Artificial intelligence (AI) offers a great deal of potential for most industries in terms of improving the efficiency of operations and creating new opportunities altogether.
However, the technology also introduces challenges, one of which is the implicit biases that it may reinforce. These biases stem from the data that is used to train them, the way in which they’re designed and even all the assumptions that are made during the development process.
Here are some of the most common and problematic types of biases that may affect AI systems.
Data Bias
If the data that is used to train the AI programme doesn’t properly represent the entire population or situation in question, then it’s most likely that the AI will produce biased outputs.
For instance, you may have a situation in which the data given to an AI system mostly features people of a specific ethnicity or from a certain area. If this is the case, it’s likely that the system won’t do a good job when applied to people of different ethnicities and from other areas.
This can result in discrimination as systems that aren’t able to properly recognise diverse user groups may end up producing inaccurate results for underrepresented data points which is problematic for obvious reasons.
Algorithmic Bias
Some types of algorithms are structured in such a way that certain outcomes end up being favoured, whether that’s because of the logic of the algorithm or the way in which it weighs input features.
It’s possible that if you’re using an algorithm that’s designed to predict something like creditworthiness, it may rely on factors that already inadvertently disadvantage some demographic groups. The implication is that historical inequalities are reinforced and perpetuated.
Confirmation Bias
In some cases, the data that is used by an AI programme ends up reinforcing existing assumptions and beliefs which results in the AI being more likely to produce outputs that align with those preconceived notions.
So in the case of a news programme, the AI-powered recommendation system that shows users content similar to that which they’ve enjoyed before can end up creating a kind of an echo chamber, so all they see is the same views and opinions over and over again.
Many people are concerned that this is what is happening on social media platforms like Facebook, resulting in polarisation and reduced exposure to diverse perspectives.
Exclusion Bias
When certain perspectives or data points are excluded from a data set, whether it’s intentionally or unintentionally, the algorithm’s capacity to understand diverse contexts is limited, resulted in exclusion bias.
Selection Bias
If data that’s used to train an AI programme isn’t selected randomly, you’ll end up with a sample that doesn’t properly represent the population, resulting in selection bias.
The problem with this is that it tends to affect AI’s ability to make accurate predictions, resulting in biased decisions when applied to broader groups than the initially selected data.
Implicit Bias
Implicit bias occurs when the specific features that are chosen to train an AI algorithm have hidden correlations with sensitive attributes, things like race and gender. This tends to leader to outputs that re biased.
For example, if you were to use something like ZIP codes to indicate people’s creditworthiness, the model is likely to introduce racial bias as some regions tend to correlate with specific races and demographics more than others.
Is It Possible to Mitigate Bias in AI?
There are some ways that will help mitigate biases in AI and also mitigate the negative effects that these biases may have:
• Collecting diverse and representative data
• Bias audits and testing
• Oversight by humans
Most people will argue that it’s next to impossible to completely mitigate bias in the world of AI, and that’s true in most contexts.
There are always biases involved in just about anything everything, and while you can limit the impact that they may have, a lot of the time it’s more about being aware of these biases and taking them into account during your analysis.
