Artificial Intelligence (AI) has the potential to add $13 trillion to our global economy by 2030, according to McKinsey. Those who have successfully deployed AI see high returns on investment and increased customer satisfaction through more personalized, efficient AI tools. With AI investment growing and teams rushing to find ways to bring it into the core of their business, the intention of responsible AI is often an afterthought. This is one of the biggest ways companies set themselves up for failure.
While many people think of responsible AI as AI that performs ethically, it’s important to consider responsible practices from every direction. There are numerous questions around the ethical impact of AI such as how we make AI that works for everyone, or how we mitigate human bias from our machines when humans are building them. We all have a responsibility to create AI that is representative of our vision, and that we deploy world-class AI designed to work for everyone, in every market. Making the effort to reduce bias in AI is paramount so that AI recognizes everything and everyone equally.
In building responsible, effective AI, remember that there’s no trade-off. The steps you take to reduce bias are identical to the steps you should be taking anyway to build a high-quality model. Organizations investing in responsible, ethical, and representative AI have a better success rate. For an AI solution to work, and work well, it must work for everyone. A biased model that works for some users, and not others, is a failed model. Or a model that wasn’t sourced responsibly can be a poor reflection of company values and a nightmare with the media. It’s helpful to remember that AI reflects the people and the company that build it: when something goes wrong, it shows something may also be wrong internally.
In Embracing Responsible AI from Pilot to Production, you’ll be able to strategically launch an inclusive AI initiative that provides business value and reflects an ethics-first approach.
Embracing Responsible AI from Pilot to Production Will Cover:
- A breakdown of what responsible AI means
- The challenges of AI adoption and why responsible AI can alleviate these obstacles
- A five-step AI development process with ethical AI practices baked in
- What the explainability problem is and why it’s so important to address