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In a relatively short time, artificial intelligence (AI) has integrated into our everyday lives.
The use of AI does not stop there.
Nonetheless, AI systems have the ability to behave in a biased manner towards end-users.
However, humans are also vulnerable to biases.
The challenge lies here.
With lower costs and faster execution, AI is an attractive choice for businesses managing largecustomervolumes.
VP of Applied AI, Onfido.
AI biases frequently lurk within extensive datasets, becoming apparent only after untangling several correlated variables.
These reports should be based on real productiondataand not on synthetic or test data.
This lays the foundation for rigorous bias treatment and increases the value of the algorithm and its user journey.
Let’s take the example of facial recognition between biometrics and identity documents (“face matching”).
This step is key in the user’s identity verification.
It is tempting in these conditions to conclude that by design, the system penalizes people with dark skin.
This decrease in document quality explains most of the relative poor performance of facial recognition.
It is indeed difficult to compensate for this bias afterward without resorting to ad-hoc methods that are not robust.
The datasets used for learning are the main lever that allows us to influence learning.
By correcting the imbalances in the datasets, we can significantly influence the behavior of the model.
Let’s take an example.
Some online services may be used more frequently by a person of a given gender.
We can correct this bias by sampling the data of each gender equally.
Online identity verification is often associated with critical services.
There is no single answer to this question.
Each use case requires its own reflection on the field of utility.
In an ideal world, this normalized rejection rate would be 1 for all groups.
Striving for perfection hinders progress
It is not possible to completely eliminate bias.
Research on bias is widely accessible with numerous publications on the topic available.
For instance, last year Metas release of the Conversational Dataset, focused on bias analysis in models.
As AI developers continue to innovate and applications evolve, biases will surface.
By implementing effective measures to mitigate bias, companies can ensure ongoing improvements in customers' digital experiences.
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The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc.
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