Ethical vs. Unethical Uses of AI in Marketing: Trends and Best Practices

Artificial Intelligence (AI) has transformed marketing, offering innovative ways to engage customers, personalize experiences, and optimize campaigns. However, ethical AI use is crucial to maintain consumer trust and ensure fairness. Discover the key principles of ethical AI, the risks of unethical practices, future opportunities, and how businesses can adopt best practices.

ethical vs unethical use of ai in marketing

Ethical AI in Marketing


Transparency is crucial in building trust between companies and consumers. When AI is used, it’s important to clearly inform consumers about its role in decision-making processes and ensure that the processes are understandable.

One notable example is IBM Watson, which exemplifies transparency by providing users with clear explanations of how it reaches its conclusions. This includes detailing the data sources and algorithms used, which helps users understand the decision-making process. Additionally, IBM offers tools like the AI FactSheets to promote transparency and accountability.

Similarly, Google’s Explainable AI  initiative works on making their AI systems transparent. This initiative helps users understand how AI models make decisions by providing detailed explanations of the processes involved, fostering trust. Google’s commitment to transparency ensures that users are aware of how their data is being used and how conclusions are derived.

Another example is Microsoft Azure, which includes features that enhance transparency, such as Model Interpretability tools. These tools allow developers to understand and explain model behavior to stakeholders, building trust and ensuring ethical use of AI. Microsoft’s approach highlights the importance of making AI understandable and accountable.

Data Privacy

Ensuring data privacy is essential in maintaining user trust and complying with regulations. Ethical AI systems should collect and use personal data with explicit user consent and store it securely.

Apple’s Privacy Policies emphasize data protection by anonymizing user data and not selling it to third parties. Features like App Tracking Transparency require apps to obtain user permission before tracking their data, highlighting Apple’s commitment to user privacy. Apple’s approach ensures that users are fully informed about how their data is used and provides control over their personal information.

In contrast, DuckDuckGo prioritizes user privacy by not tracking user activity and not storing personal data. This approach provides a more private search experience, ensuring user data remains confidential. Businesses can learn from DuckDuckGo’s commitment to privacy by implementing strict data protection measures.

Mozilla Firefox’s Enhanced Tracking Protection blocks thousands of third-party trackers by default, protecting user data and providing a more private browsing experience. Mozilla’s commitment to privacy extends to transparency reports and user education, demonstrating how companies can prioritize user privacy while maintaining transparency.

Fairness and Non-Discrimination

Ensuring AI systems are fair and non-discriminatory is essential to prevent biases that could harm certain groups. Ethical AI should promote inclusivity and fairness through diverse datasets and regular audits.

Google’s Inclusive Machine Learning Toolkit provides resources for developers to build fair and unbiased AI models. The toolkit includes guidelines and tools to detect and mitigate bias in AI systems, promoting inclusivity and fairness. Companies can adopt similar practices to ensure their AI models do not perpetuate existing biases.

IBM’s AI Fairness 360 Toolkit includes metrics and algorithms to help developers detect and mitigate bias in AI models. This toolkit is part of IBM’s broader commitment to promoting fairness in AI, demonstrating the importance of using technical tools to address ethical challenges.

Microsoft’s Fairlearn Toolkit offers tools to assess and improve the fairness of AI systems. This includes fairness metrics, mitigation algorithms, and visualization tools to understand model performance across different demographic groups. By incorporating these tools, businesses can ensure their AI systems are fair and just.


Accountability in AI ensures that companies take responsibility for AI-driven decisions and their impacts. Ethical AI practices should include clear frameworks for addressing errors or negative outcomes.

Google’s AI Principles set out a framework for ethical AI development, including commitments to avoid creating AI systems that cause harm, ensure accountability, and uphold user privacy. Google’s AI ethics board oversees adherence to these principles, emphasizing the importance of oversight in AI development.

IBM’s AI Ethics Board oversees the company’s AI initiatives, ensuring they align with ethical standards and societal values. This board includes experts from various fields to provide comprehensive oversight, demonstrating the importance of diverse perspectives in AI governance.

