Contact Information
Generaal Vetterstraat 66Amsterdam 1059 BW
Netherlands
Social:
Tobias Cummins
Global SVP Clients & Head of DACH
Josefina Blattmann
Global Marketing Manager
Dimi Albers
Chief Executive Officer
Phone: +31 (0) 6 41 24 07 87
Marjan Straathof
Global SVP Marketing & Partner
Phone: +31 6 54713934
Basic Info
Founded in: 2016
Employees: 3000
Awards: 66
Creative Work: 38
Clients: 25
Founded in: 2016
Employees: 3000
Awards: 66
Creative Work: 38
Clients: 25
DEPT®
Generaal Vetterstraat 66Amsterdam 1059 BW
Netherlands
Tobias Cummins
Global SVP Clients & Head of DACH
Josefina Blattmann
Global Marketing Manager
Dimi Albers
Chief Executive Officer
Phone: +31 (0) 6 41 24 07 87
Marjan Straathof
Global SVP Marketing & Partner
Phone: +31 6 54713934
How to prevent bias in your AI-generated content
As humans, we are biased
Where does bias come from?
A little background information on how bias in AI occurs. I asked ChatGPT to tell us more about the topic, as I am obviously no specialist in setting up an LLM (Large Language Model) myself.
This is what it answered:
“Bias in AI content can occur in a number of ways, but it typically stems from biases in the data used to train AI models. Here are a few examples:
- Biased training data: AI models can reflect the biases in their training data if the data is not diverse and inclusive.
- Biased algorithms: Algorithms used to train and deploy AI models can introduce bias, such as facial recognition algorithms that are biased against people of color due to a lack of representative data.
- Human bias: Bias can be introduced by the humans who design, train, and deploy AI models, especially if the team is not diverse and fails to recognize certain biases in the data or algorithms.
- Lack of accountability: Without proper oversight and review, biased AI models may be deployed without anyone realizing the potential for harm.”
How can you prevent bias in AI?
Now that you know how bias in AI arises, it should be ‘rather easy’ to come up with solutions to prevent the occurrence of bias. The main topic is ensuring diversity in training samples, such as audio samples from diverse backgrounds or genders, but there are of course more things you can do (during the training phase).
However, as copywriters, you often don’t influence those. But what you do influence, are prompts and use of output. Let’s put that statement into practice.
My team of copywriters and I asked ChatGPT multiple questions. A while ago, ChatGPT gave rather biased answers (on a positive note: we did receive a disclaimer that the copy could be in violation of the content policy). We’ve noticed that since then, the tool is becoming less and less biased, as its makers of course receive feedback regularly – also about this topic.