How to combat AI bias?
As we’ve established in our article on AI bias in general, it’s a fact that every AI network is, to varying degrees, biased. It automatically stands in opposition to the principle that is a basis for creating many algorithms and AI software - for them to be unbiased when we humans cannot be and make objective, unprejudiced decisions in crucial areas.
The question remains whether we can eradicate AI bias when it’s virtually impossible to do that among humans. Humans create AI, choose the (socially generated) data for its training, and unintentionally embed biases. The picture that these facts paint for the future of AI is a bit grim, so we’ve decided to present a few ways and methods to minimize and hopefully someday eliminate it.
What can we do to achieve an unbiased AI?
Understand your training data.
Reviewing the training data is pivotal. The academic and commercial datasets are the primary cause of bias in AI algorithms because they were gathered for a specific purpose and are often categorized with classes and labels that instigate the bias. Studying the data set that will be used to train our AI models is essential. By understanding what part of the dataset is small or defective, and where information is missing because of data classification, you can prepare extra measures to avoid bias.
A diverse team + more diverse data = progress.
When you understand your data, you can increase the amount of data related to the topic. Making unbiased decisions about the data in a non-diverse team is almost impossible. Each person brings to the table different experiences, viewpoints, and ideas. People from diverse backgrounds – ethnicity, gender, age, personal history, culture, etc. – will inherently ask specific questions and interact with the model differently.
Gather as much data as possible - the more, the better.
To achieve data sets as extensive and diverse as they come, gather the data from multiple sources. Creating principles for checking and understanding your source data is the next step.
Variety of algorithms and approaches for training AI models.
Bias can be caused not only by the input data set but also by some algorithms that can favor particular solutions, choices, etc. It’s vital to train your model with various algorithms and approaches to help reduce the bias that a single algorithm or method may cause. Furthermore, regular evaluation of the AI model’s performance ensures it is not exhibiting any biases.
Feedback is invaluable.
Testing and deploying should be carried out with feedback in mind. Models are rarely static for their entire existence. It’s a frequently made mistake to deploy the model without a way for end-users to provide feedback on how the model applies to the real world. Creating a forum for feedback and a way to maintain a discussion will ensure that the AI maintains optimal performance levels for everyone. Furthermore, it’s necessary to have a concrete plan to improve your model with the received feedback. Review the model using client feedback and independent individuals auditing for changes, edge cases, and instances of bias that might have been overlooked.
Can AI itself be a part of the solution?
Well, training another model to look for bias in our model is certainly an idea.
In your opinion - how great of a challenge can it be?
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