Transparent, Sustainable, Practical, Human-Centered, in one word – Responsible. That’s the guiding principle behind AI practices and standards in the enterprise data world.
Expert.ai details its position and approach towards responsible AI. By providing a framework for delivering AI-based natural language solutions for the enterprise, expert.ai makes it easier for organizations to ensure their AI projects are transparent (or explainable), sustainable and more efficient, practical and always centered around people needs.
As the use of AI is growing in the mainstream, organizations are looking for solutions that can ensure both business benefits as well as accountability. The problem is that AI-powered tools for natural language understanding and processing (NLU/NLP) are often exclusively based on machine learning (ML) techniques. Built like black-boxes, ML-only language solutions are wrought with failure and contain dark algorithms that cannot be easily identified, explained and fixed. In addition, these ML-only models typically trained on massive data sets via large language models (LLMs) that require excessive amounts of compute power, resulting in massive carbon footprints and negative impact on the environment.
To help organizations understand the most common ML limitations as well as address the main issues related to the environment, social responsibility, bias, equity and data integrity, the expert.ai Green Glass Approach offers a concrete framework to advance responsible AI, while urging industry-wide emphasis be placed on ensuring NL deployments are:
- Transparent: The ‘black box’ phenomenon where it cannot be determined how a model arrived at a particular decision is unacceptable. All AI must be explainable and accountable.
- Sustainable: Some of the world’s leading tech companies are still investing in LLMs; however, organizations need models that perform well in situations where little training data is available and at the same time are more cost effective and energy efficient. Expert.ai R&D Labs tests demonstrated that high levels of accuracy can be obtained using alternative methods, simultaneously reducing energy consumption and pollution produced by 100 times in the training phase and about 25% less in the prediction phase.
- Practical: AI-powered solutions must solve real-world problems and provide tangible value to actual users;these problems must be solved at scale not as one-off experiments or only in theory.
- Human-centered: AI must humanize the work people perform allowing them to be more productive, efficient and happier in both their jobs as well as their lives in general. Solutions cannot be exploitative to any group and operate in fair manner, without bias. Also, a fine-tuning of results with a “human-in-the-loop” ensures data and inputs can be monitored and refined by users. So, if an outcome is misleading, biased, or wrong, users can intervene to prevent future mistakes and make sure to achieve the success metrics most valuable for each use case
“In embracing continuous innovation while enhancing our natural language capabilities, we always have and will prioritize responsible AI,” said Walt Mayo, CEO of expert.ai. “With the Green Glass Approach, we want to confirm our commitment to help organizations put in practice responsible AI initiatives, providing educational resources, fostering dialogue, and driving awareness to grow natural language AI technology the right way, on the right path.”
For more information about expert.ai’s Green Glass Approach and how to make AI responsible, accountable and environmentally friendly, please visit: The Green Glass Approach to Responsible AI website page.