12 September, 2024

AI Ethical Practices that Build Consumer Trust

Most businesses are racing to promote their latest integration of generative artificial intelligence (Gen. AI) in their solutions, not only for their clients but for their potential investors as well. It’s the winning ticket, but its rapid advancement has raised significant ethical concerns.

Regardless of the type of clientele served, most consumers are becoming more aware of the potential risks of sharing their data. Data privacy, bias, and lack of transparency of how a conclusion has been made are the top concerns mentioned in most studies.

To put it numbers, some 73% of tech experts reported concerns over potentially biased outcomes, with 63% already spotted biases in generative AI outputs, according to a survey of more than 500 senior IT leaders by customer-relationship-management provider Salesforce. Similarly, 56% of the subjects of the State of AI report by global research firm McKinsey showed strong concern over data accuracy.

As AI business tools become increasingly sophisticated and autonomous, customer trust will continue to dwindle. It will be a key component of building relationships with clients to be proactive and ensure responsible Gen AI integration in your business operations.

Top Ethical Concerns about Business AI Tools

To develop the solution you need to address the basis of your customer concerns, they are different and shouldn’t be lumped together under one umbrella.

1- Data bias and discrimination

One of the primary concerns is the potential for AI to perpetuate biases present in the data it is trained on. This can lead to discriminatory outcomes in areas such as lending by banks; screening for hiring; or pre-made assumptions during customer service based on the client’s profile.

For example, a health study found racial bias in a healthcare algorithm, which “falsely concludes that patients of African descent are healthier than equally sick patients of other races.”

Another study by Cornell found that when DALL-E-2 was prompted to generate a picture of people in authority, it generated white men 97% of the time.

These biases are due to the existing stereotypes and human-fed biased data on the internet.

The problem is, data will never be unbiased. Computer scientist and ethics researcher Dr. Timnit Gebru makes it clear that “there is no such thing as neutral or unbiased data set.”

2- Data privacy and security

Most consumers are worried about how their data is being collected, stored, used, and, most importantly, the potential for its misuse.

The danger here isn’t limited to the company they trust to use their data but it’s ability to protect their data. The same McKinsey survey found that cybersecurity is a major concern for businesses sharing their data with AI.

3- How AI uses my data

The lack of transparency is a top concern for many individuals and businesses. The black-box nature of many AI algorithms makes it difficult for customers to understand how decisions are being made. This lack of transparency decreases trust and leads to concerns about accountability.

Attempts to Regulate AI Use

Several governments around the world are trying to grab the steering wheel of AI’s wild ride. While many efforts are focused on the current language models, many are attempting to establish enforceable guidelines on how data is collected and processed.

The boldest move is perhaps by Italy, who in March 2023, it’s Data Protection Authority (Garante) imposed a temporary ban on ChatGPT. This decision was based on concerns about the platform’s handling of user data and lack of transparency regarding data processing practices. The ban was later removed, however, concerns are still high.

While not specifically about AI, Europe’s General Data Protection Regulation (GDPR), enforced a law since 2018 granting individuals significant control over their personal data and imposes strict obligations on organizations that process it.

The European Union is also currently negotiating the AI Act, a comprehensive piece of legislation that aims to regulate AI systems based on their risk level. It includes provisions for data protection, transparency, and accountability.

China, on the other hand, just released a comprehensive AI governance framework, including ethical guidelines and standards. While the focus is on promoting AI development, the framework also addresses privacy concerns.

For the United States’ progress on the matter has been slow so far. While no serious national steps have been taken, a couple of state-level privacy laws have been issued.

For example, California’s Consumer Privacy Act (CCPA) and Virginia’s Consumer Data Protection Act (CDPA) provide individuals with certain rights regarding their personal information.

There are a few other initiatives by other countries, such as Singapore and South Korea have both introduced privacy laws and guidelines that apply to AI. Other countries’ laws also relatively apply to AI like Brazil’s General Data Protection Law (LGPD) and Mexico’s Personal Data Protection Law (LFPDPPP) both address privacy concerns related to AI.

10 Ways to Addresses Your Customers’ AI Concerns

Despite the ambiguity of laws and common practices on the matter, there are a few steps businesses can take to address ethical concerns in Gen AI and AI integration in business operations. The below advice is based on a comprehensive study of responsible AI practices and corporate responsibility, updated in February 2024 by the International Journal of Science and Research

1- Integrate ethics into your core: Make responsible AI practices a fundamental part of your corporate DNA, not an afterthought.

Develop comprehensive ethical guidelines that inform every stage of AI development and deployment. Ensure these principles are understood and embraced at all levels of your organization, from the C-suite to entry-level positions.

2- Commit to transparency: Foster trust by being open about your AI decision-making processes.

Take active steps to clearly communicate the types of data collected, how it is used, stored, and how AI-driven decisions are made. Be transparent about the limitations and potential biases of your AI systems.

3- Implement robust data governance: Develop strong data security measures, obtain informed consent for data use, and implement data minimization practices. Communicate to your audience how you protect their data privacy.

4- Establish accountability mechanisms: Create clear lines of responsibility for ethical AI within your organization. Consider appointing an AI ethics officer or committee. Implement regular audits of your AI systems to ensure they continue to meet ethical standards.

5- Bias mitigation: Implement measures to identify and mitigate biases in AI systems. This includes using diverse datasets, regularly auditing algorithms, and involving diverse teams in the development process.

6- Human Oversight: The most effective way to build trust is to guarantee all your AI systems are always under human oversight. This can help prevent unintended consequences and ensure that ethical standards are being met.

7- Be proactive: Go beyond compliance. Anticipate ethical challenges and address them head-on. Conduct regular ethical impact assessments of your AI systems. Don’t wait for regulations to catch up; set the standard for responsible AI use in your industry.

8- Think long-term: Prioritize sustainable, ethical approaches that build trust and position you as a leader in a values-driven market. Consider the long-term impacts of your AI systems on employees, communities, and the environment.

9- Engage stakeholders: Involve diverse voices in your AI development process to ensure inclusive and fair outcomes. Consult with employees, clientele, and external experts. This inclusive approach will help you identify potential biases and unintended consequences early in the development process.

Consider also participating in cross-sector initiatives focused on ethical AI, working with companies with a different set of stakeholders and goals will help you unveil potential opportunities and challenges up ahead.

10- Stay adaptable: Develop flexible ethical frameworks that evolve with advancing AI technologies. Regularly review and update your ethical guidelines to address new challenges posed by AI advancements. Create mechanisms for quick adaptation to emerging ethical concerns.

As you navigate the AI revolution, you need to know that ethical practices aren’t just a checkbox to show your stakeholders, it’s a strategic necessity. It’s the foundation of the sustainable growth of your business and futureproofing your operations.