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Responsible Explainable AI

“RXAI – Reasoning and optimising over large datasets, automatically and with insight.”

When integrating AI into business operations, productivity tools, and crucial decision-making processes to enhance value incrementally, it’s imperative to comprehend its operations and objectives. Are its decisions accurate and sensitive to biases? Does it respect privacy rights? Can you effectively oversee and regulate this potent technology without impeding growth or innovation? Across the globe, organizations acknowledge the necessity of Responsible and Explainable AI but vary in their progress along this path.

Responsible Explainable AI (RXAI) entails managing the risks associated with AI-based solutions. Now is the opportune moment to assess and enhance existing practices or develop new ones to responsibly leverage AI and anticipate forthcoming regulations. Early investment in Responsible AI can confer a competitive advantage that rivals may struggle to match.

Responsible Explainable AI (RXAI) opens the potential for a symbiotic relationship with technology, which allows us to stay true to our goals or objectives, whilst leveraging the most advanced and automated solutions from AI at scale, faster, and at reduced costs.

Why RXAI now?

Data science and artificial intelligence are becoming ever more prevalent as the technological advancements are resulting in more businesses turning to AI to complete complex tasks. This ranges from analysing large amounts of data, to making predictions, and to automating existing processes and decision making for increased efficiency. The greater the problem complexity, the greater the need for automated and explainable AI to help the user understand how and why an algorithm has reached a specific outcome. 

AI can be very effective to help making decisions, and ideally optimal decisions, at scales that are beyond human reasoning. It is also important to provide transparent AI algorithms so that key decision-makers can trust the software and the insights it provides. This may include what techniques are being employed to generate a set of results, giving the user necessary information on the purpose behind the findings as well as any potential method limitations that must be factored in. Eventually this approach will enable performance certificates for AI algorithms, not dissimilar to energy efficiency assessments of buildings and certification standards for technical appliances, and hence be important for the governance of AI technology. As such, one of the goals of RXAI is that of providing a meaning behind results that can lead to actionable instructions for decision makers.

 How to achieve RXAI?

To determine the efficacy of an explanation, it must be quantified by how well it communicates a sense of understanding of a generated outcome. For an AI platform, understanding can be achieved by providing the underlying explanatory factors of why a result or a decision has been reached. If used at scale on large datasets, AI can thus explain processes and decisions that humans cannot easily grapple by means of sheer, direct reasoning. It can additionally provide insight on optimal decisions that only a long-term (team of) user(s) can eventually intuit from prolonged analysis or usage. This information empowers a company to easily debug any outcomes that are misaligned with their goals, so that model performance is improved. Thus, with explainable AI, reciprocal learning can be implemented – customers are able to learn and understand their insights to further maximise their objectives.

Explainability is thus important in general, as it is specifically for Zinia and its users. With automated credit decisioning, for example, explaining the decisions put forward by the machine is essential from both compliance and ethical perspectives. Customers decision letters provide an explanation as for why an individual has received a certain credit rate. Zinia can truly empower data scientists or business users to reason on large databases and make optimal decisions that are otherwise unwieldy or that require competence and long-term understanding of the specifics of the business. The resulting feedback and suggested actions will thus be aligned with the desired business KPIs, so users can make data-driven informed decisions that lead to more successful and explainable outcomes. Consequently, users can directly discover useful insights and gain confidence in the results, as there is no need for more abstract/empirical analysis that may introduce bias. Our platform is also able to automatically identify key features from raw datasets and empowers non-specialist users to create a model that optimises a business objective. With a high level of visibility on both the input and output ends of the data analysis process, the user receives a comprehensive view of how a decision has been reached. For our case study, Zinia’s explanations can help clients better understand their lending agreement by revealing the wider business outcomes chain that led to this result. Furthermore, this can inform actionable instructions as they highlight features that should be optimised to advance their interests.


Author: Professor A.Abate –  Chief Scientist, Zinia                                                          

Date: 24/04/2024 

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