At this stage, majority of us have either heard about AI, used AI or come across AI in some way regardless of the industry that we’re involved in. However, as we continue to embrace AI, a concern regarding bias becomes more prevalent.
Understanding AI Bias
Put simply, AI bias occurs when an AI algorithm makes a prejudice decision or gives a prejudice answer due to a flaw in the machine learning process. As mentioned in our article, AI Ethics, AI algorithms are only as good as the data used to train them. Therefor if the data used is biased or inaccurate, the AI’s outputs will reflect that.
Sources of AI Bias
1. Data Bias: As mentioned above, the quality and quantity of the databases used to train AI algorithms play a vital role. If the training data is of poor quality, or limited data sets are used, the AI’s outputs are likely to be bias.
2. Algorithmic Bias: AI bias can be the result of the algorithm itself. Certain decisions made through the algorithm design process, such as selecting certain features or valuing certain inputs over others, are likely to introduce bias into the algorithm. Even if the training data used was not bias, if the algorithm was developed to favour certain data, the results will be bias.
3. Human Bias: Naturally, as humans we each have our own views, opinions, ideas and objectives and all of these can find their way into our work if we aren’t careful. When it comes to a team of people developing an AI system, each person involved can influence the design, development, implementation and evaluation of the AI algorithm and if those influences are biased, the outputs from that AI system are likely to be as well.
How to decrease the chances of AI bias
1. Use diverse datasets in AI training.
2. Conduct regular bias audits to continuously identify and rectify any instances of bias that come up.
3. Encourage users to identify and report any instances of bias through pre-set feedback mechanisms.
Bias in AI is a present concern in today’s world and can quickly become out of hand if left unchecked. However, if we continuously work towards reducing the chance of AI bias through using diverse datasets, conducting regular audits and encouraging users to identify and reports bias, we can work towards developing an AI system that is fair.