{"id":6242,"date":"2023-09-20T08:00:00","date_gmt":"2023-09-20T15:00:00","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=6242"},"modified":"2023-09-20T08:00:00","modified_gmt":"2023-09-20T15:00:00","slug":"developing-ethical-ai","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/au\/news\/stories\/developing-ethical-ai\/","title":{"rendered":"How Salesforce Develops Ethical Generative AI from the Start"},"content":{"rendered":"\n

Across countless verticals, companies are actively embracing generative AI<\/a> to power business growth. In fact, generative AI may inject over $4 trilion annually into the world\u2019s economy<\/a>.<\/p>\n\n\n\n

While 67% of senior IT leaders prioritize generative AI for their businesses \u2014 with more than 30% naming it as their key consideration<\/a> \u2014 a lack of trust among their customers may pose a challenge. According to recent data<\/a>, nearly 75% of consumers remain skeptical of AI-generated content\u2019s accuracy and trustworthiness. Similarly, more than 60%<\/a> of skilled professionals believe they lack the necessary skills to effectively and safely use AI.<\/p>\n\n\n\n

Building trustworthy generative AI requires a firm foundation at the inception of AI development. Earlier this year, Salesforce published an overview<\/a> of our five guidelines for the ethical development of generative AI that builds on our Trusted AI Principles<\/a> and AI Acceptable Use Policy<\/a>. The guidelines focus on accuracy, safety, transparency, empowerment, and sustainability \u2014 helping Salesforce AI engineers create ethical generative AI right from the start.<\/p>\n\n\n\n

Powering generative AI model accuracy<\/strong><\/h2>\n\n\n\n

Salesforce customers use generative AI models to handle tasks ranging from automating sales processes to delivering personalizing customer service interactions to creating customized buyer and merchant experiences \u2014 and that\u2019s just the tip of the iceberg. Consequently, highly accurate model responses typically result in enhanced communications and improved customer experiences. To validate model accuracy, Salesforce uses the dual approach of grounding output in customer data points with continuous iterative improvement through customer input.<\/p>\n\n\n\n

Dynamic grounding \u2014 the process of restricting the generative AI model to use factual, up-to-date data instead of older datasets \u2014 remains key for delivering highly relevant and accurate output. For example, by grounding a generative AI model with a particular customer\u2019s data \u2014 such as their knowledge articles about service queries \u2014 the answers being generated are accurate and come from a trusted source.<\/p>\n\n\n\n

Customer feedback also helps validate model accuracy. Oftentimes, Salesforce customers provide explicit qualitative and quantitative feedback on how they use or alter their AI outputs, which helps refine our product design and improve the end result.<\/p>\n\n\n\n