Understanding responsible AI design
In this section, we will explore the true meaning of responsible AI and delve into the fundamental design principles that should be considered while architecting generative AI solutions.
What is responsible AI?
As stated by Microsoft public documentation, “Responsible Artificial Intelligence (Responsible AI) is an approach to developing, assessing, and deploying AI systems in a safe, trustworthy, and ethical way.”It is like building and using smart computer programs (AI systems) in a way that is safe, fair, and ethical. Think of AI systems as tools created by people who make a lot of choices about how these tools should work. Responsible AI is about making these choices carefully to make sure AI acts in a way that is good and fair for everyone. It’s like guiding AI to always consider what is best for people and their needs. This includes making sure AI is reliable, fair, and transparent about how it works. Here are a few examples of the types of tools being developed in this space:
- Fair hiring tools: An AI tool used by a company to help choose job candidates. Responsible AI would ensure this AI doesn’t favor one group of people over another, making the hiring process fair for all applicants. For example, BeApplied, a startup in the RAI space, has developed a piece of ethical recruitment software designed to enhance hiring quality and increase diversity by reducing bias. It stands apart from traditional applicant tracking systems by incorporating fairness, inclusivity, and diversity as its core principles. The platform, underpinned by behavioral science, offers anonymized applications and predictive, skill-based assessments to ensure unbiased hiring. Its features include sourcing analysis tools to diversify talent pools, inclusive job description creation, anonymized skills testing for objective assessments, and data-driven shortlisting to focus purely on skills. BeApplied aims to create a fairer recruitment world, one hire at a time. They currently have some notable customers, such as UNICEF and England and Wales Cricket.
- Transparent recommendation systems: Think of a streaming service that suggests movies. Responsible AI would make this system clear about why it recommends certain movies, ensuring it’s not just promoting certain movies for unfair reasons. For example, LinkedIn is a notable example of a company that focuses on transparent and explainable AI systems, especially in its recommendation systems. Their approach ensures that AI system behavior and any related components are understandable, explainable, and interpretable. They prioritize transparency in AI to make their systems trustworthy and to avoid harmful bias while respecting privacy. For instance, they developed CrystalCandle, a customer-facing model explainer that creates digestible interpretations and insights reflecting the rationale behind model predictions. This tool is integrated with business predictive models, aiding sales and marketing by converting complex machine learning outputs into clear, actionable narratives for users.
