This article was updated in April 2023.
When used well, artifical intelligence can be a useful way to boost your promotional efforts, and provide a more engaging experience for your audience. However, there are also a few things you’ll need to look out for, particularly the formation of unintended bias.
AI technology is not without its faults. Although issues arise with just about any form of new technology, AI’s complexity makes it prone to unique problems. This is particularly evident in the growing issue of AI bias.
AI bias appears when AI technology begins to make judgements based on characteristics that aren’t relevant to the task at hand.
Just as with human bias, this has proven to be particularly common when it comes to demographics such as race, gender, and sexuality. For example, recent studies found that an AI algorithm used by authorities in the U.S. to predict the likelihood of criminals reoffending was biased against African Americans.
This assumption is harmful on many levels, naturally, and can result in more serious issues over the long term. The same study also found that the system underestimated the likelihood of white criminals reoffending, which again has troubling ramifications.
The AI Algorithm
As the inner workings of algorithms like these are not often made available, it’s hard to pinpoint how they went so wrong. However, we can safely assume that the data input into the system has something to do with it.
After all, an AI algorithm can only function based on the information we feed into it. Failing to correctly account for all scenarios is bound to lead to some issues.
For example, if you created an AI chatbot designed to provide fashion recommendations but gave it outdated information to base its judgements on, it wouldn’t be able to fulfill its purpose. While this is a less serious problem, it illustrates the way mistakes are often made with AI.
The main reason AI often ends up working with incomplete (or inaccurate) data is because it can be difficult to obtain well-rounded datasets. Many companies simply use whatever is easy and readily available. They may even fail to include any specific data at all.
Rushed training algorithms can also contribute to the formation of unintended bias. The popularity of AI has resulted in plenty of quick-fix efforts, despite the fact that this technology takes time to develop properly.
Let’s circle back to our earlier example of a customer service chatbot. This kind of AI is typically designed to “learn” from the people it interacts with. Any conversations it has will influence the way it operates. This means that over time, it can end up echoing the biases of its users. In fact, that effect is incredibly likely, unless it’s guarded against from the beginning.
2 Ways to Avoid Unconscious Bias in Your AI
The best way to prevent AI bias is to use comprehensive sets of data that account for as many scenarios as possible. To do that, you’ll want to take a few key factors into consideration.
Employ a more diverse team
A diverse workforce is valuable for many reasons. However, it can be especially important during the production of AI. If your development team is comprised entirely of white men in their forties, for example, you’re unlikely to achieve a well-rounded set of data.
Instead, an algorithm produced by this group may automatically favor those users who are also white, male, and middle-aged. This is because it will only have the experiences of that set of individuals to work from. On the other hand, a diverse team can identify factors and considerations that may never have occurred to the more homogenous group.
In other words, having employees from a wide range of backgrounds makes it easier to produce a technology that is prejudice free. Bringing a more diverse team on board starts with your company’s image. To attract many types of people, make sure everything you do as a brand echoes the fact that you are an inclusive business.
You should also seek to address any unconscious bias in your existing employees as well. Ensure that your hiring team thoroughly understands the importance of real representation in the workforce. After all, this is an issue that expands far beyond the use of AI.
Spend more time developing your technology
If you want to craft a well-rounded AI tool, you have to give the process plenty of time. Don’t rush the production of your data. Instead, carefully consider everything you’ll need to include. By filling in any gaps ahead of time, you can give the software a more complete picture of the particular task it was designed to fulfil.
This process can also help you avoid any bias-related issues, as you’ll be able to account for them ahead of time. Certain AI can even be trained to recognize harmful information, and automatically block or reject it, as well as the individual who input it.
As this is more of a technical issue, it will need to be addressed at your AI’s creation stage. If you’re working with a developer, make sure you ask them to program the AI to deal with biased data appropriately.
When personally crafting your AI using a tool like Dialogflow, you’ll have to spend some time teaching it the correct way to respond to harmful content. Recent developments in Natural Language Processing (NLP) are likely to improve AI in this area moving forwards.
