The central thesis of this paper is that fake news and related phenomena serve as defeaters for knowledge transmission via journalistic channels. This explains how they pose a threat to democracy; and it points the way to determining how to address this threat. Democracy is both intrinsically and instrumentally good provided the electorate has knowledge (however partial and distributed) of the common good and the means of achieving it. Since journalism provides such knowledge, those who value democracy have a reason (...) to protect it. Hostile agents, however, can undermine both the effectiveness of democratic decision-making and faith in democracy itself, by deliberately promulgating fake news and hyper-partisan views; moreover, these effects can come about unintentionally on social media. I conclude that we may need to change, not just the way we process information online, but also the informational environment in which we operate. (shrink)

The purpose of this survey was to gather individual’s attitudes and feelings towards privacy and the selling of data. A total (N) of 1,107 people responded to the survey. -/- Across continents, age, gender, and levels of education, people overwhelmingly think privacy is important. An impressive 82% of respondents deem privacy extremely or very important, and only 1% deem privacy unimportant. Similarly, 88% of participants either agree or strongly agree with the statement that ‘violations to the right to privacy are (...) one of the most important dangers that citizens face in the digital age.’ The great majority of respondents (92%) report having experienced at least one privacy breach. -/- People’s first concern when losing privacy is the possibility that their personal data might be used to steal money from them. Interestingly, in second place in the ranking of concerns, people report being concerned about privacy because ‘Privacy is a good in itself, above and beyond the consequences it may have.’ -/- People tend to feel that they cannot trust companies and institutions to protect their privacy and use their personal data in responsible ways. The majority of people believe that governments should not be allowed to collect everyone’s personal data. Privacy is thought to be a right that should not have to be paid for. (shrink)

The first few years of the 21st century were characterised by a progressive loss of privacy. Two phenomena converged to give rise to the data economy: the realisation that data trails from users interacting with technology could be used to develop personalised advertising, and a concern for security that led authorities to use such personal data for the purposes of intelligence and policing. In contrast to the early days of the data economy and internet surveillance, the last few years have (...) witnessed a rising concern for privacy. As bad data practices have come to light, citizens are starting to understand the real cost of using online digital technologies. Two events stamped 2018 as a landmark year for privacy: the Cambridge Analytica scandal, and the implementation of the European Union’s General Data Protection Regulation (GDPR). The former showed the extent to which personal data has been shared without data subjects’ knowledge and consent and many times for unacceptable purposes, such as swaying elections. The latter inaugurated the beginning of robust data protection regulation in the digital age. Getting privacy right is one of the biggest challenges of this new decade of the 21st century. The past year has shown that there is still much work to be done on privacy to tame the darkest aspects of the data economy. As data scandals continue to emerge, questions abound as to how to interpret and enforce regulation, how to design new and better laws, how to complement regulation with better ethics, and how to find technical solutions to data problems. The aim of the research project Data, Privacy, and the Individual is to contribute to a better understanding of the ethics of privacy and of differential privacy. The outcomes of the project are seven research papers on privacy, a survey, and this final report, which summarises each research paper, and goes on to offer a set of reflections and recommendations to implement best practices regarding privacy. (shrink)

Gödei's Theorem seems to me to prove that Mechanism is false, that is, that minds cannot be explained as machines. So also has it seemed to many other people: almost every mathematical logician I have put the matter to has confessed to similar thoughts, but has felt reluctant to commit himself definitely until he could see the whole argument set out, with all objections fully stated and properly met. This I attempt to do.

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)

In this article, Lucas maintains the falseness of Mechanism - the attempt to explain minds as machines - by means of Incompleteness Theorem of Gödel. Gödel’s theorem shows that in any system consistent and adequate for simple arithmetic there are formulae which cannot be proved in the system but that human minds can recognize as true; Lucas points out in his turn that Gödel’s theorem applies to machines because a machine is the concrete instantiation of a formal system: therefore, for (...) every machine consistent and able of doing simple arithmetic, there is a formula that it can’t produce as true but that we can see to be true, and so human minds and machines have to be different. Lucas considers as well in this article some possible objections to his argument: for any Gödelian formula we could, for instance, construct a machine able to produce it or we could put the Gödelian formulae that we had proved as axioms of a further machine. However - as Lucas underlines - for every of such machines we could again formulate another Gödelian formula, the Gödelian formula of these machines, that they are not able to proof but that we can recognize as true. More general arguments, such as the possibility to escape Gödelian argument by suggesting that Gödel’s theorem applies to consistent systems while we could be inconsistent ones, are moreover refuted by Lucas by maintaining that our inconsistency corresponds to occasional malfunctioning of a machine and not to his normal inconsistency; indeed, a inconsistent machine is characterized by producing any statement, on the contrary human being are selective and not disposed to assert anything. (shrink)

A Critique of Artificial Reason Hubert L. Dreyfus . HUBERT L. DREYFUS What Computers Still Can't Do Thi s One XZKQ-GSY-8KDG What. WHAT COMPUTERS STILL CAN'T DO Front Cover.

As societies become increasingly technologised, the need for careful and critical assessment rises. However, attempts to assess or normatively evaluate technological development invariably meet with an antinomy: both structurally and historically, technologies display multistable possibilities regarding uses, effects, side effects and other outcomes. Philosophers, usually expected to play applied ethics roles, often come to the scene after these effects are known. But others who participate at the research and development stages find even more difficulties with prognosis. Recent work on ‘revenge’ (...) effects (Tenner) and negative side effects (Kevles) are examined, as well as several cases of philosophers in ‘R&D’ roles. After sketching the antinomy,I outline a heuristic pragmatics of prognosis that addresses this quandary. (shrink)

MICHAEL WHEELER Cambridge, MA: MIT Press, 2005432 pages, ISBN: 0262232405 (hbk); $35.001.When I was teaching at MIT in the 1960s, students from the Artificial Intelligence Laboratory would come to...

What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI. According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. (...) But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to. (shrink)

Presenting a look at the human mind's capacity while criticizing artificial intelligence, the author makes suggestions about classical and quantum physics and ..

Winner of the Wolf Prize for his contribution to our understanding of the universe, Penrose takes on the question of whether artificial intelligence will ever ...

Through the use of such "folk" concepts as belief, desire, intention, and expectation, Daniel Dennett asserts in this first full scale presentation of...

The operations of deep networks are widely acknowledged to be inscrutable. The growing field of Explainable AI has emerged in direct response to this problem. However, owing to the nature of the opacity in question, XAI has been forced to prioritise interpretability at the expense of completeness, and even realism, so that its explanations are frequently interpretable without being underpinned by more comprehensive explanations faithful to the way a network computes its predictions. While this has been taken to be a (...) shortcoming of the field of XAI, I argue that it is broadly the right approach to the problem. (shrink)

There was a long tradition in philosophy according to which good reasoning had to be deductively valid. However, that tradition began to be questioned in the 1960’s, and is now thoroughly discredited. What caused its downfall was the recognition that many familiar kinds of reasoning are not deductively valid, but clearly confer justification on their conclusions. Here are some simple examples.