FAIR: Framework for responsible adoption of Artificial Intelligence in the financial seRvices industry

人工智能技术有可能通过新颖的个性化产品和服务，提高成本效益，增强消费者信心以及更有效地管理金融，系统和安全风险，为英国金融服务业带来显着增长。然而，目前在采用这些技术方面存在重大障碍，这是由于在翻译高级原则方面的能力不足（其中有丰富的）关于值得信赖的设计，开发和部署人工智能技术（“值得信赖的人工智能”），包括安全性，公平性，隐私意识，安全性，透明度，问责制，鲁棒性和弹性，到具体工程，治理，和商业实践。在为值得信赖的人工智能开发一个可操作的框架时，需要克服的主要研究挑战在于解决在考虑特定应用设置时所有这些方面之间不可避免地出现的紧张关系和权衡。例如，降低系统性风险可能需要产生安全风险的数据共享;测试算法的公平性可能需要收集更敏感的个人数据;提高预测模型的准确性可能会对公平对待客户构成威胁;提高透明度可能会使系统被敌对行为者“玩弄于股掌之间”，造成易受全系统风险影响的脆弱性。这带来了一个与之相匹配的业务挑战。采用人工智能方法的金融服务提供商将在关键业务领域经历深刻的转变，因为客户参与，风险，决策，合规和其他功能过渡到主要由数据驱动和算法介导的流程，涉及越来越少的人为监督。然而，只有在能够自信地保证目标人工智能应用程序的可信度的情况下，才能成功地调整当前的创新、治理、伙伴关系和利益相关者关系管理实践以应对这些变化。我们的研究假设是基于认识到这些研究和业务挑战之间的密切相互作用：人工智能中的可信度概念只能在具体的商业环境中充分运作，以提供必要的保证，从而产生具体的要求，将基础研究转化为实际的解决方案，同时平衡所有这些潜在冲突的要求的解决方案。认识到紧密行业的重要性-为了促进学术界的合作，以实现这一领域的负责任创新，该合作伙伴关系将在现有图灵-汇丰合作伙伴关系的基础上，开展一项由工业驱动的跨学科研究的系统计划。它将使金融服务提供商能够实现可信赖的数据驱动决策，同时通过提高我们对顺序数据驱动决策，隐私和安全增强技术，平衡道德，商业和监管要求的方法，微观和宏观层面风险之间的联系，人工智能模型的验证和认证方法，以及合成数据生成。帮助以安全的方式推动整个行业的创新，这将有助于建立适当的监管和治理框架，以及一个通用的“沙箱”环境，以实现对新兴解决方案的实验，并在现实世界的商业环境中测试其可行性。这也将为影响预测和利益攸关方本着负责任的研究和创新精神持续参与奠定基础。

AI technologies have the potential to unlock significant growth for the UK financial services sector through novel personalised products and services, improved cost-efficiency, increased consumer confidence, and more effective management of financial, systemic, and security risks. However, there are currently significant barriers to adoption of these technologies, which stem from a capability deficit in translating high-level principles (of which there is an abundance) concerning trustworthy design, development and deployment of AI technologies ("trustworthy AI"), including safety, fairness, privacy-awareness, security, transparency, accountability, robustness and resilience, to concrete engineering, governance, and commercial practice. In developing an actionable framework for trustworthy AI, the major research challenge that needs to be overcome lies in resolving the tensions and tradeoffs which inevitably arise between all these aspects when considering specific application settings.For example, reducing systemic risk may require data sharing that creates security risks; testing algorithms for fairness may require gathering more sensitive personal data; increasing the accuracy of predictive models may pose threats to fair treatment of customers; improved transparency may open systems up to being "gamed" by adversarial actors, creating vulnerabilities to system-wide risks. This comes with a business challenge to match. Financial service providers that are adopting AI approaches will experience a profound transformation in key areas of business as customer engagement, risk, decisioning, compliance and other functions transition to largely data-driven and algorithmically mediated processes that involve less and less human oversight. Yet, adapting current innovation, governance, partnership and stakeholder relation management practice in response to these changes can only be successfully achieved once assurances can be confidently given regarding the trustworthiness of target AI applications. Our research hypothesis is based on recognising the close interplay between these research and business challenges: Notions of trustworthiness in AI can only be operationalised sufficiently to provide necessary assurances in a concrete business setting that generates specific requirements to drive fundamental research into practical solutions, with solutions which balance all of these potentially conflicting requirements simultaneously.Recognising the importance of close industry-academia collaboration to enable responsible innovation in this area, the partnership will embark on a systematic programme of industrially-driven interdisciplinary research, building on the strength of the existing Turing-HSBC partnership. It will achieve a step change in terms of the ability of financial service providers to enable trustworthy data-driven decision making while enhancing their resilience, accountability and operational robustness using AI by improving our understanding of sequential data-driven decision making, privacy- and security- enhancing technologies, methods to balance ethical, commercial, and regulatory requirements, the connection between micro- and macro-level risk, validation and certification methods for AI models, and synthetic data generation. To help drive innovation across the industry in a safe way which will help establish the appropriate regulatory and governance framework, and a common "sandbox" environment to enable experimentation with emerging solutions and to test their viability in a real-world business context. This will also provide the cornerstone for impact anticipation and continual stakeholder engagement in the spirit of responsible research and innovation.