames for the financial crisis. He’s wrong., Robert Kuttner, op-ed in the Washington Post, February 15, 2020.

《我写了彭博社归咎于金融危机的法律。他错了。》，Robert Kuttner，华盛顿邮报专栏文章，2020年2月15日。

History of the HMDA, Federal Financial Institutions Examination Council, January 2018.

《HMDA历史》，联邦金融机构检查委员会，2018年1月。

Happy Consulting!

咨询愉快！

-Matt.

-Matt.

If you have additional questions about this article or our AI consulting framework, feel free to reach out by LinkedIn or by email.

如果您对本文或我们的AI咨询框架有其他问题，欢迎通过LinkedIn或电子邮件联系。

Other articles you may enjoy

您可能喜欢的其他文章

How Does AI Create Value?

AI如何创造价值？

Implementing a Corporate AI Strategy

实施企业AI战略

Outlier-Aware Clustering: Beyond K-Means

离群值感知聚类：超越K-Means

Rorschach Tests for Deep Learning Image Classifiers

深度学习图像分类器的罗夏测试