Salesforce’s Ethical Use Advisory Council guides the ethical use of AI and other technologies. This council provides recommendations on best practices and policies to ensure AI is used responsibly, highlighting the need for continuous ethical guidance in technological innovation.

Unethical AI Practices in Marketing

Unethical AI practices compromise user privacy, manipulate consumers, and perpetuate bias or discrimination. These practices can have serious consequences for both businesses and consumers.

Invasive Data Collection

Unethical AI systems gather personal data without explicit user consent, leading to privacy violations and user mistrust.

A prominent example is the Facebook and Cambridge Analytica scandal, where personal data from millions of Facebook profiles was harvested without consent and used to influence voter behavior in the 2016 US election. This incident highlighted the dangers of unethical data collection and the importance of user consent.

Similarly, Google faced legal action for collecting location data from users without their explicit consent. Despite users disabling location tracking, Google continued to collect data, leading to privacy concerns and lawsuits. This example underscores the need for transparent and ethical data practices.

Deceptive Practices

Using AI to create fake reviews and deepfakes misleads consumers and damages public trust.

During the 2020 US elections, deepfakes of political figures were circulated to mislead voters. These manipulated videos aimed to distort the truth and influence public opinion, raising concerns about the potential for AI to be used unethically in media and marketing.

Additionally, Amazon has struggled with fake reviews on its platform. These reviews are often generated using AI to create convincing but false feedback, misleading consumers and distorting product ratings. Businesses must ensure that their review systems are transparent and trustworthy to avoid such unethical practices.

Bias and Discrimination

AI algorithms can inadvertently perpetuate biases, leading to unfair treatment of certain groups.

For example, Amazon had to scrap an AI recruiting tool that was biased against women. The tool favored male candidates because it was trained to recognize patterns in past hiring practices that predominantly favored men, underscoring the need for diverse and representative training data.

Similarly, Twitter faced backlash when its AI photo-cropping tool was found to be biased, often focusing on lighter-skinned individuals and cropping out darker-skinned individuals. The incident highlighted the need for transparency in AI systems and the importance of addressing biases in algorithm development.

Lack of Transparency

A lack of transparency in AI practices can erode user trust and lead to significant ethical concerns.

Facebook has been criticized  for its lack of transparency in how it uses user data for advertising. Despite various privacy scandals, the platform’s complex data practices remain opaque to many users, leading to calls for greater transparency.

In the healthcare sector, Google faced scrutiny over its partnership with Ascension, where patient data was shared without explicit consent. This lack of transparency raised concerns about how AI is used in sensitive areas like healthcare.

Exploiting Vulnerable Populations

Targeting vulnerable groups with manipulative AI-driven ads is unethical and can lead to significant harm.

AI-driven junk food ads targeting children have raised ethical concerns, prompting calls for stricter regulations. Studies have shown that children are more susceptible to advertising, and targeted junk food ads can contribute to unhealthy eating habits and obesity.

AI-driven scams targeting the elderly have become increasingly sophisticated, using personalized messages to exploit their vulnerabilities. These scams often involve phishing emails or fake tech support calls, leading to financial loss and distress for victims.

Opportunities for Ethical AI in Marketing

The future holds significant opportunities for ethical AI in marketing, focusing on enhancing customer engagement, improving ad targeting, and ensuring regulatory compliance.

Enhanced Customer Engagement

Ethical AI can create personalized marketing experiences that respect user privacy.

Netflix uses AI to recommend content based on user preferences while ensuring data is handled securely. Their recommendation system, which accounts for 80% of the content watched on the platform, personalizes viewing experiences while prioritizing user privacy.

Sephora uses AI to offer personalized beauty advice through their Virtual Artist tool. This tool uses facial recognition and AI to recommend products and makeup looks tailored to individual users, enhancing their shopping experience while maintaining data privacy.

Improved Ad Targeting

Bias-free algorithms can help in fair ad targeting.

Google AdSense has made strides in creating more equitable ad delivery by regularly auditing and adjusting their algorithms. By focusing on fairness and transparency, Google aims to deliver ads that are relevant to users while avoiding discriminatory practices.

Facebook’s Special Ad Categories ensure that ads related to housing, employment, and credit do not discriminate based on personal attributes such as age, gender, or race. This move aims to create a fairer advertising ecosystem.

