AI-informed decision making based on decision field theory

机器学习（ML）、深度学习（DL）和人工智能（AI）为推荐系统（视频和音乐内容的播放列表生成器、社交媒体和网络服务平台的内容推荐器等）、几种类型的识别（例如人脸、图像、语音）和自动驾驶汽车等的发展做出了显著贡献。利用深度神经网络（dnn），研究人员在图像识别方面取得了比人类参与者更高的准确性，预测了药物的生物分子靶点或对人类健康有严重影响的环境化学物质，分别赢得了默克分子活动挑战赛和2014年Tox21数据挑战赛。尽管这些方法在几个领域取得了成功，但最近也有一些案例表明这些方法彻底失败了。例如，最近优步（Uber）的自动驾驶汽车撞死了一名行人，或者IBM沃森（Watson For Oncology）的人工智能（AI）给出了可能致命的癌症治疗建议。理解哪里出了问题并非易事，因为可解释性仍然是人工智能的核心挑战。每当政府、公共和私营部门使用人工智能来做出对人类科学和行为科学产生影响的决定时，缺乏可解释性就变得尤为重要，因为错误或误导性的决定或无法理解其机制可能会在许多领域（医疗、零售和产品供应等）造成严重后果。为了使强大的人工智能工具产生的结果更具可解释性、可靠性和可问责性，它们应该解释如何以及为什么做出特定的决策，例如，哪些属性在决策中很重要，以及有哪些信心。已经有一些努力来提高人工智能的可解释性，其中大多数都集中在增强dnn的可解释性和透明度上，参见，例如英国皇家学会的政策简报“可解释的人工智能：基础”（https://royalsociety.org/ai-interpretability）。本项目从不同的角度为这一努力做出了贡献。我们的目标是执行由决策场理论（DFT）驱动的人工智能知情决策，提出一套新的我们称之为人工智能知情DFT驱动的决策模型。这些模型通过将来自DFT的随机过程与ML工具相结合，将人类行为与人工智能相结合，并具有具有可解释参数的独特特征。一方面，我们将泛化DFT模型类以再现感兴趣的特征和行为，并运行ML和推理方法（主要是基于无似然的）来估计潜在的可解释DFT模型参数。另一方面，我们将使用黑盒DNN模型作为可解释DFT模型的代理（即近似）模型（相对于上述政策简报的表1具有相反的作用），并使用它们来学习感兴趣的过程，并做出由DFT驱动的知情预测（即决策）。因此，通过使用人工智能来学习这些过程，估计它们的参数并做出预测，我们将向最终用户解释为什么以及如何做出特定的决定，这是可解释的人工智能知情决策模型的关键特征。

Machine learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) have notably contributed to the development of recommender systems (playlist generators for video and music contents, content recommenders for social media and web services platforms, etc.), several types of recognition (e.g. face, image, speech), and self-driving cars, among many others. Using deep neural networks (DNNs), researchers have achieved higher accuracy than human participants in image recognition, have predicted the biomolecular target of a drug or which environmental chemicals are of serious concern to the human health, winning the Merck Molecular Activity Challenge, and the 2014 Tox21 data challenge, respectively.Despite their success across several fields, there have been a few recent cases where these approaches have drastically failed. For example, take the recent case of Uber's self-driving car that killed a pedestrian or IBM Watson's AI (Watson for Oncology), which gave potentially fatal cancer treatment recommendations. Understanding what went wrong is not an easy task, as explainability remains a core challenge in AI. The lack of explainability becomes especially crucial whenever AI is used, e.g. by governments, public and private sectors, to make decisions having an impact on human and behavioural sciences in general, since wrong or misleading decisions or the inability to understand their mechanisms may lead to dramatic consequences in many areas (medical treatment, retail and products supply, etc.). To make the results produced by powerful AI tools more interpretable, reliable and accountable, they should explain how and why a particular decision was made, e.g. which attributes were important in the decision making and with which confidence. There have been several efforts to improve the explainability of AI, most of them focusing on enhancing the explainability and transparency of DNNs, see, e.g. the Policy briefing "Explainable AI: The basic" from the Royal Society (https://royalsociety.org/ai-interpretability). This project contributes to this effort from a different perspective. Our goal is to perform AI-informed decision making driven by Decision Field Theory (DFT), proposing a new set of what we call AI-informed DFT-driven decision-making models. Such models integrate human behaviour with AI by combining stochastic processes coming from DFT with ML tools and have the unique feature of having interpretable parameters. On the one hand, we will generalise the class of DFT models to reproduce characteristics and behaviour of interest and run ML and inferential approaches (mainly likelihood-free based) to estimate the underlying interpretable DFT model parameters. On the other hand, we will use black-box DNN models as proxy (i.e. approximating) models of the interpretable DFT models (with a reversed role with respect to Table 1 of the above-mentioned policy briefing) and use them to learn the processes of interest and make informed predictions (i.e. decisions) driven by DFT. Hence, by using AI to learn these processes, estimating their parameters and making predictions, we will shed light on explaining to the end user why and how a particular decision was made, a crucial feature of interpretable AI-informed decision-making models.