Debunking The Bias Myth

Debunking The Bias Myth.


Author(s): Don Kaluarachchi

Originally published on Towards AI.

Separating fact from fiction in AI
Image by Don Kaluarachchi (author)

Artificial Intelligence (AI) has been under the microscope lately — especially AI bias.

Everyone is talking about it, and it seems like the perfect villain for our AI-induced headaches.

But is it really as bad as everyone makes it out to be?

Let us dive into the nitty-gritty of bias in AI — separating fact from fiction.

It should be noted that bias is not a one-size-fits-all term when it comes to AI.

It is not this ominous cloud that hovers over all algorithms, waiting to wreak havoc.

Bias can be good, bad, or just a little misunderstood.

It is like salt in… Read the full blog for free on Medium.

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Published via Towards AI


AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.

Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.

In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.