How to Build AI Trust: Ensuring Reliability in Artificial Intelligence

With the increased usage of artificial intelligence (AI) technology, companies seek to integrate AI-powered solutions into their workflows. However, the adoption of such tools is slowed down by concerns about the possible risks related to their deployment. Thus, building AI trust becomes paramount to ensure the long-term success of projects utilizing this technology.

According to the Wharton School of Business, over 50% of U.S. workers utilize generative AI, while 80% of enterprises across different industries aim to start using it within three years. In this article, we analyze the common reasons for mistrust and offer possible ways of enhancing the perceived safety of AI models.

What is AI Trust, and Why Increase It?

Trust in AI signifies the willingness of stakeholders to invest in the future applications of the technology, utilize AI-driven products, and make the most out of innovative practices.

According to surveys by McKinsey, most companies report that they face unforeseen obstacles on the way to implementing AI-driven tools. One of them is related to a common mistrust in AI, which prevents firms from making such solutions a part of their workflows.

Companies should improve their data governance practices, demonstrate their eagerness to invest in robust security measures, and increase their accountability and transparency.

When analyzing the complex relationships between the concepts of AI and trust, it is worth to consider the following dimensions:

• Stakeholders’ trust in the predictability and reliability of AI;
• The readiness of firms to implement AI and the readiness of customers to employ AI-driven products and services;
• Trust in the safety and predictability of human-machine interactions.

The concerns about generative AI hallucinations caused customers and businesses to think about whether it is possible to increase the reliability of this technology. The first use cases of autonomous vehicles, image generation tools, and virtual assistants utilizing large language models demonstrated the limitations of AI.

Instead of downplaying the dangers related to the insufficient reliability of such tools, industries should invest in making algorithmic solutions more trustworthy.

Fostering the employment of AI in medicine, aviation, and other industries requires adhering to guidelines ensuring the safety of AI systems. By exposing the low quality of data provided by AI solutions, it becomes possible to advocate for the usage of more accurate datasets.

We are confident that we have what it takes to help you get your platform from the idea throughout design and development phases, all the way to successful deployment in a production environment!

Factors Strengthening AI Trust

Besides nurturing trust in AI, it becomes crucial to foster trust in technology providers. When making up their mind about using AI services, customers consider multiple points:

• The potential benefits of using the technology: AI chatbots are available 24/7, provide mistake-free replies to queries, and can assist clients with solving complex issues, which may drive more people to put their trust in them.
• Consistent performance: Unlike human customer support agents or other professionals working in industries where AI adoption becomes increasingly widespread, AI products provide pre-programmed responses even during pick hours and offer comprehensive recommendations.
• Cost-effectiveness: Clients may be more inclined to utilize AI services in situations when they can save time and money by relying on innovative solutions. For instance, they may be willing to trust AI recommendations regarding booking during their trips or utilize AI bots to get legal consultations.

Building up trust in generative AI (gen AI) is a gradual process. It becomes possible through a series of successful interactions with the new technology. As ventures and their clients discover the upsides of utilizing AI, continuous trust development becomes the most crucial task to guarantee the future advancement of the technology.

How to Build AI Trust

Governments, enterprises of all sizes, regulators, and other stakeholders should come together to develop guidelines regulating the usage of AI tools. By increasing the accountability of service providers and promoting adherence to safety rules, it becomes possible to proactively remove possible barriers hindering adoption.

Developing trusted AI solutions necessitates using highly reliable datasets to train AI models. Provided this information gets tested regularly, it facilitates improving the efficiency and reliability of AI tools. With the evolution of generative AI, new governance practices should be introduced.

• Fostering collaboration: Ensuring the involvement of the key stakeholders in removing the obstacles in the way of adoption is a must if a venture wants to design trustworthy AI solutions.
• Gaining knowledge: Companies should increase their awareness of the risks related to AI usage and educate their employees to prevent dangerous situations.
• Building frameworks: Creating detailed guidelines with well-defined roles and responsibilities is important for establishing adherence to safety rules.

