This book proposes a regulatory system for ensuring that AI makes fair decisions. No one wants to be the subject of an unfair decision made by an AI, and fairness is so important to society that we are likely to want to regulate to demand it. But how? This book attempts to answer that question. The aim of regulation must be for an AI's decisions to match the human conception of fairness. To understand what that is, the book proposes a holistic understanding of fairness, which tells us what regulation must try to achieve. However, regulation is not an abstract activity – it regulates how humans behave, and the humans in question are those who develop and use AI for decision-making. Thus the book investigates how those humans are attempting to achieve AI fairness. It finds that there is a serious mismatch between how technologists conceptualise fairness, compared to other humans. How can AI regulation bridge this gap? Traditional models of regulation cannot solve this problem. Fairness is too nuanced, too contextual, and is ultimately a human emotional response. Instead the book proposes to place the responsibility on the AI community to explain and justify their efforts to achieve fairness, basing regulatory and legal responses on how well that explanation deals with the risks that particular AI presents, and whether the AI operates in accordance with the explanation in use. The book concludes by examining how far this regulatory model might be useful for some of the other social problems which AI generates. An original and significant contribution to the literature on AI regulation, this book is a must-read for those working in the areas of law, regulation, and technology.

In theory and practice, the notion of fairness is far from simple. The principle is often elusive and subject to confusion, even in institutions of law, usage, and custom. In Fairness, Nicholas Rescher aims to liberate this concept from misunderstandings by showing how its definitive characteristics prevent it from being absorbed by such related conceptions as paternalistic benevolence, radical egalitarianism, and social harmonization. Rescher demonstrates that equality before the state is an instrument of justice, not of social utility or public welfare, and argues that the notion of fairness stops well short of a literal egalitarianism. Rescher disposes of the confusions arising from economists' penchant to focus on individual preferences, from decision theorists' concern for averting envy, and from political theorists' sympathy for egalitarianism. In their place he shows how the idea of distributive equity forms the core of the concept of fairness in matters of distributive justice. The coordination of shares with valid claims is the crux of the concept of fairness. In Rescher's view, this means that the pursuit of fairness requires objective rather than subjective evaluation of the goods being shared. This is something quite different from subjective equity based on the personal evaluation of goods by those laying claim to them. Insofar as subjective equity is a concern, the appropriate procedure for its realization is a process of maximum value distribution. Further, Rescher demonstrates that in matters of distributive justice, the distinction between new ownership and preexisting ownership is pivotal and calls for proceeding on very different principles depending on the case. How one should proceed depends on context, and what is adjudged fair is pragmatic, in that there are different requirements for effectiveness in achieving the aims and purposes of the sort of distribution that is intended. Rescher concludes that fairness is a fundamentally ethical concept. Its distinctive modus operandi contrasts sharply with the aims of paternalism, preference-maximizing, or economic advantage. Fairness will be of interest to philosophers, economists, and political scientists.

Fairness is an increasingly important topic as machine learning and AI more generally take over the world. While this is an active area of research, many realistic best practices are emerging at all steps along the data pipeline, from data selection and preprocessing to blackbox model audits. This book will guide you through the technical, legal, and ethical aspects of making your code fair and secure while highlighting cutting edge academic research and ongoing legal developments related to fairness and algorithms. There is mounting evidence that the widespread deployment of machine learning and artificial intelligence in business and government is reproducing the same biases we are trying to fight in the real world. For this reason, fairness is an increasingly important consideration for the data scientist. Yet discussions of what fairness means in terms of actual code are few and far between. This code will show you how to code fairly as well as cover basic concerns related to data security and privacy from a fairness perspective.

This book presents a historically focused account of the concepts of 'reasonableness' and 'fairness', showing how they are subject to historical evolution.

The social organization of teaching and learning, particularly in classrooms, has not yet been recognized as a foundational element of education. However, social constructionist views of human development, cognition, and schooling, as well as the increasing challenges of cultural and linguistic diversity, make it a vital concern for teachers, researchers, and policymakers. This book introduces the concept of educational social organization, assembles the pertinent theory and evidence, and suggests future directions for training and policy. }The four goals of school reform--academic excellence, fairness, inclusion and harmony--can be achieved simultaneously, by transforming the final common pathway of all school reform--instructional activity. Teaching Transformed is a new vision for classrooms, based on consensus research findings and unified practice prescriptions, explained and justified by new developments in sociocultural theory, and clarified by an explicit five-phase developmental guide for achieving that transformation. Teaching Transformed is both visionary and practical, both theoretical and data-driven, and determined to create effective education for all students. Professional educators, parents, and any reader concerned with saving our schools will find this book necessary to understand our current plight, and to envision a realistic means of transformation.

In this book, the author argues that to achieve a fair global economy, there must be compensation of people harmed by their exposure to the global economy, but also equal division of the "gains of trade" across societies.

This book mainly aims at solving the problems in both cooperative and competitive multi-agent systems (MASs), exploring aspects such as how agents can effectively learn to achieve the shared optimal solution based on their local information and how they can learn to increase their individual utility by exploiting the weakness of their opponents. The book describes fundamental and advanced techniques of how multi-agent systems can be engineered towards the goal of ensuring fairness, social optimality, and individual rationality; a wide range of further relevant topics are also covered both theoretically and experimentally. The book will be beneficial to researchers in the fields of multi-agent systems, game theory and artificial intelligence in general, as well as practitioners developing practical multi-agent systems.

The purpose of this volume is to help jump-start an urgently needed conversation about fairness and justice in access to higher education to counteract the ubiquitous mantras of neoliberal globalization and managerialism. The book seeks to carve out a strong moral and normative basis for opposing mainstream developments that engender increasing inequality and market-dependency in higher education. The book’s chapters consider how different national communities channel access to higher education, what their “implicit social contracts” are, and what outcomes are produced by different policies and methods. The book is essential reading for scholars of higher education and students concerned with increasing inequality in a globalizing educational marketplace.