生物多样性分类器多样性_优先考虑多样性是谁的工作-优快云博客

本文探讨了技术设计中缺乏多样性的后果，特别是在AI和机器学习领域，如何加剧社会不平等。作者呼吁科技行业重视多样性，避免偏见，确保技术为所有人服务。

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生物多样性分类器多样性

Did you know that, according to the Fitzpatrick scale, there are six skin types by which dermatologists and plastic surgeons categorize skin? These medical categorizations are guidelines that group skin types based on their response to UV light. Someone’s skin type typically falls somewhere on the spectrum between I and VI instead of in one precise group.

您是否知道，根据Fitzpatrick量表，皮肤科医生和整形外科医生将皮肤分为六类？这些医学分类是准则根据对紫外线的React对皮肤类型进行分组。某人的皮肤类型通常属于I和VI之间的某个范围，而不是一个精确的组。

In comparison, take another well-known categorization of skin types: race. The concept of different races is based on the circa 15th-century idea that all humans can be separated into several biogenetically different groups based on physical traits. Race, as a human categorization, is a flawed system for grouping because it’s rooted in a problematic, colonial mindset that some groups of humans are superior to others based on physical traits. It has been perpetuated for so long, unchallenged, that we still use this social architecture in many of our cultures.

相比之下，采用另一种众所周知的皮肤类型分类：种族。不同种族的概念基于大约15世纪的思想，即根据生理特征，所有人类都可以分为几个生物遗传学上不同的群体。作为人类的分类，种族是一种有缺陷的分组系统，因为种族植根于一个有问题的殖民观念，即某些人类群体在身体特征方面优于其他种族。它已经存在了很长时间，没有受到挑战，因此我们仍然在许多文化中使用这种社会架构。

The not-so-subtle nuance between being “skin Type V” versus “Black” already paints a picture of how, when groupings are riddled with bias and a narrative that prioritizes a single group of users, the knock-on effect can be exponential when it goes unchecked. Before you roll your eyes and say that’s a little dramatic, just remember how normalized race is to us today. There was a time when the concept of race as we know it didn’t exist. The journey between point A and point B happens faster than you think.

“ V型皮肤”与“黑色”之间不太细微的差别已经描绘出了这样的情况：当分组充满偏见和将单个用户划分为优先顺序的叙述时，连锁效应可能呈指数级增长当它不受控制时。在您睁大眼睛说这有一点戏剧性之前，请记住今天对我们来说标准化的种族是多么的。曾经有一段时间，我们不知道种族的概念。 A点和B点之间的行程比您想象的要快。

Joy Buolamwini coined the phrase “the Coded Gaze,” which encompasses the dangers of machine learning techniques that are deployed without considerations of diversity in terms of gender and race. If you’re on the same page, you’re aware that AI is becoming more mainstream and an inevitable part of our day-to-day lives.

乔伊·布拉姆维尼 ( Joy Buolamwini)创造了“编码注视”一词，该词包含了机器学习技术的危险，这些技术在没有考虑性别和种族多样性的情况下就被部署了。如果您在同一页面上，您会意识到AI正在变得越来越主流，并且是我们日常生活中不可避免的一部分。

Take the ever-controversial TikTok, for example: Facial patterning and recognition are becoming normalized. A face can be digitized, codified, used for business intelligence purposes, and, don’t get me wrong, used to personalize user experiences in delightful ways. It can also be used by the police and by governments for tracking and monitoring citizens in many countries. The point that researchers and ethicists like Joy Buolamwini and Fei-Fei Li are drilling home is that technology is deeply interlinked with the values and ideals of its data scientists, designers, and developers. Without addressing diversity (not limited to race) in our day-to-day processes, you end up with a system that becomes normalized, and so problematic.

