In the last few months, ChatGPT has taken the digital world by storm, amassing roughly 57 million active users in only one month after launching for public use in December 2022 (CBS). It is safe to say that artificial intelligence technologies are here to stay. From data analysis and customer service to translation and fraud detection, business leaders across industries and functions are intrigued at the prospect of deploying AI tools to garner the promising results which have been touted in improved process efficiency, decision making, talent management, and marketing by early adopters.
While there are many pros to generative AI, concerns over bias and accuracy embedded in the technology are sufficiently evidenced – such as the example of a poorly trained AI that created this “white Obama” headshot. For this reason, executives at all levels need to become fluent with the technology’s uses and set guardrails to ensure that it works in a manner that fits each company’s comfort and requirements. This will enable companies to use generative AI to further their business goals while protecting the company’s trust with the public at a time when opinions are still mixed about AI technology.
Navigating the Complexities of AI in Marketing
While there are a multitude of applications for generative AI, marketing is perhaps one of the areas that has made the greatest inroads while still being susceptible to the greatest risks.
On the upside, generative AI can be extremely useful to marketers who heavily rely on targeting specific audiences to optimize campaign effectiveness. Generative AI models can generate personalized content and target individuals based on demographics, interests, and behaviors. However, where marketers need to heed caution is that biased models can perpetuate discriminatory targeting or reinforce stereotypes, resulting in certain groups’ exclusion or unfair treatment. Ensuring fairness and accuracy in targeting is crucial for protecting effective and ethical marketing practices.
Another area where generative AI can be helpful to marketing is in the field of customer experience and engagement. Generative AI creates personalized content, chatbots, and virtual assistants to enhance customer experiences and is doing this better and faster than ever before. However, if these AI systems produce racially stereotyped virtual agents or biased and inaccurate responses, it can negatively impact user satisfaction and engagement. Customers may feel misunderstood, misrepresented, or discriminated against, resulting in lower transaction volume and decreased brand loyalty. Providing accurate and unbiased AI-generated interactions is therefore crucial for fostering positive customer experiences.
There is also the emerging issue of regulatory compliance and legal considerations since generative AI could fall under existing advertising and consumer protection laws. The legal environment around AI tech is still taking shape. Tools that exhibit biases and inaccuracies create the likelihood of exposure to legal and financial penalties as well as substantial reputational damage.
Marketing executives put in relentless effort to create and uphold positive brand images and earn customer trust, which is no easy feat and requires considerable time and investment. When venturing into the realm of generative AI, it is therefore crucial for them to grasp both the perks and pitfalls of this technology and learn how to wield it responsibly. Being fully aware of the benefits and risks associated with generative AI will empower them to make informed decisions and safeguard their brand’s integrity.
What Are the Solutions?
So, what’s on the horizon to help marketers and other business users gain the advantages and reduce the risks of generative AI? Data scientists involved in generative AI at all levels are working hard to improve the kinds of data that the classifiers and filters built into the tools are trained on. That work and the following methods offer promising solutions to enhancing the impact and reducing the risk of generative AI technology.
- Dataset Curation and Diversity: Curating more diverse and representative training datasets can help reduce biases. Efforts are underway to include a broader range of perspectives and ensure balanced data. Researchers are developing techniques to identify and mitigate biases in training data.
- Algorithmic Improvements: Researchers are exploring fine-tuning, transfer learning, and adversarial training algorithms to mitigate biases and enhance accuracy. Ongoing algorithmic advancements and model architectures can contribute to more accurate and fair generative AI systems.
- Post-Generation Verification and Fact-Checking: Techniques are being developed to assess the accuracy of generative AI outputs. Integrating external knowledge sources, leveraging natural language processing, and collaborating with domain experts can help verify the factual correctness of generated content to identify and correct inaccuracies.
- Interpretability and Explainability: Making generative AI models more interpretable and explainable can aid in identifying and addressing biases and inaccuracies. Understanding the internal workings of these models helps stakeholders detect and resolve bias-related issues.
- Ethical Guidelines and Regulations: Recognizing the need for ethical guidelines and regulations, governments, organizations, and industry bodies are working on frameworks and policies to promote responsible AI practices. These measures incentivize the adoption of ethical practices and hold developers accountable for biases and inaccuracies.
As generative AI continues to evolve, business users – in particular marketers – need to understand their technology’s capabilities and be diligent in determining if a potential tool was trained on an acceptable range of data sets. Continued oversight and collaboration with domain experts in ethics, diversity, and linguistics will help heighten awareness of potential problems that must be remedied as early as the adoption or broader use phase.
Conclusion
One day, there will come a time when generative AI technology will be as widespread in everyday life as search engines and cell phones. While the technology is very promising and we have yet to grasp its full potential, it is still in its infancy, with imperfections and growing pains that need smoothing out.
By curating diverse datasets, refining algorithms, verifying outputs, promoting interpretability, and implementing responsible practices, the potential for bias and inaccuracy in generative AI could be minimized. However, it is essential to recognize that this challenge is complex and ongoing, requiring continued efforts from researchers, developers, policymakers, and stakeholders across multiple disciplines. Transparent and accountable practices are vital to ensure the responsible development and deployment of generative AI systems that are fair, accurate, and inclusive.
