Unveiling Bias in Generative AI: Sources, Impact, and Solutions

AI Recap: LLMs and Generative AI

Generative AI has become integral to our daily lives in ways we can’t imagine. It has various applications and it’s been expanding as fast as we can imagine. Whether generating textual content with visuals, a voiceover of your favorite actor, generating a visually appealing video, or anything else, Generative AI has your back.

Generative AI is a type of Artificial Intelligence (AI) that can individually create new content and ideas, including conversations, images, videos, music, and whatnot. It is powered by learning models popularly known as Foundation Models or FMs.

After learning what generative AI is, let’s divert our focus to what bias in Generative AI means.

Understanding Bias in Generative AI

Bias in Machine Learning or Generative AI arises because humans are involved in training these systems. This involvement can influence the AI algorithms, leading to distorted outputs and potentially harmful outcomes. Bias can be categorized into many types:

  • Algorithm Bias: Algorithmic bias refers to the systematic error or unfairness in an algorithmic-making process that prefers or gives chances to people of certain groups or races. It can occur due to various factors, including inherent biases in the historical data used by the hiring algorithm. If this data is biased toward a certain group of people, the algorithm will inevitably develop similar biases over time.

  • Linguistic Bias: Occurs when the LLM model favors a particular culture's language/semantic styles over others. This can result in AI generating content specific to that culture and language while alienating others. To avoid this bias one must ensure that the AI model remains linguistically neutral and avoid preferring the language styles of certain cultures.

  • Availability Bias: is a type of bias that occurs when AI models are exposed to a large amount of publicly available data. It means that AI will prefer more readily available content and neglect less prevalent online content.

  • Selection bias: Selection bias is the bias that occurs when you try to rely on data that isn’t large enough and is focused only on a particular population or target audience. This can lead to AI being untrained or under-trained as the machine learning model provided to train them is not large enough.

  • Confirmation Bias: This bias generally happens when the AI relies too much on pre-made beliefs or trends provided in the datasets. It can lead to answers that are not wide-scoped and usually close to the beliefs that the Generative AI believes to be correct irrespective of the fact.

  • Cognitive Bias: refers to the concept of deviating from what is the norm, whereby individuals (in our case AI) create their subjective reality that is far away from reality. It can be closely related to confirmation bias for a better understanding. This type of bias can be observed due to several factors such as bias in Training Data, Confirmation Bias, and Modeling Bias.

How to avoid this bias?

Not every AI can be perfectly crafted even after following suggested tips and tricks. One can only take precautions just to avoid common pitfalls, in this section we will discuss some common pitfalls that one can avoid disaster/bitter consequences:

  • Selecting the correct model: This step includes selecting the model that closely relates to your specific needs i.e. TTS model for speech synthesis, the Text to Text model for generating text content, and the Image diffusion model for generating images via AI. Selecting the correct model can mitigate half of the risk because the dataset is fairly specialized and it cannot hallucinate or become biased on data it does not have.. Although this isn’t a proper way of doing it. It’s a step in the right direction.

  • Train with Right Data: Training the model with the right data and balancing it with the things to avoid can improve the results specifically. You can’t be wrong with the thing you don’t know about. An AI trained only on good code can only judge bad code effectively if it has also been trained on bad code. Without both types, it cannot distinguish between them. Therefore, it should be trained on specific data types before evaluating similar data.

  • Continuous monitoring: This is the step that everyone can follow regardless of their technical awareness. Although no close monitoring can make the thing foolproof. It can decrease the chances of common mistakes and help speed up the working of your AI model.

  • Train with a diverse set of data: Diversifying the datasets can help improve the correctness and fairness of the data so produced. This can help eliminate Selection and Linguistic bias as these types of bias generally occur when the data is limited to a specific language or a group of people.

Conclusion

Generative AI can create diverse content but risks perpetuating biases from its training data. Addressing these biases is crucial for ethical use. Choosing the right model for specific tasks can minimize bias. For example, using TTS for speech or text generation for content creation helps ensure the AI remains accurate. Training with balanced data that includes both positive and negative examples reduces biased judgments. Continuous monitoring of AI outputs is essential to identify and correct biases, maintaining fairness and alignment with ethical standards.

Diversifying training data by including sources, languages, and cultural contexts helps create more inclusive models and prevents favoritism towards any particular group. While no AI can be entirely free of bias, selecting appropriate models, training with diverse and balanced data, and continuously monitoring outputs can significantly mitigate these issues. These steps ensure that generative AI remains a fair, inclusive, and ethical tool for innovation.

Manish Pamnani

Software Engineer

Published: Jun 11, 20242 min read

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