A fiduciary is a trusted agent that has the legal duty to act with loyalty and care towards a principal that employs them. When fiduciary organizations interact with users through a digital interface, or otherwise automate their operations with artificial intelligence, they will need to design these AI systems to be compliant with their duties. This article synthesizes recent work in computer science and law to develop a procedure for designing and auditing Fiduciary AI. The designer of a Fiduciary AI should understand the context of the system, identify its principals, and assess the best interests of those principals. Then the designer must be loyal with respect to those interests, and careful in an contextually appropriate way. We connect the steps in this procedure to dimensions of Trustworthy AI, such as privacy and alignment. Fiduciary AI is a promising means to address the incompleteness of data subject's consent when interacting with complex technical systems.

Artificial Intelligence (AI) is taking on increasingly autonomous roles, e.g., browsing the web as a research assistant and managing money. But specifying goals and restrictions for AI behavior is difficult. Similar to how parties to a legal contract cannot foresee every potential "if-then" contingency of their future relationship, we cannot specify desired AI behavior for all circumstances. Legal standards facilitate robust communication of inherently vague and underspecified goals. Instructions (in the case of language models, "prompts") that employ legal standards will allow AI agents to develop shared understandings of the spirit of a directive that generalize expectations regarding acceptable actions to take in unspecified states of the world. Standards have built-in context that is lacking from other goal specification languages, such as plain language and programming languages. Through an empirical study on thousands of evaluation labels we constructed from U.S. court opinions, we demonstrate that large language models (LLMs) are beginning to exhibit an "understanding" of one of the most relevant legal standards for AI agents: fiduciary obligations. Performance comparisons across models suggest that, as LLMs continue to exhibit improved core capabilities, their legal standards understanding will also continue to improve. OpenAI's latest LLM has 78% accuracy on our data, their previous release has 73% accuracy, and a model from their 2020 GPT-3 paper has 27% accuracy (worse than random). Our research is an initial step toward a framework for evaluating AI understanding of legal standards more broadly, and for conducting reinforcement learning with legal feedback (RLLF).

How we can make "good" artificial intelligence, what does it mean for a machine to be ethical, and how can we use AI ethically? Good in the Machine – 2019's SCINEMA International Science Film Festival entry – delves into these questions, the origins of our morality, and the interplay between artificial agency and our own moral compass. Read on to learn more about AI ethics. Given a swell of dire warnings about the future of artificial intelligence over the last few years, the field of AI ethics has become a hive of activity. These warnings come from a variety of experts such as Oxford University's Nick Bostrom, but also from more public figures such as Elon Musk and the late Stephen Hawking.

One of the priorities announced in the 2021 Examination Priorities Report of the U.S. Securities and Exchange Commission's Division of Examinations ("EXAMS") is a review of robo-advisory firms that build client portfolios with exchange-traded funds ("ETF's") and mutual funds. EXAMS notes that these clients are almost entirely retail investors without investments large enough to support the costs of regular human investment advisers. EXAMS sees that the risks involved in these robo-advisor accounts pose particular issues, that retail clients may well not recognize. Accordingly, it may help to reflect on the Laws of Robotics invented by that science fiction author Isaac Asimov (for "I Robot," a short story in his 1950 collection), particularly the First Law: A robot may not injure a human being or, through inaction, allow a human being to come to harm. Investors may not understand the risks associated with specific investments; the risk profiles of mutual funds and of ETF's vary widely, from diversified to concentrated, from simple to complex strategies.

Artificial intelligence (AI) is an increasingly important technology within the investment management industry.1 AI has been used in a variety of ways--including as the newest strategy for attempts to "beat the market" by outperforming passive index funds that are benchmarked against the S&P 500, despite the long-standing finding that index funds consistently win that contest.2 Investment advisers who use AI should consider the unique issues the technology raises in light of an adviser's fiduciary duty to its clients. In this client alert, we provide an overview of how AI is being used by investment advisers, the fiduciary duties applicable to investment advisers, and particular issues advisers should consider in designing AI-based programs, to ensure they are acting in the best interests of their clients.3 Under federal law, an investment adviser is a fiduciary to its clients.8

Given a swell of dire warnings about the future of artificial intelligence over the last few years, the field of AI ethics has become a hive of activity. These warnings come from a variety of experts such as Oxford University's Nick Bostrom, but also from more public figures such as Elon Musk and the late Stephen Hawking. The picture they paint is bleak. In response, many have dreamed up sets of principles to guide AI researchers and help them negotiate the maze of human morality and ethics. Now, a paper in Nature Machine Intelligence throws a spanner in the works by claiming that such high principles, while laudable, will not give us the ethical AI society we need.

AI Ethics is now a global topic of discussion in academic and policy circles. At least 63 public-private initiatives have produced statements describing high-level principles, values, and other tenets to guide the ethical development, deployment, and governance of AI. According to recent meta-analyses, AI Ethics has seemingly converged on a set of principles that closely resemble the four classic principles of medical ethics. Despite the initial credibility granted to a principled approach to AI Ethics by the connection to principles in medical ethics, there are reasons to be concerned about its future impact on AI development and governance. Significant differences exist between medicine and AI development that suggest a principled approach in the latter may not enjoy success comparable to the former. Compared to medicine, AI development lacks (1) common aims and fiduciary duties, (2) professional history and norms, (3) proven methods to translate principles into practice, and (4) robust legal and professional accountability mechanisms. These differences suggest we should not yet celebrate consensus around high-level principles that hide deep political and normative disagreement.