To what extent can we trust Artificial Intelligence (AI)? This is a question that many people still ask, because although there is no doubt that this technology is already transforming society, it still raises a series of questions and challenges that must be resolved as the months and years go by.
In any case, many of them are not even really new, because one of the consequences of creating AI in the image and likeness of the human being is that it inherits some of its problems. For example, bias, since like people, the algorithms behind their processes can be discriminatory and biased.
Being aware of its existence is essential to make ethical and appropriate use of this technology, so today we are going to explain what Artificial Intelligence bias is and give you several keys to try to avoid it in content marketing.
Can AI be biased?
Although it may seem surprising, the direct answer is yes. And even more so at a time like the current one, in which Artificial Intelligence is still in a very incipient phase of development and raises more doubts than certainties. Hence, associating innovation and technology with reliability and dependability is a mistake that can end up paying dearly.
Recently, a study revealed that the most famous AI application of the moment, ChatGPT, does not know 20% of the Spanish lexicon and makes errors in the remaining 80%. A circumstance that can cause incorrect responses from this chatbot when asked to analyze the meanings of words.
Although this is not, by any means, the worst consequence that misuse of this technology can bring: there are proven cases of failures in the clinical field when trying to diagnose COVID-19 in patients, of resounding errors in predictions real estate and even racist or misogynistic behavior. In short, examples that invite us not to leave our business or our lives in the hands of AI (yet). Or, at least, not to do it without adequate supervision.
What is Artificial Intelligence bias and why does it occur?
Understanding the bias of Artificial Intelligence is easier if we first understand how this technology works. Specifically, it uses computing to create a set of instructions or algorithms that are capable of performing complex tasks that normally require abilities linked to intelligence, such as learning, reasoning or perception.
However, just as the human brain is shaped during its first years of life and acquires its own cognitive biases due to a series of emotional, moral and social factors, Artificial Intelligence also relies on everything it has previously learned to process information. information and offer their answers. This is why the training process of these systems has become a key element for their solutions to be legal, ethical, robust and, by extension, reliable.
We are referring to a complex area of work that includes various learning processes: automatic (supervised and unsupervised), reinforcement, deep… But which, in any case, is strongly influenced by the characteristics of the information that is used to develop the AI. Mainly because, if the databases used for learning present some type of discrimination, the system will inevitably acquire a biased view of reality.
Consequently, working with an application that is affected by Artificial Intelligence bias can lead us to situations such as those already mentioned: from obtaining erroneous results to receiving morally inadmissible answers. Or something even worse from a social point of view, such as harmful indoctrination, since it has been proven that AI’s own biases can end up being transmitted to people.
What are the biases that Artificial Intelligence can have?
For what has already been mentioned, the people in charge of training and working with Artificial Intelligence have an enormous responsibility. In fact, it is important that they know that they themselves can end up creating them without realizing it, due to their own psychology or due to bad practice.
These are the types of Artificial Intelligence biases:
Conscious biases
The person who selects the data or develops the algorithm intentionally incorporates biases.
Unconscious biases
The person selecting the data or developing the algorithm introduces bias without intending to do so. This can occur in several ways:
Sample biases: The data used to train the AI does not adequately reflect reality or does not take all aspects of reality into account.
Exclusion biases: some characteristics are eliminated from the data entered because they are believed to be irrelevant. This may be due to pre-existing beliefs or lack of knowledge of the topic, so that the sample ends up presenting a bias.
Psychological biases: both the person analyzing or auditing the data and the person developing the algorithm can be unconsciously influenced by some type of personal bias. Which can lead to biases based on race, gender or social class.
Biases in measuring or obtaining data: the simple action of collecting data can induce bias, since making errors in the process or doing it inappropriately has consequences on the samples.
Context bias: cultural, temporal and geographical environments also have their own impact on the sampling used for learning Artificial Intelligence.
Reasons why AI bias is important for companies
Based on everything discussed, we can say that Artificial Intelligence inherits some human traits from its creators and developers. And, therefore, companies that work with this technology must take extreme precautions when they incorporate it into their activities, in order to make ethical and lawful use.
It is logical, therefore, that according to a study, 65% of managers claim to be already aware of the risk of discrimination involved in using these solutions. Or that, according to other work by DataRobot in collaboration with the World Economic Forum, 36% of companies recognize that their business has been negatively affected by AI bias.
Faced with these risks, companies must work to eliminate the existence of prejudices and discriminatory behavior both in the processes linked to the development of Artificial Intelligence and its use. This includes having an active concern to ensure that the algorithms are well optimized to offer reliable and accurate information, that quality data is used for their training and, ultimately, that appropriate use is made of the content generated by these applications.
How to avoid the bias of Artificial Intelligence in Content Marketing?
According to Hubspot’s report on the state of AI in 2023, 48% of companies claim to take advantage of the benefits of this technology to create content. And, within them, half of the professionals who use it use it to generate new texts and affirm that they do not need to make many changes before publication.
This demonstrates high reliability in the material generated, an optimism that is justified if the content marketing professional applies a protocol to avoid bias. Which may include measures such as the following:
Good knowledge of Artificial Intelligence
Understanding how Artificial Intelligence works and all its implications is a great starting point. If the professional has adequate training to work with this technology and is aware of its problems, such as the aforementioned bias, he will be able to take steps to make good use of its tools.
Employ algorithms that have been previously audited
As a result of the problem posed by Artificial Intelligence bias, the role of the algorithm auditor has gained importance. This is a professional who analyzes AI algorithms to confirm that they are transparent, diverse and fair, thus preventing them from having a negative impact on people.
Determine the origin of the data with which the AI has been trained
It is important to use applications that make clear the origin of the data that has been used for training. These must be truthful, diverse and of quality, in addition to not containing information from works that are protected or have copyright (unless the developers have prior consent).
Control of unconscious biases
Without realizing it, content marketing professionals may be influenced by their own biases when working with AI. So, it is important that you have the ability to identify and control them, so that they are not projected into the final result.
Use the correct prompt
In the context of AI, the prompt is the set of instructions or requests that, through code or natural language, we use to make the Artificial Intelligence complete the tasks we want. Therefore, if in our request we indicate that we want to generate content with “diverse, inclusive and respectful” characteristics, the application itself will make an extra effort to please us.
Ensure content that respects privacy and data security
The work of generating AI content must be carried out within a framework of respect for data privacy and security. In this regard, compliance with regulations is essential to prevent illegal use of information or security risks that could leak or expose clients’ confidential data. Thus, companies must implement measures to guarantee the protection and responsible management of all the material used when interacting with these applications.
Review of the result
As much as we trust the Artificial Intelligence we are using, it is recommended that we spend time reviewing its results. Doing a final review can help us polish the content we have generated and avoid biases of any kind. Especially if a large and diverse group of people participates in this process.
Eliminate AI bias for quality content marketing
In short, the bias of Artificial Intelligence is an aspect that must be kept in mind in content marketing and that companies should not underestimate in order to offer reliable, transparent, diverse, inclusive and, therefore, quality content.