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Did you know that around 38% of so-called facts generated by artificial intelligence contain bias? In fact, according to recent research, generative AI models tend to lean left-wing, posing a slew of problems in terms of accuracy and professionalism, even if the responses align with your own personal beliefs.
In this article, we explore what AI bias is, why it occurs and the best way to address the issue. Keep reading to learn more.
What Is AI Bias?
AI bias is where artificial intelligence models, like ChatGPT or Claude.ai, produce responses that prejudice toward a particular opinion or against a certain type of view, despite supposedly being impartial.
This bias, however, is not solely confined to generative AI models; it’s also present in models that utilize computer vision for surveillance or facial recognition purposes. For example, one study discovered that artificial intelligence was biased toward white males and unable to identify black females.
Types of AI Bias
Although AI bias may present itself as political, cultural, or ideological, there are several additional systemic types that you might encounter as a result of the way the models are programmed.
Data bias in AI is where the machine learning algorithms that AI models are based on draw incorrect or skewed conclusions due to incomplete, poor, or inaccurate datasets. If a dataset contains several anomalies that cause improper trends and data patterns to occur, for instance, these inaccuracies may be reflected in the AI model’s output, leading to incorrect and problematic results.
Human bias refers to AI prejudice caused by imbalanced and often low-quality training datasets that use human-produced content. For example, if a large language model like ChatGPT was trained on thousands of right-leaning news articles and only a handful of left-wing pieces, it would generate responses that align with the sentiments of its right-wing training data, while only occasionally expressing left-wing views.
This is because generative AI works by predicting the most likely relevant response to user input based on its training data. In other words, human bias in AI occurs when artificial intelligence attempts to reflect the views of humans.
AI algorithmic bias is closely tied to data bias, as it regards how AI algorithms are trained. Essentially, if there is a technical problem with base algorithms or they are trained using low-quality data, it stifles how they think and draw conclusions. This impacts AI models’ responses and how they process new training data, sometimes failing to filter-out inaccuracies and obvious biases.
Why Does AI Bias Occur?
In the above section, we touched on a few reasons as to why AI bias occurs, let’s now dive a little deeper into the causes of AI bias.
Bias Training Data
Bias training data was largely covered in the previous section, so we won’t linger here long. However, it’s crucial to reiterate the importance of using a balanced dataset that expresses the views of those across the political spectrum in order to allow AI models to properly discern between viewpoints and what constitutes bias.
Incomplete Training Data
Using incomplete training data means that many groups of people will be underrepresented while others are overrepresented. It also means that AI models may struggle to fully understand the context of user queries, as they do not have enough information to comprehend user inputs. Consequently, AI models may produce results that adhere to the overarching beliefs of the “majority” with no consideration for the “minority,” making them even more disposed to hallucinations.
AI hallucination is where artificial intelligence models, particularly large language models like ChatGPT and Claude, produce inaccurate responses but state them as fact, often with believable explanations. For example, you might ask an AI chatbot to explain how nuclear fission works and, despite not knowing, it’ll try to give you an answer based on the limited information it can find. In other words, AI chatbots sometimes try to tell you what you want to hear, even when they are uncertain about the answer to your query.
Hallucinations can contribute to biased responses as a result of AI’s understanding of the user. For instance, if an AI model identifies that you have a penchant for dramatic historical events but you ask about a tame epoch of time on which it has limited knowledge, it could embellish its answers to appeal to your historical palette.
How to Address Bias in AI Models When Developing Software
So, now that you understand why AI bias happens and how it works, let’s explore how you can address bias in AI models, whether you’re building your own or trying to get the best out of an existing model.
When asking AI models questions regarding certain factual topics, like World War II, you should feed it several trusted sources from across the web to ensure it can draw accurate information. Using multiple trusted sources allows AI models to use high-quality data and produce a balanced response based on the sources provided.
For example, you might want ChatGPT to produce a summary of a major news event. In this case, it would be considered good practice to provide it with news sources known for their differing political opinions, such as Fox News and The New York Times, as well as a few international news outlets.
However, while some AI models still don’t allow link scraping, you can provide them with text extracts from the news website or use certain third-party plugins. If you’re training your own model, keep in mind that you should first get the original content creators permission before using it in your model–most news websites will now have an AI use policy for you to review.
Use Diverse Training Data
We earlier discussed incomplete datasets and how they can lead to under- and over- representation – and ultimately AI data bias. To combat this, you should use a diverse and large set of training data. This means including content and data produced by a wide range of people from different backgrounds, aiming to make your training data a microcosm of society’s cultural and political makeup. Using a diverse training data set will enable your AI model to draw information from several different perspectives and generate balanced responses.
Verify and Peer Review
If you’re using a third-party AI model, editing the system’s training data may not be an option–not in any significant way, at least. As a result, you should work toward verifying AI responses, ensuring they are both accurate and balanced. You can do this yourself by fact-checking via trusted online sources or by sending responses to peers for review. Generally speaking, it’s best to get another set of eyes on your AI responses, as it’s easy to brush over mistakes when reviewing the same data over and over again. You could also potentially apply your own personal biases without even realizing.
AI Development Solutions From Idea Maker
At Idea Maker, we have a team of expert machine learning, artificial intelligence, and software development experts dedicated to building client projects that exceed expectations. So, if you’re looking for an AI solution for your business with considerations made for bias, you’re in the right place. Schedule a free consultation with us today to learn more.