Many people still ponder: “Can we trust artificial intelligence?” Recognizing their doubts as valid is the first step toward building initial trust. Further success depends on the ability of all the stakeholders to follow the steps outlined below.

Establishing transparency

To trust AI products, people need to understand how they function and what are the key advantages and limitations of the technology. Companies developing AI applications should increase their accountability. Being open about the origins of data sets and all the steps of decision-making processes allows developers to explain why AI helpers react in a specific way to commands. If the behavior of AI chatbots and services cannot be explained comprehensively, it will be impossible to build trust in such solutions.

Improving the reputation of AI tools

Utilizing virtual avatars, using a suitable tone of voice, and offering personalized responses to queries are the only ways to demonstrate that customers are not dealing with soulless robots. Highlighting the reliability of AI products, companies should emphasize that AI assistants are designed to provide top-level support and solve the most complex queries efficiently.

By increasing AI literacy, firms can make the most out of such tools and minimize possible negative consequences. Establishing boards of experts tasked with controlling the usage of AI helps businesses become a part of a rapidly transforming industry and maintain full transparency of their internal processes.

Encouraging users to provide feedback

User reviews play an important role in building trust. However, companies should be wary of utilizing paid or AI-generated reviews. Offering a choice between using services provided by AI and human employees yields positive results as well.

When a customer understands they can still get assistance from regular agents, even if they have to wait longer, they are more likely to trust AI tools after deciding to use them willingly to save time. It allows them to test out the service and recognize its reliability.

Improving usability

Leveraging machine learning (ML) technology enables developers to make UX experience more enjoyable and train AI models to complete advanced-level tasks. Making such products as intuitive as possible and fostering their effectiveness facilitates winning trust.

Guaranteeing data protection

Many people fear providing data to robots as they do not have faith in the safety measures implemented by companies to guarantee their privacy. Openly discussing data security measures and inviting reputable third-party auditors to confirm the safety of a product diminishes privacy concerns.

Addressing common concerns

The media campaign against the usage of intelligent automation (IA) tools highlighted possible AI risks that may slow down the adoption. According to WEF, AI development may lead to the elimination of 85 million jobs. However, this non-governmental organization also expects 97 million new jobs to appear.

Educating potential adopters about the perils and advantages of AI is the first stage of normalizing the usage of such tools and showcasing how they can help employees solve time-consuming tasks more effectively.

Things to Consider When Building AI Trust

Due to the complicated nature of the human-machine relationship, demonstrating AI models’ ability to make accurate predictions based on past user behavior and the available data is an important part of building AI trust. When promoting AI solutions, their developers should prove their willingness to follow the principles of AI ethics and do the following:

• Use of MLOps and other practices: Introducing machine learning operations leading to workflow optimization streamlines deployment and adoption.
• Ensure dataset quality: Building trustworthy models requires using comprehensive assessment criteria allowing stakeholders and auditors to confirm the source and quality of data used during AI training.
• Achieve consistent performance: Demonstrating the predictability of a model under different scenarios restores trust in robotics and other innovative technologies. Utilizing the human in the loop approach enables data researchers and operators to play a pivotal role in a model’s development.
• Follow ethical rules: Adhering to privacy protection laws and implementing measures necessary to protect user data ensures end-users, investors, and other stakeholders that AI systems manage sensitive information effectively. Eliminating biases and ensuring fairness facilitates widespread adoption.

Following these steps, you can gradually build trust in the AI models you develop and demonstrate their reliability.

Final Words

At Global Cloud Team, we specialize in creating custom AI solutions aligning with trust and safety guidelines. Seeing our goal in expediting the adoption of this technology, we offer advanced solutions that allow businesses to maintain their accountability and adjust to the changing environment to remain competitive.

Our experienced professionals recognize the importance of embracing a responsible approach when building AI models. Establishing AI trust is a prerequisite to unlocking the full potential of innovative solutions.

Author

Alex Johnson

Total Articles: 111