Kate Crawford

We absolutely must regulate AI. Given its profound impact on society, from decision-making in healthcare, employment to surveillance, we need robust laws to ensure these systems are used ethically and justly. Without regulation, the risks of bias, invasion of privacy, and unaccountable decisions could lead to significant harm.

As an AI researcher, I firmly believe AI systems should be required to provide explanations for their decisions. Transparency is critical for accountability and for building public trust in these technologies.

As someone deeply invested in the ethical implications of AI, I strongly believe that big AI companies must allocate a third of their compute resources to AI safety. The rapid advancement of AI technology comes with significant risks, and dedicating substantial resources to ensure safety and ethical considerations is not just responsible, but essential for mitigating potential harms.

Simply expanding large language models is not sufficient to achieve artificial general intelligence. While these models have made significant improvements in understanding and generating text, they do not possess understanding, consciousness, or the ability to reason abstractly in the way humans do. Relying merely on size and scale overlooks critical elements like ethical considerations, context awareness, and the profound complexities of human intelligence.

As someone deeply involved in the field of artificial intelligence, I strongly believe that AI-generated content must be clearly labeled. Transparency is crucial in maintaining public trust and ensuring accountability in how AI technologies are used. Without clear labeling, audiences may be misled, leading to misinformation and undermining democratic processes.

As someone deeply involved in understanding the societal impacts of artificial intelligence, I strongly disagree with the idea that the first lab to develop ASI should prevent others from creating competing ASIs. Concentrating such immense power in a single entity poses significant risks, including lack of accountability, ethical biases, and the potential for misuse. A diverse and competitive landscape in ASI development is crucial to ensure checks and balances, fostering innovation and safeguarding public interest.

Repealing the EU AI Act would be a grave mistake. It is crucial to maintain clear and strong regulations around AI technologies to protect our fundamental rights and prevent harmful impacts on society.

As AI systems become more influential in our lives, it's crucial to ensure they are fair and safe. Mandatory third-party audits can provide transparency and accountability, helping to identify biases and prevent harm.

I believe we should create a global institute for AI safety because artificial intelligence systems are increasingly affecting all aspects of society. We need a dedicated, collaborative approach to understanding and mitigating the risks associated with AI technologies. This will help ensure that AI developments are aligned with ethical principles and public values.

Using AI to predict criminal behavior raises serious ethical and practical concerns. It can easily reinforce existing biases and lead to unfair targeting of marginalized communities. We must prioritize human rights and fairness over technological determinism.

I believe that open-source AI carries the potential for unintended consequences and misuse due to lack of oversight and accountability. Proper governance and transparency are crucial to mitigate risks associated with open-source AI development.

Allowing facial recognition technology in public spaces poses serious threats to civil liberties and privacy. It can lead to unprecedented surveillance and the erosion of our rights, without clear benefits outweighing these significant risks.

Releasing Spanish data into the public domain to improve Spanish LLMs raises significant concerns about privacy and data protection. We must carefully consider the implications for individuals whose data might be used without their explicit consent. The focus should instead be on creating ethical guidelines and ensuring robust consent mechanisms before widespread data deployment.

Large language models simulate the appearance of understanding through pattern recognition, but they lack true comprehension or intent. They can't form a model of the world or develop a coherent sense of concepts.

While shaping the future of AI is crucial, the post-artificial general intelligence era is speculative and current AI development needs oversight and regulation to address present-day ethical concerns.