Marketing & Trustworthy AI: 8 Questions to Ask

Marketing and trust. Being in marketing my entire career, reading an article that starts like that feels like a setup. Something, it seems, isn’t going to end well. But that’s not THIS article! No, this article will hopefully provide insight into issues that Marketers will likely run into as they begin to explore adopting AI technology into their operations. So let’s get on with it.

Let's start with a definition of Trustworthy AI as developed by my friends at Cognilytica. They’ve created a framework and certification track for Trustworthy AI – it's worth checking out. As various flavors of AI gain acceptance within organizations, the need for understanding what is meant by “Trustworthy AI” becomes more important. This is particularly true in marketing as we all tend to prefer brands when we trust the value we seek is going to be delivered or exceeded.

So, what is Trustworthy AI?

The Cognilytica team suggest there are five pillars that make up the concept of Trustworthy AI. Together the pillars offer a compelling foundation for a concept that can be a bit nebulous. And the bad actors out there whose actions give AI an occasional black-eye, settling on these five attributes seems like a good place to start. So, quickly, the five pillars include:

Ethical AI – Ensuring that AI is developed and deployed with a clear ethical framework in mind and that the approach considers the impact on society, individuals/living things and the planet.

Responsible AI – Proactively focused on preventing harm that AI systems might cause, including addressing issues of bias and discrimination.

Transparent AI – Emphasizes the importance of clarity about system design, what data is used/excluded, model training, how bias is handled, and holding organizations accountable for system outputs and decisioning.

Governed AI – Proper management of data, respecting privacy, ensuring data protection and organizational guardrails are applied to prevent the misuse or improper system feature inclusions.

Explainable AI – Key elements include understanding root cause explanations, algorithmic interpretability, and ability to communicate how outcomes were arrived at from data sources in plain language for non-experts to understand.

Okay, now that we’ve established what Trustworthy AI includes. Now let’s dig into some questions that Marketing and organizational leaders should be asking as they start their AI journeys.

How does data privacy impact Trustworthy AI in Marketing?

Data privacy is a critical concern for marketing operations that utilize AI. Balance is needed to leverage data for personalization while respecting consumer privacy rights. On one hand, personalized content can significantly enhance customer engagement and satisfaction, making marketing efforts more effective. On the other hand, intrusive data practices or breaches lead to mistrust, damage brand reputation, and can result in legal issues, not to mention being a PR nightmare.

By adopting a privacy-first approach to AI use in marketing, brands can build trust with consumers and help ensure that personalization efforts are perceived as valuable services rather than invasive surveillance. This balance is essential not only for legal compliance to regulations such as GDPR and CCPA, but also for maintaining long-term customer relationships in the age of AI.

What risks does bias present in Marketing operations using AI?

Bias in marketing AI primarily arises from unrepresentative training data or prejudiced algorithms, leading to AI models that could unfairly favor or penalize certain groups. Such bias risks alienating segments of the consumer base and diminishing the effectiveness of marketing efforts. It can also harm a brand's reputation by appearing insensitive, exclusionary, or simply out of touch. To counteract these risks, marketers need to ensure their AI models are trained on diverse data sets, rigorously tested for biases, and continuously monitor strategies, tactics and outcomes that leverage AI in marketing campaigns. Furthermore, biased decision-making by AI models can lead to a homogenization of the consumer experience, stifling innovation, and the personalization efforts that AI seeks to enhance. The necessity and importance of addressing bias is an essential component of a trustworthy approach when deploying AI in Marketing.

Why is Transparency with AI's use in Marketing crucial?

As one of the pillars of Trustworthy AI, transparency is crucial in marketing as it builds the foundation for consumer confidence and trust. When AI is used to support decision making in marketing— from personalized recommendations to recommendation engines — customers want to understand how and why certain decisions are made. Transparent practices, where the workings of algorithms are openly shared, has a reassuring impact on consumers that their data is being used ethically and for their benefit. This openness helps demystify AI operations making it more relatable and acceptable to the public.

Where does Explainability come into play?

Explainability plays a vital role as a pillar in this process by making the operations of AI systems understandable to non-experts. When consumers can grasp how AI models arrive at certain decisions or recommendations, they are more likely to trust these systems and feel comfortable with their data being used. This level of trust leads to greater acceptance of the product or process and AI’s role. In turn, consumer loyalty gains strength, which is essential for long-term customer relationships and brand value.

What ethical considerations should guide AI in Marketing?

