What are the implications and solutions to biased algorithms?

偏向算法的含义和解决方案是什么？

Americans make up <5% of the world population. Yet incarcerated Americans make up 20% of the world’s incarcerated population.

美国人占世界人口的不到5％。 然而，被监禁的美国人占世界被监禁人口的20％。

So how do we solve this intensely disproportionate incarceration rate? To make the conviction process supposedly more efficient, US courtrooms have turned to artificially intelligence to calculate the criminal risk-assessment score to evaluate the likelihood that an individual will commit another crime.

那么，如何解决这种极不均衡的监禁率呢？ 为了使定罪程序更加有效，美国法庭已转向人工智能来计算犯罪风险评估分数，以评估个人犯下另一种犯罪的可能性。

A higher score can contribute to a harsher sentence and/or getting jailed before trial. Because AI is trained on past data to make generalizations for the future, it is more likely to label low-income or people of darker complexion as “high risk.” At the Data for Black Lives conference, Marbre Stahly-Butts, the Executive Director at Law For Black Lives, stated that:

较高的分数会导致更严厉的判决和/或在审判前入狱。 由于AI受过过去数据的培训，可以对未来进行概括，因此更有可能将低收入或肤色较深的人称为“高风险”。 在“黑人生命的数据”会议上，“黑人生命法”的执行主任马布雷·斯塔里-巴茨说：

“Data-driven risk assessment is a way to sanitize and legitimize oppressive systems.”

“数据驱动的风险评估是对压迫性系统进行消毒和合法化的一种方式。”

The problem with using AI as a tool to aid life-changing decisions such as prison sentences is that systemic racial biases are further perpetuated rather than addressed at the root causes, such as educational, financial, and employment inequality.

使用AI作为帮助改变生活的决定(例如判刑)的工具的问题在于，系统性种族偏见会进一步长期存在，而不是在教育，财务和就业不平等等根本原因上得到解决。

In order to further understand and mitigate this issue, companies such as IBM have been researching how to implement ethical values into AI that can make “morally correct” choices, free of societal bias to prevent discrimination. But how might this be implemented?

为了进一步理解和缓解此问题， IBM等公司一直在研究如何在AI中实施道德价值观，从而可以做出“道德上正确的”选择，而没有社会偏见以防止歧视。 但是如何实现呢？

1.明确概述我们的价值观。 (1. Clearly outline our values.)

Ethics is defined as “the discipline dealing with what is good and bad and with moral duty and obligation” on Merriam-Webster. We would need to explicitly state what the definition of “good and bad” is in order to implement them into an algorithm.

伦理学在Merriam-Webster上被定义为“处理善与恶以及道德义务和义务的学科” 。 为了将它们实现为算法，我们需要明确说明“好与坏”的定义。

Consistency is critical. However, ethics is subjective — there will never be one moral code that is universally agreed upon.

一致性至关重要。 但是，道德是主观的-永远不会有被普遍认同的道德准则。

2.训练数据集必须消除歧视性偏见。 (2. Training datasets must eliminate discriminatory bias.)

A biased dataset is useful when it reflects experience that can be utilized to make accurate predictions, such as the relationship between a doctor’s experience level and their success in diagnosing a certain condition.

有偏见的数据集在反映可用于做出准确预测的经验(例如医生的经验水平与其在诊断特定疾病中的成功之间的关系)时很有用。

However, this must be distinguished from biased datasets that promote practices based on prejudice, such as the criminal risk-assessment score.

但是，这必须与偏见的数据集区分开来，这些偏见的数据集会促进基于偏见的做法，例如犯罪风险评估评分。

According to Lionbridge, there are many different types of data bias. Here are some of the most consequential ones:

根据Lionbridge的说法，存在许多不同类型的数据偏差。 以下是一些最重要的结果：

Sample bias: when the data collected does not reflect model’s use case, for example, when facial recognition algorithms train primarily on white men.

样本偏差：当收集的数据不能反映模型的用例时，例如，面部识别算法主要针对白人进行训练时。

Observer bias: during the labeling of the data, the observer’s subjective views influence their decisions either consciously or unconsciously.

