To implement fair machine learning in a sustainable way, identifying the right fairness definition is key. However, fairness is a concept of justice , and various definitions exist. Some of them are in conflict with each other and there is no uniformly accepted notion of fairness . The most appropriate fairness definition for an artificial intelligence system is often a matter of application and the right choice depends on ethical standards and legal requirements. In the absence of officially binding rules, the objective of this documentis to structure the complex landscape of existing fairness definitions. We first explain the different options by example. Furthermore, we propose the "Fairness Compass", a tool which formalises the selection process and makes identifying the most appropriate fairness metric for a given system a simple, straightforward procedure. We argue that documenting the reasoning behind the respective decisions in the course of this process can help to build trust from the user through explaining and justifying the implemented fairness.
Fairness is a concept of justice . Various definitions exist, some of them conflicting with each other . In the absence of an uniformly accepted notion of fairness , choosing the right kind for a specific situation has always been a central issue in human history. When it comes to implementing sustainable fairness in artificial intelligence systems, this old question plays a key role once again: How to identify the most appropriate fairness metric for a particular application? The answer is often a matter of context, and the best choice depends on ethical standards and legal requirements. Since ethics guidelines on this topic are kept rather general for now, we aim to provide more hands-on guidance with this document. Therefore, we first structure the complex landscape of existing fairness metrics and explain the different options by example. Furthermore, we propose the "Fairness Compass", a tool which formalises the selection process and makes identifying the most appropriate fairness definition for a given system a simple, straightforward procedure. Because this process also allows to document the reasoning behind the respective decisions , we argue that this approach can help to build trust from the user through explaining and justifying the implemented fairness.
R
To implement fair machine learning in a sustainable way, identifying the right fairness definition is key. However, fairness is
Fairness is
R
, and various definitions exist. Some of them are in conflict
. Various definitions exist, some of them conflicting
R
and there is no
. In the absence of an
R
. The
, choosing the right kind for a specific situation has always been a central issue in human history. When it comes to implementing sustainable fairness in artificial intelligence systems, this old question plays a key role once again: How to identify the
R
definition for an artificial intelligence system
metric for a particular application? The answer
R
application and the right
context, and the best
R
In the absence of officially binding rules, the objective of this documentis to
Since ethics guidelines on this topic are kept rather general for now, we aim to provide more hands-on guidance with this document. Therefore, we first
R
definitions. We first
metrics and
R
metric
definition
R
We argue that documenting
Because this process also allows to document
R
in the course of this process
, we argue that this approach