Smart algorithms are only as good as their training data sets. As such, it’s not surprising that algorithmic bias (or Bias in Artificial Intelligence = AI Bias) increasingly pops up when Artificial Intelligence (AI) and Machine Learning (ML) models go into production.
AI bias is dangerous because it could easily lead to poor decisions with disastrous consequences. I’m sure you have come across examples of AI bias in the news, like AI’s inability to recognize minorities and so on. So, it’s not hard to imagine businesses finding themselves in a legal nightmare.
How do you overcome or prevent AI bias?
Unfortunately, eliminating AI bias is challenging, and we must accept that we can’t stop it entirely. However, we can reduce bias by taking proactive steps to prevent it. The first step in this process is understanding how training datasets can help generate and evolve AI models.
It’s important because research suggests that we’re severely lacking when it comes to highly inclusive and diverse datasets. For example, as many as 24% of companies surveyed reported that it was mission-critical to enable access to unbiased, diverse, global AI datasets.
How Drives AI Bias?
AI is supposed to intervene whenever it detects human bias. So, it’s natural to think that smart algorithms are unbiased. But you would be wrong, very wrong!
Both AI and ML models are created by people and often train on socially trained datasets. So, there is always a risk of existing human biases creeping into ML models and amplifying the negative consequences that come along with it.
ML algorithms analyze historical data tables and produce a training model. Once created, a new row of data is fed into the model, returning a prediction. For example, you can train a model on automobile transactions and then leverage the model to predict future sale prices of unsold vehicles remaining in the parking lot.
The problem with this process is that it creates a “black box” problem. This is because while ML models can make highly accurate predictions, they are unable to explain the reasoning behind the predictions that are incomprehensible to our human mind. Instead, they just provide a score that reflects its confidence in the prediction.
We must all understand that algorithms can’t think beyond the data used to train them. So, if you leave bias in the training data unchecked, you can bet it will be present in predictions. Organizations will also have to contend with a vicious circle of bad decisions that compound over a period of time.
Responsible organizations must understand the issue and take proactive steps to prevent or minimize AI bias. After all, most AI initiatives will ultimately crash and burn without any effort to reduce algorithmic bias.
What can we do to mitigate the risk of AI bias? Here are the top nine ways to eliminate or prevent AI bias.
1. Define Your Business Problem, and Narrow it Down
Whenever businesses try to solve issues related to multiple scenarios, it often leads to failure. This is because you must have a massive number of labels across several classes that will quickly become unmanageable.
To minimize or prevent this situation from occurring, it’s best to narrowly define the problem you want to solve with AI. It’s also vital to ensure that your AI model performs well and does precisely what it was built for before unleashing it in real-world situations.
2. Always Utilize Structured Data
For AI to truly have an impact, smart algorithms must train on diverse and inclusive datasets. At present, we collect enormous volumes of real-time data for AI models. They come in three forms—structured, unstructured, and semi-structured.
Enterprises must only train algorithms on structured real-time data to eliminate AI bias. This is because structured data allows different opinions that help create more flexible AI models. For example, there could be many valid opinions (or labels) for a single data point. By gathering and accounting for all legitimate (but often subjective), differences of opinion will help make the model flexible.
3. Understand and Use Suitable Training Data
When companies introduce appropriate training data into AI data models, smart algorithms have a better chance of learning from completely diverse training datasets. It’s important as data with multiple classes come with the risk of introducing bias into both AI and ML models.
We can help you to prevent AI Bias with your own comprehensive and representative ML Training Datasets.
4. Build a Diverse ML Team That Asks Different Questions
To ask a diverse range of questions, you need a diverse and highly reprehensive ML team of engineers with different experiences and ideas. This makes it important to build an ML team from diverse (geological and economic) backgrounds, ages, genders, races, cultures, and so on.
Whenever companies do this, they can ask inherently different questions and interact with AI models in vastly different ways. This approach helps ML engineers identify bias-related issues with the model before it goes into production.
5. Put Your Target Audience First
It’s also vital to understand that your end-users will be different from your in-house ML team (even while championing diversity). It’s safe to say that you won’t be able to adequately represent the target audience who boasts a whole host of different preferences, experiences, location-specific cultural influences, and much more.
As such, it’s crucial to have a solid and in-depth understanding of your end-users to train intelligent algorithms and derive unbiased insights. In this case, ML teams must anticipate how people who are different and unlike them would interact with the application.
6. Annotate with Diversity
A diverse team of human annotators will help embed more varied viewpoints. This approach can dramatically reduce AI bias once you launch the application and beyond if you continuously train and retrain AI models.
7. Continuously Monitor Training Data
You can quickly look for loopholes, weaknesses, and areas that demand improvement by monitoring performance data in real-time. In this scenario, the performance of AI models will depend on various factors that can potentially introduce bias. So, monitoring is critical to eliminating bias.
8. Test, Deploy, and Pay Attention to Feedback
Most AI models don’t remain static throughout their lifecycle. Often, companies make a massive mistake by deploying AI models without allowing end-users to provide feedback on how the model is applied in real-world situations.
Testing and closely listening to end-user feedback are key to maintaining optimal performance levels for the target audience.
9. Devise a Robust Plan to Improve AI Models Continuously
Once an AI model is in production, ML teams must have a plan in place to seamlessly review and improve by listening to user feedback, edge cases, independent audits, or any potential instances of bias missed during the development phase.
ML teams must also get feedback from the model and provide it with feedback of their own to boost performance. By constantly tweaking the model, they can iterate toward more accurate predictions.
As you can see from the above, to unlock the power of total automation and drive real change, we need to better understand how AI biases are created, understand the far-reaching consequences, and take proactive steps to avoid them.
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