As companies race to take advantage of the innovative potential that Generative AI has to offer, it’s important to remember the responsibility of upholding ethical and fair practices, preventing the generation of harmful, biased, or misleading content. Join Caylent’s Brian Tarbox as he explores some key ethical considerations that organizations should be aware of.
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Brian is an AWS Community Hero, Alexa Champion, runs the Boston AWS User Group, has ten US patents and a bunch of certifications. He's also part of the New Voices mentorship program where Heros teach traditionally underrepresented engineers how to give presentations. He is a private pilot, a rescue scuba diver and got his Masters in Cognitive Psychology working with bottlenosed dolphins.
View Brian's articlesCompanies across the world are racing to take advantage of the innovative potential that Generative AI has to offer. However, with these newfound capabilities, comes the responsibility to uphold ethical and fair practices.
Making efforts to account for ethicality in generative AI prevents the generation of harmful, biased, or misleading content, and safeguards against misuse that could perpetuate misinformation, discrimination, or unethical behavior. By promoting responsible AI development, we can harness the potential of generative AI while upholding ethical values and social well-being. There are several different aspects of ethics that we need to look at.
The first is attribution. There are already lawsuits from independent artists accusing companies of using their creative artwork on models without giving them credit. These lawsuits may result in organizations having to pull back said models, highlighting the importance of attribution. This is one area where Amazon excelled at with their AI product, Amazon CodeWhisperer, that generates code based on other code, but if you're using open-source code, it will tell you the attribution that you should put in your code.
The other big area for ethics is bias because all ML models are essentially extrapolating from their training data. If the training data is biased, then the outcomes will also be biased. There are many different areas that generative AI is going to be applicable to, underscoring the awareness of our biases. It is important to ensure all the data used to build the model is included so that when someone else from a different culture, background, or perspective uses the model to generate a response, they will be getting a valid output because the inputs were valid.
We hope this provides you with a glimpse into the different areas of ethics to consider in the AI realm. Are you exploring ways to take advantage of Analytical or Generative AI in your organization? Partnered with AWS, Caylent's data engineers have been implementing AI solutions extensively and are also helping businesses develop AI strategies that will generate real ROI. For some examples, take a look at our Generative AI offerings.
Brian Tarbox