Regulatory Compliance

With upcoming laws focusing on AI ethics, businesses can prepare to comply with regulations, enhancing their reputation and avoiding penalties.

The European Union’s AI Act   is a proposed regulation that sets out a framework for the development, deployment, and use of AI systems in the EU. It focuses on high-risk AI applications and ensures they meet rigorous standards for safety, transparency, and accountability.

In the United States, the California Consumer Privacy Act (CCPA) provides another example of regulatory compliance. The CCPA grants California residents new rights regarding their personal information and imposes corresponding obligations on businesses. Companies need to ensure their AI systems comply with these regulations to avoid penalties and build consumer trust.

Recommendations for Ensuring Ethical AI Use

To avoid the pitfalls of unethical AI and ensure responsible use, businesses should adopt several key practices.

Develop Clear AI Policies

Establish transparent and accountable AI guidelines. For example, Microsoft’s AI principles provide a framework for ethical AI development, emphasizing transparency, fairness, accountability, and inclusiveness. These principles guide Microsoft’s approach to AI, ensuring that their AI technologies are developed responsibly.

Regular Audits and Monitoring

Conduct bias assessments and impact evaluations regularly. Ensuring that AI systems are continuously monitored and audited for biases and errors is crucial. Google’s AI ethics board, although it faced challenges, was a step towards accountability and transparent oversight of AI development.

User Consent and Education

Ensure informed user consent for data collection and educate users about AI use. Apple’s privacy policies, such as through its privacy features and App Tracking Transparency, serve as a model for how companies can prioritize user privacy and transparency.

Engage with Ethical AI Experts

Collaborate with ethicists and AI experts to develop and review AI systems. Regular third-party audits can provide unbiased assessments of AI practices. Brent Purves, CEO of Stir Marketing, emphasizes, “Incorporating ethical AI practices is not just a regulatory necessity but a moral imperative. It builds trust and ensures long-term success.”

Ethical AI in marketing is essential for maintaining consumer trust and ensuring fair practices. By following these recommendations, businesses can harness the power of AI responsibly.

For businesses looking to integrate AI ethically into their marketing strategies, partnering with experts can make a significant difference. At Stir Marketing, we specialize in helping companies navigate the complexities of AI implementation while adhering to ethical standards. Our team offers comprehensive services, including AI audits, policy development, and ongoing monitoring to ensure your AI systems are transparent, fair, and accountable.

Contact Stir Marketing today to learn how we can assist you in developing and implementing ethical AI practices in your marketing efforts. 

Checklist for Ensuring Ethical AI Use in Marketing

Develop Clear AI PoliciesBlank check box free icon

  • Set ethical guidelines that prioritize transparency, fairness, and accountability in AI use.
  • Educate all employees to ensure they understand and adhere to these policies.

Blank check box free icon Conduct Regular Audits

  • Perform bias assessments regularly.
  • Monitor the impact of AI on different demographic groups.

Blank check box free icon Ensure Transparency

  • Clearly communicate how AI is used in decision-making processes.
  • Provide explanations for AI-driven decisions.

Blank check box free icon Prioritize Data Privacy

  • Obtain explicit user consent for data collection.
  • Implement robust data protection measures.

Blank check box free icon Engage with Experts

  • Consult with AI ethicists and experts.
  • Conduct third-party audits for unbiased assessments.

Blank check box free icon Stay Compliant with Regulations

  • Ensure AI practices comply with relevant laws such as GDPR or CCPA.
  • Keep updated with changes in AI-related legislation.

Blank check box free icon Implement Fair and Inclusive AI

  • Use diverse datasets to minimize biases.
  • Ensure AI promotes inclusivity and does not discriminate.

Blank check box free icon Monitor and Iterate

  • Continuously update AI systems based on feedback and audits.
  • Adapt to user feedback to improve AI practices.

Blank check box free icon Engage with Customers

  • Build trust through transparency and accountability.
  • Educate customers about their rights and AI data usage.

Blank check box free icon Contact Experts

  • Partner with agencies like Stir Marketing for guidance on ethical AI implementation.