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A Type-2 Fuzzy Logic Approach to Explainable AI for regulatory compliance, fair customer outcomes and market stability in the Global Financial Sector
رویکرد منطق فازی نوع 2 به هوش مصنوعی قابل توضیح برای انطباق با مقررات ، نتایج عادلانه مشتری و ثبات بازار در بخش مالی جهانی-2020 The field of Artificial Intelligence (AI) is enjoying
unprecedented success and is dramatically transforming the
landscape of the financial services industry. However, there is a
strong need to develop an accountability and explainability
framework for AI in financial services, based on a risk-based
assessment of appropriate explainability levels and techniques by
use case and domain.
This paper proposes a risk management framework for the
implementation of AI in banking with consideration of
explainability and outlines the implementation requirements to
enable AI to achieve positive outcomes for financial institutions
and the customers, markets and societies they serve. The work
presents the evaluation of three algorithmic approaches (Neural
Networks, Logistic Regression and Type 2 Fuzzy Logic with
evolutionary optimisation) for nine banking use cases. We review
the emerging regulatory and industry guidance on ethical and safe
adoption of AI from key markets worldwide and compare leading
AI explainability techniques.
We will show that the Type-2 Fuzzy Logic models deliver very
good performance which is comparable to or lagging marginally
behind the Neural Network models in terms of accuracy, but
outperform all models for explainability, thus they are
recommended as a suitable machine learning approach for use
cases in financial services from an explainability perspective. This
research is important for several reasons: (i) there is limited
knowledge and understanding of the potential for Type-2 Fuzzy
Logic as a highly adaptable, high performing, explainable AI
technique; (ii) there is limited cross discipline understanding
between financial services and AI expertise and this work aims to
bridge that gap; (iii) regulatory thinking is evolving with limited
guidance worldwide and this work aims to support that thinking;
(iv) it is important that banks retain customer trust and maintain
market stability as adoption of AI increases. Keywords: Regulatory Compliance | Accountability and Explainability | Type-2 Fuzzy Logic | Neural Networks |
مقاله انگلیسی |
2 |
Hybrid Deep Learning Type-2 Fuzzy Logic Systems For Explainable AI
سیستم های منطق فازی نوع 2 یادگیری عمیق ترکیبی برای هوش مصنوعی قابل توضیح-2020 The recent years have witnessed a rapid rise in the use of Artificial Intelligence (AI) systems, in particular Machine Learning (ML) models. The vast majority of AI systems employ black box models that lack transparency in operation and decision making. This lack of transparency curtails the use of these AI systems in regulated applications (such as medical, financial applications, etc.) where it is important to understand the reasoning behind the predictions of the AI system. In these situations, interpretable models need to be used. However, interpretable models can turn into black-box models for high dimensional inputs. There are a variety of approaches that have been proposed to solve this problem. In this paper, we present a novel hybrid deep learning type-2 fuzzy logic system for explainable AI which addresses these challenges to provide a highly interpretable model that has reasonable performance when compared to the other black box models. Keywords: Explainable Artificial Intelligence | Interval Type-2 Fuzzy Logic System | Deep Learning |
مقاله انگلیسی |
3 |
An interval type-2 fuzzy logic based framework for reputation management in Peer-to-Peer e-commerce
یک نوع چارچوب بازه ای مبتنی بر منطق فازی نوع 2برای مدیریت شهرت در تجارت الکترونیک نظیر به نظیر-2016 During the last two decades, the Internet has changed people’s habits and improved their daily
life activities and services. In particular, the emergence of e-commerce provided manufactures and vendors with more business opportunities. This allowed customers to benefit from
a global, quicker and cheaper shopping environment. However, e-commerce is evolving from
a centralised approach, where consumers directly purchase products and services from businesses, to a Peer-to-Peer (P2P) perspective, in which customers buy and sell goods amongst
themselves. In P2P scenarios, it is crucial to protect both buyers and sellers (the peers) from
being victimised by possible fraud arising from the uncertainties, vagueness and ambiguities
that characterise the interactions amongst unknown business entities. For this reason, the
so-called reputation models are becoming a key architectural component of any e-commerce
portal. These systems are intended to evaluate the basic features of each entity (buyer, seller,
goods, etc.) involved in a given trading transaction in order to assess the trust level of the
given transaction and minimise fraud. However, in spite of their wide deployment, the reputation models need to be enhanced to handle the various sources of uncertainties in order
to produce more accurate outputs which will allow to increase the trust and decrease the
fraud levels within e-commerce systems. In this paper, we present an interval type-2 fuzzy
logic based framework for reputation management in (P2P) e-commerce which is capable of
better handling the faced uncertainties. We have carried out various experiments based on
eBay®-like transaction datasets which have shown that the proposed type-2 fuzzy logic based
system can provide better performance (in terms of malicious peer detection and exchanged
message overhead) when compared to the other well-known and heavily used approaches
like the eBay® approach, EigenTrust, PeerTrust as well as the type-1 fuzzy based counterpart
approach.
Keywords: Type-2 fuzzy sets | E-commerce | Trust management | Reputation management systems |
مقاله انگلیسی |