观察者偏见：在标记数据期间，观察者的主观看法有意识或无意识地影响他们的决定。

Association bias: when a dataset reflects cultural norms, such as when a model is trained on data where all doctors and men and all nurses are women. The model may not “know” that women can be doctors and men can be nurses.

关联偏见：当数据集反映文化规范时，例如在对模型进行训练时，所有医生和男性以及所有护士都是女性。 该模型可能不会“知道”女性可以当医生，而男性可以当护士。

This highlights the changes we must make in our own society. But it is also a warning against using AI to repeat the mistakes of our past.

这突出了我们必须在自己的社会中做出的改变。 但这也警告不要使用AI重复我们过去的错误。

3.问责制。 (3. Accountability.)

How do we know if we are successful in creating an “ethical AI?” Would it ever be possible to create a measurement that quantifies the morality of a system? The answers to these questions are just as philosophical as they are scientific.

我们如何知道我们是否成功创建了“道德AI”？ 是否有可能创建一种量化系统道德的衡量标准？ 这些问题的答案与科学一样，都是哲学上的。

In terms of the algorithm itself, the complexity can mask and discourage further investigation into the reasoning behind a decision. We cannot merely accept the output of AI before it’s proven to be accurate and moral, which is why transparency is crucial.

就算法本身而言，复杂性可能掩盖和阻止进一步调查决策背后的原因。 在证明AI的准确性和道德性之前，我们不能仅仅接受AI的输出，这就是透明性至关重要的原因。

Despite the seemingly objective nature of machines, the dataset will understandably be culled from various biases such as racism, sexism, ageism, etc. because the minds behind the code are just as subject to these biases by mere existence in today’s society. The burden should rest first with the dataset to be truly objective vs. to prove that it is not.

尽管机器看似客观，但可以理解的是，数据集可以从种族主义，性别歧视，年龄歧视等各种偏见中剔除，因为代码背后的思想只因存在于当今社会中而受到这些偏见的影响。 负担应首先放在数据集上，使其真正客观，而不是证明事实并非如此。

🌱可能的解决方案。 (🌱 Possible Solutions.)

There have been efforts, however, to remedy some of the obstacles outlined above, through:

但是，已经做出了努力，通过以下方法来纠正上述一些障碍：

Manually creating diverse datasets with intention (but very tedious and inefficient because people must sift through and record each datapoint).

手动创建具有意图的多样化数据集(但非常繁琐且效率低下，因为人们必须筛选并记录每个数据点)。

Cleaning a training dataset before use that supposedly “equalizes” the data by use of statistical optimization.

使用前清洗训练数据集，可以通过使用统计优化来“均衡”数据。

Software to rate the level of bias in an AI algorithm, however, this brings up further questions on how to hold this additional layer of software accountable.

用来评估AI算法中的偏见程度的软件，然而，这带来了关于如何使这一额外的软件层负责的问题。

💡外卖。 (💡 Takeaway.)

Clearly, there are many obstacles we will face as we navigate the implementation of AI into every pocket of our society — from criminal justice to bank loans. At the end of the day, a truly 100% “moral” machine is unachievable simply because it cannot be defined.

显然，当我们将AI的实施导航到我们社会的每个角落时，从刑事司法到银行贷款，我们将面临许多障碍。 归根结底，仅由于无法定义一个真正100％的“道德”机器是无法实现的。

We must approach this problem from all disciplines, all cultures, and all identities in order to attack this issue that is intertwined in all aspects of our lives.

我们必须从所有学科，所有文化和所有身份中解决这个问题，以便解决这个与我们生活各个方面息息相关的问题。

At the end of the day, we need to identify and reduce discriminatory practices in AI because this will also help us do the same within ourselves.

归根结底，我们需要确定并减少人工智能中的歧视性做法，因为这也将帮助我们在内部做同样的事情。

Do you believe we will ever achieve ethical AI, and is it even a goal worth pursuing? If moral machines were possible, would this ultimately be able to help us identify and combat our own bias?

您是否相信我们会实现道德规范的AI，这甚至是值得追求的目标吗？ 如果有道德机器是可能的，那么这最终将能够帮助我们发现并克服自己的偏见吗？

翻译自: https://medium.com/the-innovation/is-ethical-ai-even-possible-f6bf8b9045f5

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