以备受争议的TikTok为例：面部图案和识别正变得标准化。面Kong可以被数字化，编码，用于商业智能目的，并且，不要误会，它以令人愉悦的方式用于个性化用户体验。警察和政府也可以使用它来跟踪和监视许多国家的公民。研究人员和伦理学家，例如Joy Buolamwini和Fei-Fei Li正在钻研这一点，是因为技术与它的数据科学家，设计师和开发人员的价值观和理想息息相关。在我们的日常流程中，如果不解决多样性(不限于种族)，您最终会遇到一个系统变得标准化的问题。

As a technologist, you’re unconsciously trained to segment people into personas and groups based on their behavior or demographics. Segmentation allows for personalization, and personalization is what the layman can and should expect. Technology should meet an individual where they are in their user journey: I co-sign that type of experience 100%.

作为技术人员，您无意识地受过训练，可以根据人员的行为或人口统计将其分为角色和组。细分允许个性化，而个性化是外行可以并且应该期望的。技术应该满足他们在用户旅程中的个人需求：我100％共同签署了这种体验。

Personas are a tool that has always sounded nice in theory, but, if I’m being brutally honest, has not been taken out of my PM toolbox all that often. The impact of well-mapped and deep user personas is not felt immediately by a product team in a for-profit business. If you’ve read up to this point, you should already be able to guess where I’m going with this: Grouping humans based on a logic that is not properly informed will, eventually, result in deepening and spreading systemic inequality.

从理论上讲，角色角色一直听起来不错，但是，如果我实话实说，我并没有经常被带出PM工具箱。营利性业务中的产品团队不会立即感觉到映射良好且深入的用户角色的影响。如果您已经读到这一点，那么您应该已经能够猜出我要怎么做：基于没有适当了解的逻辑对人进行分组，最终将导致系统不平等的加剧和蔓延。

In order for these personas to be truly representative, you, as a technologist, need to have an uncanny ability to question bias and identify your own lazy assumptions and heuristics to understand users better. There are reasonable heuristics or shortcuts you can take when building something, but thought needs to be given to how wide you’re casting the net and doing your research. Testing and validating with only those people around you and those you’re familiar with will result in problematic design. Any and every tech team or data scientist has the bandwidth, if they choose, to visualize the diverse spectrum of target users. We choose not to. It’s easier not to.

为了使这些角色真正具有代表性，作为技术专家，您需要具有不可思议的能力来质疑偏见并确定自己的懒惰假设和启发法，以更好地了解用户。在构建某些东西时，可以采取一些合理的试探法或捷径，但是需要考虑到铸造网络和进行研究的范围。仅与您周围的人和您熟悉的人进行测试和验证会导致设计出现问题。每个技术团队或数据科学家(如果愿意)都有带宽可视化目标用户的各种频谱。我们选择不这样做。更容易不去。

A thousand shortcuts without a breadcrumb trail behind you can lead you too far off the path.

一千条没有面包屑痕迹的快捷方式可能会使您走得太远。

Normalizing diversity on a macro level requires that it be normalized the way our technology is designed, given how deeply digitized our lives are. This is the level of influence that technologists hold. Race and diversity issues need to be decoupled from being solely an HR/marketing initiative and become a design imperative. There will never be enough space made for this if product teams don’t lead on this front.

在宏观层面上规范多样性需要考虑到我们生活的数字化程度，因此必须规范我们技术的设计方式。这是技术人员所具有的影响力水平。种族和多样性问题需要从单纯的人力资源/营销计划中脱颖而出，并成为设计的当务之急。如果产品团队不领导这方面，将永远不会有足够的空间。

If you’re reading this and work in tech, think about the side of history you want your product to be on. Think about the part of the future you’d like to help shape. Don’t reduce your work to being a forgettable app that takes up space on phones. The world needs smart technology that automates all the functions of a society we actually want to live in.

如果您正在阅读本文并从事技术工作，请考虑一下您希望产品继续存在的历史。考虑一下您希望帮助塑造的未来部分。不要将您的工作减少为一个会占用手机空间的易记应用程序。世界需要能够使我们实际上想要生活的社会的所有功能自动化的智能技术。