Rethinking Progress in the Age of AI: A Call for Ethical Technology

Artificial intelligence has the potential to be one of the most transformative forces in human history. From advancing medical research and mitigating climate change to optimizing resource distribution and improving accessibility, AI is already shaping how we approach global challenges. However, progress is not inherently tied to technology alone. AI does not automatically lead to a better world; rather, it amplifies the intentions, priorities, and values of those who develop and deploy it.

Despite its vast potential, AI is too often wielded under a paradigm of technological fundamentalism, where solutions are pursued for their own sake rather than being carefully evaluated for their societal impact. A blind belief in AI as a force of inevitable progress risks worsening social inequalities, entrenching power imbalances, and accelerating environmental destruction. Instead of surrendering to the logic of "move fast and break things", we must redefine what meaningful AI progress looks like - one that centers human well-being, fairness, and sustainability.

This essay explores how AI can be reoriented toward a more ethical, inclusive, and purpose-driven trajectory, challenging the assumption that mere technological advancement equates to progress.

AI's Transformative Potential - When Used Wisely

There is no denying that AI has already delivered remarkable benefits across various fields. In healthcare, AI-powered models assist in diagnosing diseases with higher accuracy, accelerating drug discovery, and personalizing treatment plans¹. In environmental science, AI helps optimize renewable energy grids, track deforestation, and model climate change scenarios with unprecedented precision². These are clear examples of how AI, when deployed with intention and ethical foresight, can augment human capabilities and create tangible benefits for society.

The real power of AI lies in its ability to enhance human intelligence rather than replace it. AI-driven decision-support systems can help doctors detect cancers earlier, aid urban planners in designing more sustainable cities, and assist policymakers in identifying the most effective social policies based on data-driven insights³. However, this requires AI to be developed and deployed within frameworks that prioritize public interest, not just profit or efficiency.

The challenge, then, is not whether AI can be used for good - but whether our current systems are structured to ensure that its benefits are equitably distributed.

The Risk of AI Serving Power Instead of People

While AI's capabilities are undeniable, much of its development has been driven by profit motives rather than societal needs. The vast majority of AI research is controlled by corporate interests, with leading firms like OpenAI, Google, and Microsoft shaping the direction of AI innovation⁴. In many cases, AI is being used to maximize economic efficiency, control markets, and reinforce existing power structures rather than to promote collective well-being.

For example, AI has been deployed in mass surveillance systems, predictive policing, and automated hiring, often with outcomes that disproportionately disadvantage marginalized communities⁵ (see my other article). These applications do not serve public progress - they serve control, extraction, and surveillance. When AI is used to automate decisions that affect livelihoods, freedoms, and opportunities, it must be held to a higher ethical standard, ensuring that it is neither opaque nor unaccountable.

Moreover, AI-driven automation often exacerbates economic inequality, disproportionately benefiting those who control technological resources while displacing workers in lower-income jobs⁶. Instead of AI being designed as a complement to human labor, it is too often deployed as a means of cost-cutting, prioritizing efficiency over long-term social stability. Without deliberate policy interventions, AI could accelerate a world where economic benefits are concentrated in the hands of a few while broader society bears the brunt of automation-driven disruption.

AI, Sustainability, and the Limits of Growth

A particularly troubling trend in AI development is its insatiable demand for energy and resources, which runs counter to the urgent need for environmental sustainability. As discussed in previous essays, training large AI models requires vast amounts of electricity, and much of this energy still comes from non-renewable sources⁷. Furthermore, AI hardware relies on rare earth minerals, often extracted under exploitative conditions, creating an environmental and ethical paradox⁸.

If AI is to be part of a sustainable future, it must be developed with resource-conscious constraints rather than the current race toward ever-larger and more computationally expensive models. Some researchers advocate for "Green AI", which focuses on reducing the computational footprint of machine learning models while maintaining performance⁹. However, these efforts remain marginal in an industry driven by performance benchmarks and competitive scaling.

Rather than uncritically accepting AI's energy-intensive expansion, we must ask: Does every AI advancement justify its resource costs? Not all technological progress is equally meaningful. AI research should shift toward solving real-world sustainability challenges, rather than focusing purely on achieving higher accuracy scores on abstract benchmarks.

Rethinking AI Progress: Towards Ethical and Inclusive Development

To reclaim AI as a tool for genuine societal progress, we must fundamentally rethink how it is designed, deployed, and governed. AI should not be developed for its own sake, but rather with a clear purpose that aligns with societal and environmental well-being. Instead of prioritizing commercial interests, AI must be directed toward meaningful applications such as improving healthcare, addressing social inequalities, and tackling climate change. Funding and incentives should shift away from applications that merely optimize advertisement targeting or drive speculative financial trading, redirecting resources toward projects that generate real public value.

Additionally, AI access should not remain concentrated within a few powerful corporations. Governments, academic institutions, and grassroots organizations need to take a more active role in shaping AI development to ensure it serves the public good¹⁰. Encouraging open-source AI initiatives and decentralized infrastructures can democratize AI knowledge, preventing technological progress from becoming the exclusive domain of elite institutions.

Transparency and accountability must be core principles in AI governance. Systems that influence hiring, financial lending, and the criminal justice system should be explainable and free from bias¹¹. To achieve this, strong regulatory frameworks are necessary to hold AI developers accountable for any discriminatory or harmful consequences that emerge from their models.

Finally, AI must align with environmental sustainability goals. Research efforts should focus on energy-efficient algorithms and alternative computational approaches that minimize environmental harm⁹. Companies involved in AI development should be required to disclose the carbon footprint of their models and take genuine steps to reduce emissions, rather than relying on superficial offsetting mechanisms. By embedding these principles into the AI development process, we can ensure that technological progress serves not just economic interests but the broader needs of humanity and the planet.

Conclusion

AI has the potential to be an unparalleled force for positive change - but only if we consciously steer it toward ethical, sustainable, and inclusive development. Progress is not about building bigger AI models, automating more jobs, or optimizing market efficiency. True progress lies in AI's ability to uplift humanity - strengthening our societies, preserving our planet, and promoting justice rather than exacerbating inequality.

It is time to abandon the blind faith that "more AI" inherently means a better world. Instead, we must ask: How can AI serve the needs of people rather than the logic of markets? How can it align with ecological limits rather than accelerating environmental collapse?

The future of AI should not be dictated by the speed of innovation, but by the wisdom of its application. Only by redefining progress beyond technological expansion can we ensure that AI remains a tool for collective empowerment rather than a mechanism of unchecked power.

References

• ¹ Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
• ² Rolnick, D., et al. (2019). "Tackling Climate Change with Machine Learning", NeurIPS Workshop.
• ³ Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
• ⁴ Zuboff, S. (2019). The Age of Surveillance Capitalism. PublicAffairs.
• ⁵ Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. Polity Press.
• ⁶ Acemoglu, D., & Restrepo, P. (2020). "AI and Jobs: The Role of Demand", Brookings Papers on Economic Activity.
• ⁷ Patterson, D., et al. (2021). "The Carbon Footprint of AI Model Training", MIT Technology Review.
• ⁸ Nkulu, C. B., et al. (2018). "Sustainability Challenges in Cobalt Mining", Nature Sustainability.
• ⁹ Schwartz, R., et al. (2020). "Green AI", Communications of the ACM.
• ¹⁰ Srnicek, N. (2017). Platform Capitalism. Polity Press.
• ¹¹ Raji, I. D., et al. (2020). "Closing the AI Accountability Gap", FAccT.