Are A.I Written Novels Biased Towards Certain Topics?

Artificial intelligence has come a long way in the past few years. It is no longer just a concept in sci-fi movies, but it has become a part of our everyday lives. A.I technology is now able to perform various tasks with high efficiency. One of these tasks includes writing books. Although it sounds incredible, machines these days are able to write entire novels without any human input. However, one may wonder about the contents of these novels. Are they biased towards certain topics?

How A.I written novels work

Before we dive into the topic of bias, it’s essential to understand how A.I written novels work. These novels are created using a technique called “deep learning.” It is a subset of machine learning, in which the machine can learn how to perform tasks without the need for explicit instructions. The process begins by feeding the machine-learning algorithm with a vast amount of data. Afterward, the program analyses the data and creates its internal model, which is later used to generate new text. A.I written novels are produced by using generative algorithms, which means that the machine creates original content through a step-by-step process.

Are A.I written novels biased?

Now, let’s address the elephant in the room, bias. It is a fact that prejudices exist in human writing, but what about A.I written novels? Do they have biases too? Well, the answer isn’t a straightforward one. The bias in A.I written novels is not the result of intent but rather a reflection of the type of data that algorithms are using. Since A.I written novels use deep learning algorithms, they rely heavily on the data that is being fed. If the data used is biased, the generated content will be biased too.

Examples of bias in A.I written novels

There have already been many examples of bias in A.I written novels. For instance, in 2017, an American researcher named Joanna Bryson tested a language-generating algorithm that was trained on text from a huge corpus of books. She found out that the machine had developed significant biases, including gender and racial biases. The A.I created sentences that were sexist, racist, and downright offensive. Similarly, another study also found that A.I algorithms trained on a corpus of books show preferences for specific professions. For example, the machine would more often mention men in the context of “doctor” or “engineer,” while women were associated with “nurse” or “secretary”.

The need for responsible A.I

The examples mentioned above highlight the need for responsible A.I development. Machine learning algorithms should be trained on diverse and unbiased data to prevent the perpetuation of stereotypes and prejudices. The A.I systems should also be designed to identify and mitigate any biases identified in the data set. To build a more diverse and inclusive world, developers need to take responsibility for their technologies. The lack of diversity in the tech industry perpetuates bias, and therefore, a diverse workforce is paramount. Adding more diversity to the development team will ensure that the A.I models are developed responsibly, without prejudices and stereotypes. At present, many researchers are working to create machine learning algorithms that are free of biases. There is a long road ahead, but it is nonetheless encouraging to see that there is a growing awareness of the issue and an active effort to address it.

Conclusion

In conclusion, A.I written novels aren’t inherently biased; the data used to train the algorithms that generate content dictates this behaviour. However, if the source data is biased, then the content produced by the machine will be biased too. The development of responsible A.I technology should be a collective effort to achieve unbiased and inclusive outcomes. It is crucial to ensure that the content produced by A.I reflects the diversity within our society and the values that we hold.