Even though Artificial Intelligence (AI) has been around for some time now, it’s still considered to be in the early stages of development. That is, indeed, fortunate because there are still a ton of issues that need to be addressed.
One of the major issues is a human bias that makes its way into AI systems whether we like it or not. In most cases, these biases aren’t placed there on purpose. It’s the ignorance of potential negative implications that pave the way for biases to enter such systems, to begin with.
After all, AI with its machine learning and deep learning capabilities doesn’t learn from other systems but from the data we provide it with. A machine doesn’t comprehend human emotions or needs but it comprehends data rather well.
It will act and “behave” as the data it learns from tells it to. That can be a major problem, aside from it being a major advantage and benefit.
Companies are being early adopters of AI technology because of its capabilities and potential. Many business categories embraced AI technology, from marketing to manufacturing software companies and here we can see many businesses are having needs for AI implementation.
Still, AI needs to be checked
We use data to create algorithms that will teach AI to perform various tasks. Companies do this all the time and they leverage the big data they’ve collected over the years to provide AI technology with algorithms it requires to function.
Human biases are oftentimes included in the data we provide AI with and people are rarely aware of this. Let’s take a company that wants to create a hiring system using AI. In the past, this company didn’t hire many members of certain minorities or women.
The reason behind this was not because of their ethnicity or gender but because these candidates weren’t qualified for the given job positions. These records are kept in the company’s datasets and they will be used to create an AI hiring system.
AI doesn’t realize that human biases are already present and it canl disregard minorities and women when selecting future candidates. AI systems, therefore, need to be checked.
AI is choosing the situation
As mentioned before, AI is a machine or software that learns from the data we provide it with. Human bias is omnipresent in all economic as social aspects of our lives. For example, an employer may look at two identically qualified candidates and then decide to check out their credit history.
When it comes to AI and human bias, the data can make AI systems far more biased than humans and even more discriminative than anyone might’ve imagined. On the other hand, AI systems can learn to ignore biases programmed correctly.
For example, AI’s machine learning capabilities allow it to disregard variables that do not accurately predict outcomes. This is contracts to bias human decision-makers who may choose to lie about hiring one candidate over another.
Since AI cannot lie, it can be programmed to neglect bias altogether and focus on outcomes-based on accurate variables.
Underlying data and its importance
The algorithms AI systems use to predict outcomes are not the main source of bias. It’s the underlying data. Social or historical inequities, as well as biased human decisions, can ease their way into data we use to create algorithms for AI systems.
How data is collected and how it’s selected for use may introduce biases to AI algorithms. For data to be improved and biases erased, we must first consider what the data itself contains. We could, for example, probe algorithms for bias potentially revealing issues that have passed unnoticed.
We could also backtrack where these biases originate from to alter the data itself. Therefore, the training data we provide to AI systems need to be analyzed correctly because it’s easier to analyze data than analyze more complex algorithms that have already been implemented in the AI systems.
Now we come to one of the greatest challenges AI developers and software engineers face today, which is defining fairness to avoid biases in AI systems.
Researchers came up with various technical solutions on how to define fairness, such as the necessity for models to have equal predictive value across selected groups or the necessity for models to have equal false positive and false negative rates across selected groups.
This works quite well with minor problems that occur when various definitions of fairness have to be accounted for. Current models are unable to accurately account for all equitable outcomes across different groups and this is a field that needs to be improved more in the future.
Archiving significant technical progress
There are quite a few approaches in existence today that enforce fairness constraints on AI models and systems. One of such models is pre-processing the data to boost informational accuracy. There are also the so-called “counterfactual fairness” approaches that maintain the same decisions in the counterfactual world when sensitive attributes are changed.
In addition, experts are testing out post-processing data approaches where models are transformed after they’ve been made so that they can meet some of the fairness constraints. These are all equally viable models that have proven to satisfy the need to some extent.
However, it seems that researchers are still years away from developing an actual solution to biases in AI systems. The real problem is that who can decide that an AI system is completely bias-free? Experts are wondering how explainability features could potentially lead to more accountability in AI predictions than in human decision-making.
So, there are still a lot of challenges that experts need to overcome before they can create a system that will truly be able to ignore all human biases in AI systems. Until then, we can only minimize or mitigate biases in the AI technology we commonly use today.
No matter how far the AI technology advances it still requires human judgment to ensure Ai supported decision-making is, indeed, fair. That’s precisely why human decision-making standards need to be elevated and improved. It will not only require experts and engineers but also social sciences, law, and ethics, to develop standards so that humans can deploy AI with bias and fairness in mind. If we cannot eliminate biases from our own decisions we cannot hope to teach AI to do so.
Travis Dillard is a business consultant and an organizational psychologist based in Arlington, Texas. Passionate about marketing, social networks, and business in general. In his spare time, he writes a lot about new business strategies and digital marketing for Finddigitalagency.