“Digital footprints” is an attractive, useful, and increasingly popular metaphor for thinking about Big Data. In this essay, I elaborate on this metaphor to highlight three relatively basic fallacies in the way we tend to think about Big Data: first, that they contain information on complete populations, or “N = all”; second, that they contain recordings of naturalistic behavior; and third, that they can be understood devoid of context.

Recently, there has been an upsurge of attention focused on bias and its impact on specialized artificial intelligence applications. Allegations of racism and sexism have permeated the conversation as stories surface about search engines delivering job postings for well-paying technical jobs to men and not women, or providing arrest mugshots when keywords such as “black teenagers” are entered. Learning algorithms are evolving; they are often created from parsing through large datasets of online information while having truth labels bestowed on them (...) by crowd-sourced masses. These specialized AI algorithms have been liberated from the minds of researchers and startups, and released onto the public. Yet intelligent though they may be, these algorithms maintain some of the same biases that permeate society. They find patterns within datasets that reflect implicit biases and, in so doing, emphasize and reinforce these biases as global truth. This paper describes specific examples of how bias has infused itself into current AI and robotic systems, and how it may affect the future design of such systems. More specifically, we draw attention to how bias may affect the functioning of a robot peacekeeper, a self-driving car, and a medical robot. We conclude with an overview of measures that could be taken to mitigate or halt bias from permeating robotic technology. (shrink)

Doxing is the intentional public release onto the Internet of personal information about an individual by a third party, often with the intent to humiliate, threaten, intimidate, or punish the identified individual. In this paper I present a conceptual analysis of the practice of doxing and how it differs from other forms of privacy violation. I distinguish between three types of doxing: deanonymizing doxing, where personal information establishing the identity of a formerly anonymous individual is released; targeting doxing, that discloses (...) personal information that reveals specific details of an individual’s circumstances that are usually private, obscure, or obfuscated; and delegitimizing doxing, which reveals intimate personal information that damages the credibility of that individual. I also describe how doxing differs from blackmail and defamation. I argue that doxing may be justified in cases where it reveals wrongdoing, but only if the information released is necessary to reveal that such wrongdoing has occurred and if it is in the public interest to reveal such wrongdoing. Revealing additional information, such as that which allows an individual to be targeted for harassment and intimidation, is unjustified. I illustrate my discussion with the examples of the alleged identification of the creator of Bitcoin, Satoshi Nakamoto, by Newsweek magazine, the identification of the notorious Reddit user Violentacrez by the blog Gawker, and the harassment of game developer Zoe Quinn in the ‘GamerGate’ Internet campaign. (shrink)

This article is based on a roundtable held at the Association of Internet Researchers annual conference in 2017, in Tartu, Estonia. The roundtable was organized by the Oxford Internet Institute’s Digital Ethics Lab. It was entitled “Artificial Intelligence and the Good Society”. It brought together four scholars—Michael Zimmer, Stine Lomborg, Ben Zevenbergen, and Corinne Cath—to discuss the promises and perils of artificial intelligence, in particular what ethical frameworks are needed to guide AI’s rapid development and increased use in societies. The (...) paper covers three case studies. They give a distinct overview of the ethical issues raised by the use of AI at different levels of analysis: top-down application of AI, bottom-up use of AI, and how academics and governments have reacted to these new challenges. From the case studies, four areas emerged. They represent some of the most topical ethical questions related to AI: its uses, its users, its designers, and the data that fuel it. Each of them provided a specific subset of ethical concerns that need further investigation. In conclusion, three recommendations are formulated for researchers and regulators to ensure the AI has a net-positive impact on society. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)