This article is intended to provide a general view of responsible AI. For more information about IBM's specific perspectives, see the AI Ethics page.
The widespread adoption of machine learning in the 2010s, driven by advances in big data and computing power, poses new ethical challenges such as bias, transparency and the use of personal data. AI ethics emerged as clear discipline as technology companies and AI research institutions responded to proactively managing AI efforts during this period.
According to Accenture Research, “Only 35% of global consumers who trust how AI technology is implemented by organizations believe that their organization must be responsible for the misuse of AI.”1 In this atmosphere, AI developers are encouraged to guide their efforts with a powerful and consistent ethical AI framework.
This is especially true for new types of generation AI, which are currently being adopted rapidly by companies. Responsible AI principles help employers to make the most of the potential of these tools and minimize unnecessary outcomes.
AI must be trustworthy and must be transparent for stakeholders to trust AI. Technology companies need to be clear about who is training AI systems, the data used in their training, and most importantly, what came into the algorithm recommendations. If you use AI to make important decisions, it must be explained.
