Diversity is about being able to manage and understand difference in order to drive real change; it needs a broad approach and to be consistently applied. The many components of diversity—including sex, gender, class, sexual orientation, ethnicity, religion, education, age, health, and income—indicate why bias can and does exist when teams are less inclusive and made up of similar individuals with a limited range of characteristics. Individuals are not one-dimensional; like an ecosystem, they have multiple inputs that make them who they are.
Embracing data insights from diverse teams helps to identify and mitigate biases in AI algorithms, thus leading to broader perspectives and fairer outcomes. These varied perspectives enhance problem solving and innovation by breaking free from the limitations of group think. Diverse data is necessary to guide business decisions that serve a broader range of users more equitably.
Building Trust Through Ethical Practices
Often, ethics comes hand-in-hand with diverse practices. Ethics as it relates to data and AI has a number of considerations. People are concerned about how data is used, so it’s important that businesses ensure personal data is collected, stored, and used responsibly. Being clear about how data is used and how decisions are made by AI improves transparency and builds trust. Since data is used in AI models, and since historic data has the potential for bias to be present, finding ways to avoid and mitigate bias to create fair and just outcomes becomes an ethical consideration.