Explainable AI (XAI): Trust through Transparency

XAI bolsters user acceptance and is required to fulfill regulatory requirements

The use of artificial intelligence (AI) opens up opportunities for most industries in terms of promising breakthroughs in speed, scalability, decision-making and product personalization. The application of AI has repeatedly driven fundamental changes in processes or otherwise along the value chain of companies across the world: product development, profiling & personalization, fully automated data processing. As speed, quality and cost factors improve, customer expectations re-adjust, motivating a perpetual cycle of innovation. Adoption of AI can also create conflict between innovation, strategy, regulation as well as compliance violations.

XAI ("Explainable AI") is an active area of research with a colourful array of methods seeking to cast light into black box machine learning models. The unique motivations and challenges surrounding model explainability at a global or at a local level each require dedicated approaches to provide any satisfactory result. These approaches also require perspective to prevent application out of context and thereby risking misinterpretation of models. Just as a three-dimensional object can only truly be perceived by viewing at different angles, models can only truly be interpreted by applying a comprehensive set of techniques, each within its boundary of applicability.

Artificial intelligence must be transparent in order to gain widespread acceptance, winning the trust of the full spectrum of stakeholders – developers, subject-matter experts, management, auditors, regulators, employees and customers. XAI methodologies and tools can play a central role toward achieving this acceptance – as well as to improving the quality of AI through the additional transparency and understanding. Highly regulated industries, such as banking, insurance, pharmaceuticals and automotive will all benefit from explainable AI, enabling innovation while managing liability risk and ensuring compliance to the regulatory terrain within which they operate. As AI becomes more transparent and comprehensible, quality and reliability will improve, use-cases will proliferate and its power to transform industry, science and economy will expand.

Download the Deloitte Whitepaper “Bringing Transparency zo Machine Learning Models & Predictions” and learn more about Explainable AI (XAI).

Bringing Transparency to Machine-Learning Models & Predictions