Test for Social Bias in Training Data

3 / 45 Data This practice was not ranked.
Click to read more.
This practice addresses requirements
from the EU guidelines for trustworthy ML.
Click to read more.


Intent

Identify instances of social bias in training data, in order to counteract the effects of bias in trained and deployed models.

Motivation

Bias in data is one of the main sources of unfairness in machine learning applications. Responsible use of machine learning requires that developers counteract unfairness, starting with identifying the sources of bias.

Applicability

Testing for social bias in training data should be done whenever an application processes data with personal information, or related to users. Note that personal data can have explicit fields for gender, ethnicity, etc, -- but also seemingly innocuous data such as location, name, or even hobbies might implicitly encode social traits.

Description

In order to avoid social bias in ML algorithms, it is imperative to continuously check that the training data is well balanced with respect to social attributes such as gender, ethnicity, or otherwise belonging to a specific social group.

In many cases, other data attributes (such as location or neighbourhood) can be proxies to sensitive social attributes, and may introduce latent bias. Using, or not testing for such latent biases, is a common pitfall called failure through unawareness. A common example is the one day delivery service offered by Amazon, which was biased for race; see Amazon Doesn’t Consider the Race of Its Customers. Should It?.

Social bias can be detected technically – by analysing the distributions of social factors, and avoiding (over-) or under-representation. However, technical limitations may prevent bias stemming from latent factors to be detected.

Therefore, it is important to be aware of technical limitations, and improve the social and human factors that can aid bias detection. For example, in order to strengthen your teams’ ability to detect and remove social biases, it is recommended to build diverse teams, both in terms of demographics and in terms of skill sets.

Related

Read more



3 / 45 Data This practice was not ranked.
Click to read more.
This practice addresses requirements
from the EU guidelines for trustworthy ML.
Click to read more.