Corporate governance is a system of rules, processes, and laws that control, regulate, and operate a business. It includes the relationships between a company’s stakeholders, and the internal and external factors that affect their interests.
In today’s rapidly evolving technological landscape, the need to reassess corporate governance, particularly in AI-driven companies, has never been more pressing.
Traditional Corporate Governance: Shareholder Primacy
- The modern corporate governance regimes in capitalistic and neo-capitalistic economies have traditionally favoured the theory of shareholder primacy.
- This means that in modern corporations, the primary objectives are profit generation and wealth creation for shareholders and investors, often taking precedence over other business goals, including the public good.
- For example, if you are opening a tea shop, your primary goal would be to maximise profits for yourself and your investors.
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The Emergence of Stakeholder Benefit Approach
- In contrast, there has been a growing movement towards a stakeholder benefit approach to corporate governance.
- This approach seeks to maximise the benefits for all stakeholders, including society, customers, and the environment.
- In this model, if you were a tea shop owner, you would prioritise sourcing from local farmers, using eco-friendly materials instead of harmful plastics, and ensuring good working conditions for employees.
The Role of AI in Modern Corporate Governance
- In recent years, corporations with alternative governance models leaning towards stakeholder capitalism have become more common.
- These corporations are increasingly involved in products, technologies, and services that cannot be driven solely by profit-making objectives but also have a greater social purpose.
- Generative Artificial Intelligence (AI) is one such example, where companies are exploring new governance structures to balance profit generation with social responsibility.
Issues Associated with AI Development
- Data Issues: The development of AI technologies requires access to vast amounts of data, which can accelerate the potential misuse of personal information and undermine privacy.
- Societal Impact: AI is not just a technological tool; its deployment impacts society deeply. There is a significant concern that human prejudices can be embedded into AI systems, leading to algorithmic biases with harmful consequences.
- For example, if AI becomes biassed against a particular gender or religion, it could lead to severe societal harm. Consider a facial recognition technology used to identify criminals; if it is biassed, it could incorrectly target specific groups, leading to grave mistakes.
Real-World Examples of AI Bias and Governance Challenges
- One notable instance of AI governance challenges is the case where Meta (formerly Facebook) was asked to pause its plans to train large language models using public content from Facebook and Instagram in Europe, due to privacy concerns raised by the Irish privacy regulator.
- Additionally, Amazon discontinued using a recruiting algorithm after discovering it was plagued with gender bias. Researchers at Princeton University also found that AI software linked certain words to European names more favourably than African-American names, demonstrating how AI can perpetuate existing biases and create inequality in opportunities and access.
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Corporate Responses to AI Governance Issues
- These considerations have prompted several companies to alter their corporate governance structures.
- For example, OpenAI and Anthropic have adopted structures that prioritise public good and responsible AI development.
- Anthropic, in particular, is governed by a Long-Term Benefit Trust model, composed of financially disinterested members who have the authority to select and remove part of the board. This system ensures that long-term ethical considerations are more important than short-term profit.
The OpenAI Controversy and Its Implications
- OpenAI, originally incorporated as a non-profit, transitioned into a hybrid model by incorporating a capped profit-subsidiary to support its capital-intensive innovation.
- This shift led to controversy when OpenAI’s non-profit board fired CEO Sam Altman over concerns about the rapid commercialization of AI at the potential cost of user safety.
- The dismissal was strongly criticised by Microsoft, OpenAI’s largest investor, and opposed by about 90% of employees who held stock options.
- Altman was eventually reinstated, and the board was replaced.
- This incident raised questions about the viability of public benefit corporate structures in the tech industry.
- As Milton Friedman famously asserted in 1970, businesses have a social responsibility to generate profits for their shareholders.
- The above event shows that even with the adoption of creative governance structures, shareholder primacy remains dominant, especially in tech companies where employees also have stock-based incentives.
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What Needs to Be Done: A Workable Strategy
To address these challenges, a more nuanced approach to corporate governance in AI companies is needed. This can be done by targeting following key areas:
- Enhancing Long-Term Profit Gains: Companies should be encouraged to adopt public benefit purposes that also offer long-term financial returns.
- Incentivizing Managerial Compliance: Managers should be rewarded for not only achieving financial success but also for adhering to ethical standards. Success should be measured not just in numbers but also in how ethically the work is conducted, with bonuses tied to these metrics.
- Reducing Compliance Costs: Simplifying processes and providing government incentives, such as tax benefits, can help reduce the cost of adopting ethical governance practices.
- Promoting Ethical AI Development: Additionally, ethical standards for AI governance must be established, similar to the EU’s proposed guidelines for high-risk AI systems.
- Setting accountability: Transparency in decision-making, regular audits, and diversity in teams can help mitigate biases and ensure a wide range of perspectives. Education in ethics for all stakeholders, along with potential amendments to corporate governance laws, can further support these efforts.
| Case Study: In the US, Delaware has set an example by passing a public benefit corporation law that requires companies to consider the public good. This law could serve as a model for encouraging companies to think beyond profit and prioritise societal benefits. |
Implementation Challenges:
Despite these strategies, several challenges remain like:
- AI’s global nature where regulations are country-specific.
- As technology is advancing rapidly, making it difficult to keep up.
Thus, a balance must be struck between regulation and innovation to ensure ethical governance without stifling technological progress.
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Conclusion
Reshaping the governance structures of AI companies is essential to ensure that AI development aligns with broader societal values. While the task is complex, integrating ethical considerations into corporate governance is not only possible but necessary for the sustainable growth of AI and its positive impact on society.
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