Understanding Bias in AI and How to Mitigate It
Artificial Intelligence (AI) has integrated into nearly every aspect of our digital world. Its use across all industries like manufacturing and transportation has made it ubiquitous in our everyday lives, simplifying daily routines that make life easier for humans.
However, this abundance in AI brings with it many new issues that need to be resolved before further advancement can continue because it has become apparent that many AI systems are capable of creating bias in their decision-making that can create detrimental impacts on people around the world.
Understanding AI Bias
Bias in AI refers to a model that has been trained using a data set that contains certain patterns or traits that are unintentionally amplified by an algorithm. Once an AI model learns these biases, it will begin to make systematic errors in predictions and decision-making that weigh these unwanted biases into its process.
There are three types of AI biases:
Pre-existing: AI systems can learn bias from training data when that same bias is already present in society. Simple training datasets can contain inherent bias that is not obvious to programmers and can impact groups based on factors like race or gender.
Algorithmic: AI models can begin to develop bias on their own due to poor programming and design that causes the algorithm to weigh certain variables more than others.
Emergent bias: Bias can adapt over time depending on user interaction that begins to skew a model’s weighting abilities. These adaptations can reinforce themselves over time, becoming more likely to exhibit bias.
Bias from AI has been observed many times already since Deep Learning became more prevalent at the turn of the century. Companies like Amazon, for example, tried to implement AI systems into their resume screening process which ended up screening out large numbers of female applicants by mistake in 2018.
The Impact of AI Bias
Left unchecked, bias in AI systems can lead to more serious challenges in the future if we become too reliant on faulty algorithms. With the hiring process for some of the world’s largest tech companies already running into skewed results, similar issues in more important fields like healthcare or criminal justice could create major problems.
This issue is compounded by the large amount of data generated online and AI’s ability to use it for training purposes. A lot of the information on the internet is unverified and can cause machines to rely on inaccurate decision-making.
How AI Bias Occurs
The root of bias in AI can often be found in the training phase where it is usually fed poorly curated data or has made a mistake in evaluating the given information that has not been properly corrected.
Biased training data: Training data that is biased itself will be easy for AI models to learn. This is because an AI model has no preconceptions of human society and will take any information as given truth.
Algorithmic design: Poorly designed algorithms can create unwanted bias by weighing certain variables too heavily in favor of others.
Lack of diversity in development: Diverse training datasets are necessary to avoid bias because they increase the sample size and show AI models how expansive certain sample pools can be.
Mitigating AI Bias
There are many ways that bias can be reduced in AI systems that begin with providing more diverse data during the training phases. This can help AI algorithms expand their understanding of certain concepts and demographics so that they can avoid making routine mistakes.
Explainable AI, a system that attempts to reveal the decision-making process that is normally hidden in a “black box,” can also help programmers and consumers identify how machines determine their decisions. This creates more transparency for the general public and lets everyone know if an AI model is impartial or not.
Regular algorithmic auditing is another way that programmers can refine their systems. By inviting a separate team to monitor the systems, they can help identify unnoticed errors as well as provide debiasing techniques such as unweighting.
The Future of AI: A Bias-Free Landscape
As AI continues to develop at a rapid pace, the need for bias-free AI machines will become more important than ever. AI is everywhere in our modern world and, if left unchecked, could create undue burdens on society as we struggle to break away from any over-reliances on faulty AI models.
Regulations and legal action will undoubtedly play a significant role in the development of the bias-free algorithm, however, their ability to implement legislation could be slower than desired, so it’s important that developers do their best to create as much impartiality as possible before releasing new products and services to consumers and businesses.
