AI technology is rapidly advancing and has the potential to bring transformative benefits to society. For instance, discoveries like AlphaFold have improved our understanding of neglected diseases by providing access to a large number of protein structures. This achievement would have previously required years of research and expensive equipment.
However, AI also poses challenges. From bias in machine learning algorithms to irresponsible development and deployment, there is a risk of harm. To ensure AI technology serves society, we need to navigate these complex issues.
This requires the adoption of principles prioritizing safety and innovation by all those involved in building AI. Additionally, new institutions with expertise and authority must be established to responsibly steward AI development.
Creating institutions may seem difficult, but it is necessary to avoid superficial ethics and address the realities of the problems we face. Historically excluded communities should be included in these conversations.
Responsible innovation should be incentivized in the market. Labs building AI systems should establish proper checks and balances to inform their decision-making. Investors should prioritize safety and ethics over novelty when funding AI companies.
The industry is converging on practices such as impact assessments and involving diverse communities in development and testing. However, there is still a long way to go, especially regarding diversity and representation.
We can learn from the cybersecurity community and adopt practices like bug bounties to address bias in AI datasets and outputs.
Multinational governance is a challenge with the advancements in AI. Local guidance is important, but international policy alignment is also crucial due to the global nature of AI’s opportunities and risks.
Regulators should focus on creating future-proof laws that foster innovation while protecting people. This requires collaboration between government, tech companies, and civil society.
Building institutions for AI requires diverse skills, backgrounds, and collaborations. Scientific expertise, socio-technical knowledge, and multinational public-private partnerships are essential.
In a world where nostalgia and isolationism are on the rise, multilayered approaches to governance may not be popular, but they are necessary for solving the challenges of AI. Building institutions is the unglamorous work that will enable technologists to shape a better future together.
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