A Universally Responsible AI Ecosystem

The proliferation of generative AI technologies has triggered an unprecedented 'arms race' in the tech world. With it, however, comes a host of risks, including the potential misuse of these advanced tools, and the glaring absence of stringent regulations to govern their application.

Balancing the incredible benefits of AI against these potential pitfalls is both a challenge and a necessity.

We all have been reading about the risks of poor, often dangerous outcomes like fake news, false narratives, factual errors, and plagiarism of IP. All this begs the question - What should the regulation of Generative AI look like? National Institute of Standards and Technology (NIST)?and the?The White House?have been frantically working on providing guidance and frameworks for Responsible AI but I haven't seen anything noteworthy on the specific domain of Responsible GenAI.

In the past, we trusted the service producer/provider to setup the necessary discipline, oversight, and ongoing monitoring to ensure only responsible AI products were being offered to consumers (that was my experience from my? Capital One days). That is just not good enough any more! The ease with which anybody can now build, deploy, and access GenAI services, we are going to ...

... need something that is as universal as an electrical "circuit breaker" to detect and prevent harmful outcomes for the human race.

So as a product guy, here is how I would approach the problem. Lets build an independent Prompt Evaluation API and a Response Evaluation API, which every "in market" language model service (LLM) provider must use. The purpose of this model will be to embed greater accountability into AI interactions, making them safer, more efficient, and ultimately more beneficial to users. It will have the ability to reject/stop the process flow at the request and the response stages. These APIs will be a pair of model endpoints developed by a consortium of federal agencies, academic institutions, and public policy think tanks.

The purpose of this model is to embed greater accountability into AI interactions, making them safer, more efficient, and ultimately more beneficial to users.

The Prompt Evaluation API analyzes the initial prompts given to the AI, ensuring they adhere to ethical guidelines, appropriateness, and are free from any harmful intent or misinformation. By monitoring the input, we preemptively eliminate harmful or unwanted AI behavior that could stem from unsuitable prompts.

On the other hand, the Response Evaluation API inspects AI-generated responses. It assesses the responses for accuracy, relevance, safety, and compliance with ethical norms before they reach the end-user. This double-layered scrutiny reduces the chances of inappropriate AI responses, enhancing user trust and reliability in AI systems.

In a rapidly evolving AI landscape, every LLM service provider's responsibility should be to foster a safe, reliable, and beneficial environment for users. This model is a step towards that future, offering a dependable way to regulate AI input and output, reduce harmful interactions, and build a more trustworthy AI.

I look forward to seeing the transformative impact of this model on the AI industry, reaffirming our commitment to responsible AI practices. Let's embrace this journey towards a safer, reliable, and more accountable AI ecosystem together.

#AIforGood #EthicalAI #aitransparency #productmanagement #productstrategy #llmops #GenAI #governance