A Hypothesis on Good Practices for AI-based Systems for Financial Time Series Forecasting: Towards Domain-Driven XAI Methods

Machine learning and deep learning have become increasingly prevalent in financial prediction and forecasting tasks, offering advantages such as enhanced customer experience, democratising financial services, improving consumer protection, and enhancing risk management. However, these complex models often lack transparency and interpretability, making them challenging to use in sensitive domains like finance. This has led to the rise of eXplainable Artificial Intelligence (XAI) methods aimed at creating models that are easily understood by humans. Classical XAI methods, such as LIME and SHAP, have been developed to provide explanations for complex models. While these methods have made significant contributions, they also have limitations, including computational complexity, inherent model bias, sensitivity to data sampling, and challenges in dealing with feature dependence. In this context, this paper explores good practices for deploying explainability in AI-based systems for finance, emphasising the importance of data quality, audience-specific methods, consideration of data properties, and the stability of explanations. These practices aim to address the unique challenges and requirements of the financial industry and guide the development of effective XAI tools.Comment: 11 pages, 1 figur

Human-Guided Phasic Policy Gradient in Minecraft: Exploring Deep Reinforcement Learning with Human Preferences in Complex Environments

This study presents a novel approach to enhancing the performance of artificial agents in complex environments like Minecraft, where traditional reward-based learning strategies can be challenging to apply. To improve the efficacy and efficiency of fine-tuning a foundation model for complex tasks, we propose the Human-Guided Phasic Policy Gradient (HPPG) algorithm, which combines human preference learning with the Phasic Policy Gradient technique. Our key contributions include validating the use of behavioral cloning to improve agent performance and introducing the HPPG algorithm, which employs a reward predictor network to estimate rewards based on human preferences. We further explore the challenges associated with the HPPG algorithm and propose strategies to mitigate its limitations. Through our experiments, we demonstrate significant improvements in the agent’s performance when executing complex tasks in Minecraft, laying the groundwork for future developments in reinforcement learning algorithms for complex, real-world tasks without defined rewards. Our findings contribute to the broader goal of bridging the gap between artificial agents and human-like intelligence

A Consequentialist Model for Just Social Contracts

abstract: The paper reviews some of the models of consequentialist justice, the nature of social contracts, and the social coordination of behaviors through social norms. The challenge with actualizing justice in many contemporary societies is the broad and often conflicting individual beliefs on rights and responsibilities that each member of a society maintains to describe the opportunities and compensations they attribute to themselves and others. This obscurity is compounded through a lack of academic or political alignment on the definition and tenets of justice. The result of the deficiency of commonality of the definition and tenants of justice often result in myopic decisions by individuals and discontinuity within a society that reduce the available rights, obligations, opportunities, and/or compensations that could be available through alternative modalities. The paper begins by assessing the challenge of establishing mutual trust in order to achieve cooperation. I then examine utility enhancement strategies available through cooperation. Next, I turn to models that describe natural and artificial sources of social contacts, game theory, and evolutionary fitness to produce beneficial results. I then examine social norms, including the dual inheritance theory, as models which can selectively reinforce certain cooperative behaviors and reduce others. In conclusion, a possible connection among these models to improve the overall fitness of society as defined by the net average increase in available utility, rights, opportunities, and compensations is offered. Through an examination of concepts that inform individual choice and coordination with others, concepts within social coordination, the nature of social contracts, and consequentialist justice to coordinate behaviors through social norms may illustrate an integrated perspective and, through additional examination, produce a comprehensive model to describe how societies could identify and foster just human coordination.Dissertation/ThesisMasters Thesis Philosophy 201