Is AI Ethical?
AI is all the rage in marketing. Spurred by one of the fastest growing apps ever, ChatGPT, marketers are now reexamining the role AI plays in their programs. Every marketer I’ve personally spoken to in the last six months is tasked with using AI to create workflow efficiency and drive more revenue.
Marketing AI is worth the hype–it can provide businesses with advanced tools and capabilities to analyze consumer behavior, optimize campaigns, and personalize experiences. But at the same time, the great potential of AI also raises important ethical concerns around addressing bias, understanding limitations, and respecting creative professionals' work. Take the current writers’ strike; people are rightly concerned that unregulated use of AI can result in what is basically reused work—without compensation.
By taking these ethical concerns into account, marketers will be able to adopt more responsible and inclusive AI practices.
Amplifying Existing Societal Bias
While AI promises increased efficiency and accuracy, it can inadvertently reinforce and amplify existing biases in marketing. AI algorithms that operate on reinforcement learning principles may unintentionally perpetuate racial, gender, or other biases present in the data they are trained on. If the data used to train AI models is skewed, the resulting outputs will reflect those biases and limitations.
For example, take image generation. If the AI technology is asked to produce a picture of a CEO, and is trained using data from today’s top 50 global companies, it will most likely produce an image of a white man. Why? Because it’s being trained on existing data, where white men are representing an overwhelming majority of CEOs for the world’s top companies. The bias is amplified because the AI incorrectly accounts it as truth.
AI may also pick up on more subtle biases that are difficult for the human eye to detect. If you train AI on a set of 100 birds, it might not recognize a bird with an especially large beak because that type of bird was forgotten in the data set. The same thing happens with bias—if we accidentally overlook something, then the inaccurate training set will amplify that bias out in the market.
Similarly, AI for marketing can lead to a number of ethical concerns - discriminatory targeting, biased content, stereotypical recommendations, and more. Marketers need to critically examine their training data, identify potential biases, and implement mechanisms to correct or mitigate them. Augmenting training data with diverse and inclusive datasets can help address these challenges. Additionally, marketers should look to mitigate bias in their AI systems via solutions that consider review processes and allow for AI overrides with set rules.
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Reinforcing Team Blind Spots
The composition of the development team responsible for training and deploying AI models plays a crucial role in mitigating bias. Non-diverse teams may unintentionally overlook biases or fail to notice potential ethical issues arising from the AI model's outputs. Take the previous CEO example; a gender-balanced team is much more likely to notice that lack of diversity in AI-generated imagery.
While marketing leaders may not own the development process, many marketers will be responsible for running RFPs for martech that utilizes AI. Marketers must determine whether a diverse team that brings different perspectives and experiences to the table are part of a martech vendor’s development efforts. After all, diverse teams are more likely to recognize biases, challenge assumptions, and develop AI systems that are inclusive.
Reusing Existing Creative Work
AI models, particularly generative AI, often rely on large datasets that include copyrighted material to generate content. This raises ethical questions regarding intellectual property rights and the use of proprietary creative work for training AI models. Legislations like the pending EU AI Act are emerging to address some of these concerns.
Marketers must consider the ethical implications of using copyrighted material; not only in terms of abiding by intellectual property rights, but by respecting creative professionals who deserve compensation for the use of their work. It’s crucial to seek appropriate permissions or develop alternative approaches to training AI systems with these rights in mind.
This will continue to become an even more complex issue as many content producers are purposely producing poor content to throw off AI (like fan fiction writers with ChatGPT), and major content producers (like Getty Images) are suing AI companies for copyright infringement.
Marketers Take a Leading Role in AI Ethics
The integration of AI in martech presents both incredible opportunities and significant ethical challenges. Marketers must proactively address biases, training data limitations, team diversity, and copyright concerns to ensure responsible and inclusive AI practices. By considering the potential biases and implications of AI training using human work, marketers can harness the efficiency and effectiveness of AI to build inclusive, trustworthy, and original AI-powered marketing programs.