Building Trust in an AI-Driven World: A Comprehensive Guide

Discover the key risks associated with AI and the measures needed to mitigate them.

This article was written by ChatGPT and revised by Human Intelligence to build trust in AI.

In the rapidly evolving digital landscape, Artificial Intelligence (AI) has become a game-changer for businesses worldwide. However, with the increasing reliance on AI, trust has emerged as a critical factor. This article explores the key risks associated with AI and the measures needed to mitigate them, thereby building trust in an AI-driven world.

Key Risks in AI

Understanding the potential risks in AI is the first step towards building trust. These risks can be categorized into seven key areas:

  • Data Protection risks: With high-end models often dependent on cloud services, the use of sensitive data can pose challenges.
  • Intellectual Property Concerns: The use of generative models may infringe on others' intellectual property, leading to potential legal issues.
  • Security Concerns: Generative models could be used to generate malicious content, resulting in reputational damage for companies.
  • Overreliance on Generated Data or Hallucination: Overreliance on AI-generated data can result in companies making incorrect decisions or deteriorating their service quality.
  • Opacity: The lack of clear and understandable explanations can challenge decision-making processes and raise questions regarding accountability.
  • Bias and discrimination: Generative models may incorporate and amplify biases in the training data, leading to discriminatory outputs.

Mitigating AI Risks

To mitigate these risks, businesses need to focus on several key areas:

  • Human-in-the-loop: Involving human judgment in critical AI applications can increase reliability and robustness, thereby enhancing trust in these technologies.
  • Robustness: Ensuring consistent, reproducible, and resilient AI models is crucial. This includes prompt engineering to get accurate, reproducible, and robust results.
  • Fairness: Investing in diverse datasets, data quality, and transparency during the planning, development, and training phase can help avoid biased outcomes.
  • Security & Privacy: Understanding the data flow, ensuring data protection, and building a trust layer can bridge the trust gap in AI.
  • Governance: Implementing responsible AI practices from strategy to execution, involving all lines of defense in a company, is essential.


In conclusion, building trust in an AI-driven world requires a comprehensive understanding of the potential risks during the whole AI-Lifecycle and a strategic approach to mitigate them. By focusing on human involvement, robustness, fairness, security and privacy, and governance, businesses can effectively navigate the AI landscape and harness its full potential.

This content is based on a panel discussion Yan Borboën had at the Trust Valley Trust & AI Forum in Lausanne, 21.09.2023. A big Thank You to Roman Dykhno, Senior Solution Engineer at Salesforce, Athanasios Giannakopoulos, Engagement Director at Unit8 SA and Hugo Flayac, PhD, Co-Founder & CEO, for the great discussion!


Building trust in AI

If you would like to learn more on how to mitigate risks while implementing responsible AI-supported solutions in your company, please feel free to reach out to us. We help you along all stages: From strategy (definition of the use case) to execution and finally, the monitoring of the AI.

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Yan Borboën

Yan Borboën

Partner, Leader Digital Assurance and Cybersecurity & Privacy, PwC Switzerland

Tel: +41 58 792 84 59