Buffett’s rationale is that, much like nuclear weapons, AI holds the potential to unleash profound consequences on a vast scale, both for better or worse.
And, like nuclear weapons, AI is concentrated in the hands of the few. In AI’s case, tech companies and nations. This is a comparison that is not often talked about.
As these companies push the boundaries of innovation, a critical question emerges: Are we sacrificing fairness and societal well-being on the altar of progress?
One study suggests that Big Tech’s influence is ubiquitous across all streams of the policy process, reinforcing their position as "super policy entrepreneurs."
This allows them to steer policies to favour their interests, often at the expense of broader societal concerns.
This concentrated power also allows these corporations to mould AI technologies using vast datasets reflective of specific demographics and behaviours, often at the expense of broader society.
The result is a technological landscape that, while rapidly advancing, maybe inadvertently deepening societal divides and perpetuating existing biases.
Ethical concerns
The ethical concerns stemming from this concentration of power are significant.
If an AI model is primarily trained on data reflecting one demographic's behaviour, it may perform poorly when interacting with or making decisions about other demographics, potentially leading to discrimination and social injustice.
This bias amplification is not just a theoretical concern but a pressing reality that demands immediate attention.
Porcha Woodruff, for example, a pregnant Black woman, found herself wrongfully arrested due to a facial recognition error—a stark reminder of AI's real-world consequences.
In healthcare, a widely used algorithm severely underestimated Black patients' needs, leading to inadequate care and perpetuating existing disparities. These cases underscore a troubling pattern: AI systems, trained on biased data, amplify societal inequalities.
Consider the algorithms driving these AI systems, developed mainly within environments that lack sufficient oversight regarding fairness and inclusivity.
Developing bias
Consequently, AI applications in areas such as facial recognition, hiring practices, and loan approvals might develop biased outcomes, affecting underrepresented communities disproportionately.
This risk is accentuated by the business model of these corporations, which emphasises rapid development and deployment over rigorous ethical review, putting profits above proper consideration of the long-term societal impacts.
To counter these challenges, a change in AI development is urgently needed.
Broadening the influence beyond big tech companies to include independent researchers, ethicists, public interest groups and government regulators working collaboratively to establish guidelines which prioritise ethical considerations and societal well-being in AI development would be a good start.
Governments have a pivotal role to play.
Stringent antitrust enforcement would limit big tech’s power and promote competition.
An independent watchdog with authority to sanction Big Tech practices would also help along with increasing public participation in policymaking and requiring transparency in tech companies' algorithms and data practices.
Global cooperation on fostering ethical standards and investments in educational programs to empower citizens to understand the impact of technology on society will further support these efforts.
The academic world, too, can step up. Researchers can advance methods to detect and neutralise biases in AI algorithms and training data. By engaging the public, academia can ensure diverse voices are heard in the shaping of AI policy.
Public vigilance and participation are indispensable for holding companies and governments accountable. The public can exert market pressure by choosing AI products from companies that demonstrate ethical practices.
While regulating AI would help prevent the concentration of its power among the few, antitrust measures which curb monopolistic behaviour, promote open standards, and support smaller firms and startups could help steer AI advancements towards public good.
Unique opportunity
Nonetheless, the challenge remains that developing AI requires substantial data and computational resources, which can be a significant hurdle for smaller players.
This is where open-source AI presents a unique opportunity to democratise access, potentially creating more innovation across diverse sectors.
Allowing researchers, startups, and educational institutions equal access to engage with state-of-the-art AI tools levels the playing field.
The future of AI is not predetermined. Taking action now can shape a technological landscape that reflects our collective values and aspirations, ensuring the benefits of AI are shared equitably across society. The question is not whether we can afford to take these steps but whether we can afford not to.
Arif Perdana is an Associate Professor at Monash University Indonesia, specialising in digital strategy, and data science. He is the director of Action Lab, Indonesia.
Ridoan Karim is a Lecturer in Business Law, and Deputy Director of Undergraduate Studies at the School of Business, Monash University Malaysia. He has taught and researched in the fields of business and cyber law.