There’s no denying the popularity of AI in content and marketing. However, it’s vital that you put in plenty of thought and effort when crafting your own forms of this technology. Fortunately, taking the time to gather a well-rounded set of data with a more diverse team should help you avoid any bias problems.
Do you have any further questions about AI’s bias problem? Let us know in the comments section below!
I do have a question. You state that a more diverse team should be hired to write the algorithm but I couldn’t find sources on the members of that team and whether they were… diverse enough. Could you cite a link to who they are? Id hate to think you’re making assumptions, that would be poor journalism.
The real question is, are they being biased? Or are you just trying to be PC?
The sad fact is that stereotypes exist for a reason.
Adrian Rainbow on
How is mathematically based profiling a bias?
Yann Leclun on
Terrible article author clearly had no clue how AI works…
John is a blogging addict, WordPress fanatic, and a staff writer for WordCandy. Also he knows jack sh!t about AI.
Tegra, it seems he does not. He specifically stated that these AI algorithms are not given out to the public which is utter bullshit. He also does not seem to know that you can not introduce bias by writing it into the code because Neural networks do not have “Code” They have Matrix Multiplication and Sigmoid functions. The Author literally… does not know what he is talking about. It sounds like he heard some buzz words and felt that would make a great shock piece to sucker people into his blog.
In his defence, you can code ML algorithms to be more or less resistant to biased data based on hyperparameter selection and also depending on how you pre-process the data before training on it can lead to more/less bias – so the code/coder does dictate the level of bias in a model to a certain degree.
(Yes the bare skeleton neural network mathematics is not racist but the actual algorithms/implementations/code in use can enhance biases).
Also I’ve dissented this.
Is it bias when the data supports the decision? Can you show me how a diverse background effects the data?
Wait… don’t AI’s use iterative learning? Something that would–if biased data were introduced–allow those biases to be challenged and adjusted going forward?
Or am I confused about what AI/machine learning tools do?
Lol I sense a lot of bias in the comments. But hey, I’m probably just the first black person to leave one
OK so this article is a little waffley, but the message is spot on.
I am doing a PHD in medical AI/ machine learning and in my field there is a huge problem with racial bias in data.
When you see patient genomes that have been mathematically projected on to a 2D space (to allow for visualisation) you see that European, Asian and African genomes are completely distinct (except for mixed race people linking the clusters) and the genomic diversity of Africans is immense – yet researchers continue to develop medical AI/test drugs etc. based on homogeneous, mostly white European samples – its a big problem! I cant comment on other fields outside of medical AI but it wouldn’t surprise me at all if the same thing is happening – the stats show that racial bias is everywhere whether we like it or not (I’m sure I probably have some unconscious ones of my own).
Listen to what this blogger is saying!!! if you don’t then the future of personalised/genomic medicine and ML in general could end up being really unfair to ethnic minorities. Of course, we need to be more general than this as it is likely that are lots of other biases that are not as obvious to detect. We can’t always change data collection to account for these less obvious biases so instead need to make sure that ML models are trained in a way that reduces over fitting etc.
Bias has become a buzzword…look…the fact is this…the global economy is largely driven by the global market. IF you have a capatalist country that is run by the market, than what you have driving that is the majority of the consumers. That is a big reason why so many things appear racist. A national market along with all the statistics and and information is going to cater to the consumer. Thats how markets make money..cater to the customers….it is an unfortunate fact for those who have different consumer needs…bc the market is interested in money and products and amenities that cater to white people will always be more available..because it takes capital to make capital..and businesses arent going to spend money putting out a product that doesnt give them a reasonable return..AI was made to serve our societal purpose..which in america is to make money and be successful and produce and consume.
well written article..need some more clarification for a few topics,
Here you can read one of machine learning article:
Faster, more accurate iXBRL tagging through auto-tagging and machine learning