Computability, Complexity, Consistency and Controllability: A Four C's Framework for cross disciplinary Ethical Algorithm Research
The ethical consequences, constraints upon and regulation of algorithms arguably represent the defining challenges of our age, asking us to reckon with the rise of computational technologies whose potential to radically transforming social and individual orders and identity in unforeseen ways is already being realised. Fittingly, concurrent with the emergence of such epoch shaping technologies has emerged a rapidly expanding and multi disciplinary set of research disciplines focused on these very questions. As the inexorable march of computational technologies encroaches across society and academic disciplines, it is natural that diverse specialisations including computer science, moral philosophy, engineering, jurisprudence and economics should turn their attention to the algorithmic zeitgeist. Yet despite the multi disciplinary impact of this algorithmic turn, there remains some way to go in motivating cross disciplinary collaboration is crucial to advancing feasible proposals for the ethical design, implementation and regulation of algorithmic and automated systems. In this work, we provide a framework to assist crossdisciplinary collaboration by presenting a 'Four C's Framework' covering key computational considerations researchers across such diverse fields should consider when approaching these questions: (i) computability, (ii) complexity, (iii) consistency and (iv) controllability. In addition, we provide examples of how insights from ethics, philosophy and population ethics are relevant to and translatable within sciences concerned with the study and design of algorithms. Our aim is to set out a framework which we believe is useful for fostering cross disciplinary understanding of pertinent issues in ethical algorithmic literature which is relevant considering the feasibility of ethical algorithmic governance, especially the impact of computational constraints upon algorithmic governance. The rapid acceleration of artificial intelligence and algorithmically based technologies over the last several decades has led to an explosion in research programmes dedicated to framing, understanding and regulating the ethical, moral, philosophical and jurisprudential consequences of these powerful and epoch defining technologies. As computational and information sciences advance throughout all facets of social, economic and political dimensions of society, it is natural that responses to this technological imperative involve multidisciplinary research fields, Copyright (c) 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org) All rights reserved. including the burgeoning fields of fair machine learning (Chouldechova 2017; Caton and Haas 2020) algorithmic jurisprudence (Zalnieriute, Moses, and Williams 2019) interpretability and regulatory technology. The rise of such technologies and, moreover, the increasing envelopment of technical and epistemological practice by algorithmic technologies, a phenomenon we denote as the algorithmic turn (in homage to its linguistic namesake) is as profound as it is ubiquitous. The extraordinary reach of such technologies naturally motivates cross disciplinarity in such research programmes among ethicists, philosophers and computer scientists in order to grapple with the consequences of these technologies. While such cross collaboration has increased over several years, there remain challenges in translating between the epistemological citadels within which each discipline is resides. Though multidisciplinary in impact, research programmes often remain less cross disciplinary than called for. An example is within fair machine learning research, where technical results on the theory and mathematics of implementing fairness criteria algorithmically (to ameliorate bias, discrimination for example) tend to have limited, if any, engagement with the directly relevant and vast literature within ethics, social choice and decision theory, philosophy and jurisprudence. Of course, disciplinary silos are nothing new in the academe and specialised focus within disciplines is critical to pushing frontiers of research. Moreover, it is impossible for there to be a single, overarching, epistemological framework to provide direction to such a variegated topic as ethical algorithms. Nevertheless, as we contend in this work, there are benefits to be had by providing a framework where researchers in the sciences, economics and humanities can engage with common themes, protocols and domain criteria relevant to ethical consideration of algorithms. For example, seeking to ethically constrain or shape algorithms via design or regulation requires domain understanding of precisely what is meant by the term algorithm, along with familiarity with core concepts in computational science which inform algorithms themselves, such as computational complexity, computability and others. Similarly, programmes that seek to explore ethical constraints upon algorithms would benefit considerably from a consideration of how issues and dilemmas facing their field, such as the inevitability of trade offs among fairness criteria, probabilistic approaches to ethical classification and auditing have been explored in fields such as moral philosophy, population ethics and decision theory. Indeed the very validity of fairness criteria themselves, for example, is a rich and ancient debate across ethics and associated disciplines. Our answer to this challenge is to propose a framework based upon core computational science concepts of computability, complexity, consistency and controllability, which we name the 'Four C's Framework' The aim of the framework is to set out core concepts from computer science applicable to consideration of the ethical status and consequences of algorithms and automated systems, in order to provide researchers across disciplines with a series of considerations that we believe should be taken into account in any proposals for ethical regulation and assessment of algorithmic systems. Though care has been taken to address the nuances of various research fields, inevitably any crossdisciplinary synthesis that seeks to abstract key concepts will simplify, gloss over and omit any number of important results. Results and Contributions The contributions of our paper relative to the state of the art are as follows: 1. Development of a holistic framework to assist crossdisciplinary collaboration among researchers by setting out key technical research criteria concerning computability, complexity, consistency and controllability which should be taken into consideration when the ethical classification, regulation or assessment of algorithmic systems is undertaken. 2. Explication of connections among computational science, philosophical and economic science research in a way that identifies common problems both seek to address and that assists in motivating the translation of results in one field, say social choice and decision theory, to another, such as fair machine learning. Structure The structure of our paper is as follows. Part I sets out a framework for understanding how to classify and characterise algorithms as ethical. It introduces conceptualisations including ethical computation, ethical decision (deontological) procedures as distinct from ethical (consequentialist) outcomes. We connect ethical auditing to provability concepts and argue algorithmic governance in the limit will be itself necessarily algorithmic. Part II of the paper introduces the Four C's Framework described above. It unpacks concepts from computability and complexity theory, such as efficiency and feasibility as they pertain to ethical algorithmic analysis. The importancy of consistency (and completeness which we roll into consistency considerations) including the extent to which consistency in process or outcomes are ethically significant, is examined. We similarly explore how controllability is a critical element to consider when assessing ethical algorithms. We conclude the part with consideration of the ethical consequences for the inevitable reliance upon heuristics in any algorithmic regulatory context. Part III focuses on cross disciplinary connections between algorithmic epistemology and other disciplines, such as impossibility results in social choice and social welfare theorems, contextualising population ethics' debates among other examples. Part IV concludes our paper, exploring a number of cross disciplinary options for handling key challenges with ethical algorithms, including determining appropriate trade offs, uncertainty in algorithmic processes or outcomes, via analogy with existing institutions, such as the law, which face similar challenges. Part I: Ethical Algorithms
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