Screening for Ethics at Scale

Screening for Ethics at Scale

How frontrunners such as OpenAI should leverage human-in-the-loop to screen for ethical use at scale. In this article I’ll discuss these questions and explore different methods to mitigate negative outcomes, using OpenAI as an example.

Last June OpenAI released the most powerful language model ever created, which became the topic of much discussion among developers, researchers, and entrepreneurs. Its capabilities of zero- and one-shot learning blew people’s minds, with many GPT-3 powered applications going viral on twitter every second day.

This API is being released in an era when polarization and bias have never been as intense, with technology that is powerful, scalable, and potentially dangerous — imagine a fake news generator or a social media bullying bot powered by the human-like GPT-3.

Understanding the harmful potential of its API technology, OpenAI has taken a unique Go To Market approach, strictly limiting access to a small number of vetted developers. By doing so, it became one of the first companies to voluntarily forfeit short-term profits in favor of being socially-responsible.

As our understanding of AI evolves, other companies developing advanced AI technologies such as ScaleAI might follow a similar path.

The combination of a for-profit company, a powerful technology, and the decision to screen for access is novel, raising several questions:

  • What are the challenges of scaling a human-in-the-loop workflow? What are the specifics when screening for ethical usage?
  • How can you scale a subjective screening process which is based on human intuition and OpenAI’s guidelines?
  • How can you scale these operations to cater to an ever-growing customer base?
  • What are the implications of a screening process that doesn’t scale well?

In this article I’ll discuss these questions and explore different methods to mitigate negative outcomes, using OpenAI as an example.

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