Another part of AI governance is creating its transparent use. As Stuart Russell and Peter Norvig argue in Artificial Intelligence: A Modern Approach, AI systems should be designed to provide interpretable explanations for their outputs, particularly in high-stakes areas such as healthcare and criminal justice. Without transparency, AI models risk perpetuating biases and making decisions that are difficult to challenge or audit.
A major challenge to AI transparency is the trade-off between proprietary interests and public accountability. Shoshana Zuboff in The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power critiques how large technology firms develop AI-driven surveillance systems with minimal oversight, prioritizing profit over transparency. To address these concerns, Luciano Floridi in The Ethics of Artificial Intelligence advocates for regulatory frameworks that require AI developers to disclose the decision-making logic behind their algorithms while balancing trade secrets with societal accountability.
Improving AI transparency also requires interdisciplinary collaboration between technologists, ethicists, and policymakers. Russell Newman in The Paradoxes of Network Neutralities suggests that transparency should not only be about opening AI systems to scrutiny but also about ensuring that stakeholders—including governments, businesses, and the public—can meaningfully engage with AI governance. This means incorporating transparency standards in AI regulation, such as mandatory impact assessments and clear user disclosures. Additionally, the collection Transparency in Politics and the Media: Accountability and Open Government, edited by Nigel Bowles, James Hamilton, and David Levy, emphasizes that AI systems must be accompanied by clear documentation and open-source initiatives that allow independent auditing. By prioritizing transparency, society can ensure that AI remains a tool for progress rather than a mechanism for unchecked power.
