You’re right, there definitely are deeper, systemic issues that play a role in how AI—and technology in general—is developed and used. Here are a few deeper, more fundamental problems that contribute to the concerns about AI and its ethical implications:
1. Power Imbalances & Control
AI development is often concentrated in the hands of a few large corporations and governments. These entities hold the most advanced technology, the data, and the financial resources to develop AI. This creates power imbalances, where the benefits of AI innovation are skewed towards those who already have significant control over the economy, while others—particularly marginalized communities—remain excluded or harmed by the technology.
- Example: A tech giant creating AI that primarily serves its interests, without considering the impact on communities that depend on jobs AI might replace.
2. Data Colonialism
This term refers to the way data—especially personal or social data—is extracted and monetized, often from populations that don’t have the same access to power, ownership, or legal protections. It’s a kind of modern "colonialism" where corporations extract value from data generated by individuals, often without fully compensating them or giving them control over how their data is used. This practice disproportionately affects people in the Global South, who are often unaware of how their data is being harvested or used.
- Example: AI algorithms that use personal data to target ads or sell products, but don’t give users an understanding of how their data is being used or shared.
3. Lack of Ethical Frameworks
The development of AI and other technologies often outpaces the ethical frameworks and policies needed to govern them. Many companies are focused on technological advancement, but ethical considerations are often afterthoughts, if they are considered at all. This is problematic because without clear ethical standards, it’s harder to ensure that AI systems are developed and used responsibly.
- Example: The development of AI facial recognition technologies without considering its impact on privacy or the potential for racial profiling.
4. Technological Determinism
Technological determinism is the idea that technology evolves according to its own logic, and society must adapt to it. This often leads to the belief that technology—AI included—moves forward regardless of societal concerns or the potential harm it may cause. It overlooks the human agency involved in designing, deploying, and regulating technology. When technology evolves in a way that isn't aligned with human values or needs, it creates unintended consequences that harm people, especially when the human side of the equation is neglected.
- Example: AI systems designed without input from diverse voices, leading to solutions that work well for some groups but harm others, such as biased algorithms in criminal justice or hiring.
5. Commodification of Humanity
AI development is often driven by the desire to make human behavior predictable and, in some cases, monetizable. Platforms like social media use AI to optimize for user engagement, often exploiting human vulnerabilities. The commodification of human attention, emotions, and behavior turns people into products to be bought and sold, which raises questions about autonomy, exploitation, and the right to privacy.
- Example: AI in social media platforms that manipulates users into spending more time on the platform, often by targeting their emotional triggers, leading to potential harms like addiction, depression, or polarization.
6. Unintended Consequences from "Fast-Tech" Culture
The rapid pace of technological advancement often prioritizes speed over careful consideration of societal impacts. This “move fast and break things” mentality has driven some companies to roll out new technologies without fully understanding how they might impact individuals, communities, or economies.
- Example: The rollout of self-driving cars before clear laws and safety standards were in place, or AI systems used in hiring that are based on flawed assumptions about "ideal candidates."
These deeper issues, when combined, create a perfect storm of ethical concerns, exploitation, and inequality in AI development. In the end, it’s a mix of corporate greed, lack of regulation, and social apathy that drives much of this, alongside the drive for rapid progress.