Ethical AI practices require that marketers not only comply with legal standards but also strive to uphold the spirit of applicable laws. In the age of AI, fostering trust and respect between the brand and its customers takes on new significance. Trust developed over years, decades even, can be lost in an instant during a lapse in ethical behavior.

In Marketing, ethics primarily involves respecting consumer autonomy and ensuring fairness. It’s doing the right thing, because it’s the right thing to do, not because you’re forced or a profit motive is at play. Key among these considerations is the necessity to avoid manipulation of consumers, markets, or campaign assets. Marketing, supported by AI or not, should aim to inform and engage consumers, not exploit vulnerabilities, or deploy tactics that could deceive. This includes being mindful of how behavioral data is applied to influence consumer decisions.

What key compliance frameworks influence both Marketing and AI use?

The impact regulatory issues play a critical role in shaping marketing strategies. Due to a patchwork of global regulations marketing leadership and legal are strongly encouraged to collaborate on a position. The two frameworks getting attention of late in marketing include:

General Data Protection Regulation (GDPR): Enforced in the European Union, GDPR imposes strict rules on data collection, processing, and privacy, requiring explicit consent from individuals before using their data. For marketers, this means ensuring transparent data practices and securing user consent.

California Consumer Privacy Act (CCPA): Like GDPR, CCPA gives consumers more control over their personal information. Marketers must allow consumers to opt-out of data selling and provide clear privacy notices. The impact is felt on how data is collected and used for targeting and personalization.

For more on privacy and data protection laws around the world, visit: DLA Piper's Global Data Protection Laws of the World.

What challenges do Dependency and Autonomy present Marketing when operationalizing AI?

As marketing increasingly integrates AI, it navigates a delicate balance between dependency on AI’s capabilities and the autonomy of its decision-making. Ensuring this balance aligns with the principles of Trustworthy AI is vital for maintaining marketing efficacy and brand integrity.

Dependency Challenges

Strategic Complacency: An over-reliance on AI can dull strategic thinking. The result can lead to marketers potentially overlooking opportunities that require human insight and perspective.

Data Quality and Integrity: It’s “garbage in, garbage out” again. AI is only as good as the data it’s trained on. Marketers must guard against data bias as previously mentioned to prevent brand-damaging missteps.

Creative Dilution: While AI identifies patterns to generate content, it can produce bland creative output. Creative Directors, you’re going to be needed for a while! There’s still requirements for the human touch to drive distinct and compelling campaigns.

Skill Stagnation: Heavy reliance on AI can limit the development of marketing and creative skills leading to a workforce less equipped for innovation.

Autonomy Challenges

Ethical and Legal Navigation: Autonomous AI systems need to be developed and utilized within ethical and legal standards, a process that requires human interpretation and judgment.

Bias and Discrimination: As discussed in this and previous articles, AI can embed existing biases into its operations, leading to unfair consumer treatment and tarnishing the brand’s image resulting in lost revenue.

Black Box Decision-Making: Marketers need to be transparent and open with AI decision-making to build consumer trust and ensure accountability.

Last, but certainly not least…

Maintaining Human Oversight

Balance and Expertise: A collaborative approach that leverages AI’s data-processing prowess with human creative and strategic expertise is necessary to conceive and build effective marketing campaigns and systems.

Ethical Decision-Making: Humans are essential for interpreting complex and nuanced ethical considerations then using judgement to align AI use with organizational values and societal norms.

Adaptive Contextualization: Humans excel at adapting to market shifts and interpreting nuances that AI alone may miss, this ensures messaging remains relevant and resonant.

Accountability and Compliance: Marketers are the custodians of AI-generated content, upholding accountability, and ensuring ethical alignment with brand and audience expectations.

These are some of the challenging questions marketers will face during their journey of operationalizing AI. Working within a framework of Trustworthy AI, while it’s still being defined isn’t easy. Missteps will be made but each endeavor is a learning experience and should be viewed as such. There will be conflicting perspectives such as organizational views, the perspectives of individuals and those of society at large. Marketing teams that start their journeys now and address the challenges posed by these questions head-on will be best suited to be Marketing AI leaders in the near future.

Helpful Resources and Points of View

NVIDIA: What is Trustworthy AI?

NIST: Minimizing Harms and Maximizing the Potential of Generative AI

IBM Research: Trustworthy AI

European Commission: AI Watch – Trustworthy AI

Credo.ai – Governance Resources