• Unboxing the “Black Box”: The Need for Explainability

    Staying ahead of the game with AI (Part 2)


    This is part 2 of the series answering one of the most popular questions on Artificial Intelligence (AI). What is the reasoning behind the claims of the “black box problem” by data scientists when it comes to machine learning and AI? Read Staying ahead of the game with AI (Part 1): Unlock the black box. 

    The explainability of artificial intelligence or machine learning (AI/ML) systems outcomes often becomes a concern when used in the financial sector. ML models are often referred to as black boxes because they are not directly explainable by the user (Guidotti and others 2019). 

    Explainability is a complex and multifaceted issue. There are several reasons why ML models are frequently considered to be black boxes:

    1. they are complicated and cannot be easily interpreted;
    2. their input signals/source might not be known;
    3. they are an ensemble of models rather than a single independent model; and
    4. the prediction reasoning is unknown.

    Without unboxing the “Black Box”, the trust and appropriateness of ML credit decisions are difficult for industry players to accept. Robustness of the model may be undermined due to exposure to vulnerabilities such as biased data, unsuitable modeling techniques, or incorrect credit decision making.

    AI comes with a promise to help businesses fulfill their business goals. But without a correct demonstration of AI and how it is programmed, people might have lesser trust for AI-powered systems.

    This is where Juris Mindcraft succeeds –  it is an explainable AI, which means it can provide an explanation behind every decision reached. Juris Mindcraft allows users to:

    • leverage on feature importance comparison which displays the importance of each of the features or attributes,
    • use statistics or past data to support the reason behind prediction, and
    • see the exact order of the top predictors at a customer level, giving them confidence in underlying signals or analysis driving model performance.

    Juris Mindcraft is JurisTech’s very own proprietary AI, an automated Machine Learning (autoML) and artificial intelligence (AI) platform. It uses advanced machine learning (ML) techniques to build powerful AI models. An effortless AI that enables enterprises especially banks and financial institutions to make intelligent business decisions and gain insights to solve real-world problems.

    Thanks to Juris Mindcraft, we can now provide an explanation behind every decision reached. For the other “Black boxed” AI’s in the market, we usually do not have the idea of how the dataset turned into information. However, Juris Mindcraft can evaluate and interpret the model with all types of explainability methods such as evaluation metrics and confusion matrix and etc. or by the model behaviours. This can improve human readability and understand why an AI/ML model is giving specific results. On top of that, you can debug unbias or odd behaviours from the model to avoid discrimination and sociteal bias. Furthermore, Juris Mindcraft has out-of-the-box capabilities such as machine learning, cognitive behavioral scoring, and continuous and autonomous self-learning to improve accuracy. Its capabilities stretch in all areas of the banking industry and it can be applied to other fields such as onboarding customers, product recommendations, and collections.

    Curious to learn more about the capability of Juris Mindcraft, request a free demo now at contact@juristech.net


    About JurisTech

    JurisTech (Juris Technologies) is a leading Malaysian-based fintech company, specialising in enterprise-class software solutions for banks, financial institutions, and telecommunications companies in Malaysia, Southeast Asia, and beyond.

    By | 2024-01-24T10:19:32+00:00 16th August, 2022|Artificial Intelligence, Insights|

    About the Author:

    Sophia is with the Presales team at JurisTech. Sophia is a tech enthusiast who studied Economics and Psychology at the University of Washington, Seattle. Once upon a time, she was an Analyst at Bank Negara Malaysia for 7 years. She counts reading, research, and music among her myriad interests.