The Ethics of AI: Who Owns the Algorithm?

Artificial intelligence is reshaping our world, but as AI grows more powerful, questions about who controls it become urgent. When intelligence itself becomes proprietary, the balance between innovation, fairness, and accountability shifts dramatically. This post explores the ethical and intellectual ownership of AI algorithms, drawing on recent data and reports to understand the challenges posed by corporate dominance in AI development.

AI computing hardware in a data center

Corporate Concentration of AI Patents

Recent data from the World Intellectual Property Organization (WIPO) and the Organisation for Economic Co-operation and Development (OECD) reveals a striking concentration of AI patents. In 2024, 78% of AI patents filed worldwide are held by just 2% of global corporations. This small group of companies controls a vast majority of the intellectual property behind AI technologies.

This concentration raises concerns about the monopolization of AI knowledge and tools. When a few entities own most AI patents, they can limit access to algorithms and data, slowing down open scientific research and innovation. This control also affects who benefits from AI advancements, often prioritizing profit over public good.

The dominance of a few players challenges the principle of machine learning transparency. Without clear insight into how algorithms are developed and used, it becomes difficult to hold these entities accountable for ethical lapses or biases in AI systems.

Ethical Bias and the Need for Transparency

The UNESCO AI Ethics Report from 2023 highlights another critical issue: less than 15% of AI models undergo ethical bias audits. This means most AI systems are deployed without thorough checks for fairness or discrimination.

Bias in AI can reinforce existing inequalities, affecting decisions in hiring, lending, law enforcement, and healthcare. Without transparency, users and regulators cannot verify if AI systems treat all individuals fairly.

AI governance frameworks are essential to address these risks. They provide guidelines for ethical technology innovation, ensuring AI respects human rights and societal values. However, the slow adoption of such frameworks leaves many AI applications unchecked.

Code screen displaying ethical bias audit highlights

The Impact on Open Science and Fairness

The rise of corporate AI monopolies threatens open science, which depends on sharing knowledge freely to accelerate discovery. When algorithms and data are locked behind patents and trade secrets, researchers and smaller organizations face barriers to entry.

This limits diversity in AI development and narrows the range of perspectives contributing to technology design. It also reduces opportunities for independent audits and improvements, which are vital for machine learning transparency.

Fairness suffers when AI tools are controlled by a few. These entities may prioritize applications that maximize profits rather than address social needs. For example, AI used in public services or healthcare requires careful ethical oversight, which may be lacking if proprietary interests dominate.

Building Strong AI Governance Frameworks

To counterbalance corporate control, governments and international bodies must develop and enforce robust AI governance frameworks. These frameworks should:

• Require transparency about AI algorithms and data sources

• Mandate regular ethical bias audits

• Promote open access to AI research and tools

• Encourage collaboration between public institutions, academia, and industry

• Protect user rights and privacy

  
  

Countries like the European Union have started implementing regulations that demand transparency and accountability in AI systems. These efforts aim to ensure AI benefits society broadly, not just a few stakeholders.

Encouraging Ethical Technology Innovation

Ethical technology innovation means designing AI systems that respect fairness, privacy, and human dignity. It requires integrating ethics into every stage of AI development, from data collection to deployment.

Companies and researchers can adopt practices such as:

• Conducting bias impact assessments

• Publishing audit results publicly

• Engaging diverse teams to reduce blind spots

• Using open-source models when possible

  
  

These steps improve machine learning transparency and build trust in AI technologies.

Researcher reviewing AI ethical guidelines on a tablet

Moving Forward: Who Should Own AI?

The question of who owns AI algorithms is not just legal but deeply ethical. When intelligence becomes proprietary, it shapes who controls knowledge and power in society.

To ensure AI serves the public interest, ownership models must balance innovation incentives with openness and fairness. This could include:

• Expanding public funding for AI research with open-access requirements

• Creating shared AI infrastructure accessible to diverse users

• Strengthening international cooperation on AI ethics and governance

  
  

Researchers, students, and policymakers all have roles in shaping this future. By demanding machine learning transparency and supporting AI governance frameworks, they can help build a more equitable AI landscape.

The ownership of AI algorithms will define how technology shapes our world. Ensuring ethical technology innovation and fair access is essential to harness AI’s potential for good. The next steps involve collective action to create systems that prioritize transparency, accountability, and inclusivity.