Artificial Intelligence for a Better Future

This open access book proposes a novel approach to Artificial Intelligence (AI) ethics. AI offers many advantages: better and faster medical diagnoses, improved business processes and efficiency, and the automation of boring work. But undesirable and ethically problematic consequences are possible too: biases and discrimination, breaches of privacy and security, and societal distortions such as unemployment, economic exploitation and weakened democratic processes. There is even a prospect, ultimately, of super-intelligent machines replacing humans. The key question, then, is: how can we benefit from AI while addressing its ethical problems? This book presents an innovative answer to the question by presenting a different perspective on AI and its ethical consequences. Instead of looking at individual AI techniques, applications or ethical issues, we can understand AI as a system of ecosystems, consisting of numerous interdependent technologies, applications and stakeholders. Developing this idea, the book explores how AI ecosystems can be shaped to foster human flourishing. Drawing on rich empirical insights and detailed conceptual analysis, it suggests practical measures to ensure that AI is used to make the world a better place

Algorithmic Fairness, Algorithmic Discrimination

There has been an explosion of concern about the use of computers to make decisions affecting humans, from hiring to lending approvals to setting prison terms. Many have pointed out that using computer programs to make these decisions may result in the propagation of biases or otherwise lead to undesirable outcomes. Many have called for increased transparency and others have called for algorithms to be tuned to produce more racially balanced outcomes. Attention to the problem is likely to grow as computers make increasingly important and sophisticated decisions in our daily lives. Drawing on both the computer science and legal literature on algorithmic fairness, this paper makes four major contributions to the debate over algorithmic discrimination. First, it provides a legal response to a recent flurry of work in computer science seeking to incorporate fairness in algorithmic decision-makers by demonstrating that legal rules generally apply in the form of side constraints, not fairness functions that can be optimized. Second, by looking at the problem through the lens of discrimination law, the paper recognizes that the problems posed by computational decisionmakers closely resemble the historical, institutional discrimination that discrimination law has evolved to control, a response to the claim that this problem is truly novel because it involves computerized decision-making. Third, the paper responds to calls for transparency in computational decision-making by demonstrating how transparency is unnecessary to providing accountability and that discrimination law itself provides a model for how to deal with cases of unfair algorithmic discrimination, with or without transparency. Fourth, the paper addresses a problem that has divided the literature on the topic: how to correct for discriminatory results produced by algorithms. Rather than seeing the problem as a binary one, I offer a third way, one that disaggregates the process of correcting algorithmic decision-makers into two separate decisions: a decision to reject an old process and a separate decision to adopt a new one. Those two decisions are subject to different legal requirements, providing added flexibility to firms and agencies seeking to avoid the worst kinds of discriminatory outcomes. Examples of disparate outcomes generated by algorithms combined with the novelty of computational decision-making are prompting many to push for new regulations to require algorithmic fairness. But, in the end, current discrimination law provides most of the answers for the wide variety of fairness-related claims likely to arise in the context of computational decision-makers, regardless of the specific technology underlying them

Fairness in Information Access Systems

Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that have been developed for studying other machine learning systems. While fair information access shares many commonalities with fair classification, the multistakeholder nature of information access applications, the rank-based problem setting, the centrality of personalization in many cases, and the role of user response complicate the problem of identifying precisely what types and operationalizations of fairness may be relevant, let alone measuring or promoting them. In this monograph, we present a taxonomy of the various dimensions of fair information access and survey the literature to date on this new and rapidly-growing topic. We preface this with brief introductions to information access and algorithmic fairness, to facilitate use of this work by scholars with experience in one (or neither) of these fields who wish to learn about their intersection. We conclude with several open problems in fair information access, along with some suggestions for how to approach research in this space

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

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Robotics, AI, and Humanity

This open access book examines recent advances in how artificial intelligence (AI) and robotics have elicited widespread debate over their benefits and drawbacks for humanity. The emergent technologies have for instance implications within medicine and health care, employment, transport, manufacturing, agriculture, and armed conflict. While there has been considerable attention devoted to robotics/AI applications in each of these domains, a fuller picture of their connections and the possible consequences for our shared humanity seems needed. This volume covers multidisciplinary research, examines current research frontiers in AI/robotics and likely impacts on societal well-being, human – robot relationships, as well as the opportunities and risks for sustainable development and peace. The attendant ethical and religious dimensions of these technologies are addressed and implications for regulatory policies on the use and future development of AI/robotics technologies are elaborated

The Impact of Place-Based Services on Child Maltreatment: Evaluation Through Big Data Linkage and Analytics

There is a clear evidence that place is one factor associated with rates of child maltreatment and that rates of child abuse differ between different neighbourhoods and communities. Although there are few place-based initiatives (PBIs) focused specifically on child maltreatment, there is an increasing policy and research interest on PBIs that address a range of problems for children and families in disadvantaged communities. Evaluating the effectiveness of these initiatives is extremely challenging, both methodologically and ethically, but one potential way forward is to use linked administrative data to track outcomes of children and families. This chapter discusses the opportunities and challenges for the use of administrative data linkage in the evaluation of PBIs. The chapter is informed by interviews with 12 Australian experts on the use of ‘big data’ in public policy