Suchergebnisse

Demonized by governments and the media as criminals, glorified within their own subculture as outlaws, hackers have played a major role in the short history of computers and digital culture-and have continually defied our assumptions about technology and secrecy through both legal and illicit means. In Hacker Culture, Douglas Thomas provides an in-depth history of this important and fascinating subculture, contrasting mainstream images of hackers with a detailed firsthand account of the computer underground. Addressing such issues as the commodification of the hacker ethos by Silicon Valley entrepreneurs, the high-profile arrests of prominent hackers, and conflicting self-images among hackers themselves, Thomas finds that popular hacker stereotypes reflect the public's anxieties about the information age far more than they do the reality of hacking.

It is sometimes claimed that those who succeed with the aid of enhancement technologies deserve the rewards associated with their success less, other things being equal, than those who succeed without the aid of such technologies. This claim captures some widely held intuitions, has been implicitly endorsed by participants in social–psychological research and helps to undergird some otherwise puzzling philosophical objections to the use of enhancement technologies. I consider whether it can be provided with a rational basis. I examine three arguments that might be offered in its favour and argue that each either shows only that enhancements undermine desert in special circumstances or succeeds only under assumptions that deprive the appeal to desert of much of its dialectic interest.

This article argues for a politics of code grounded in the performance of hacking as an activity that is able to deconstruct the longstanding binary of code and performance by centering on the relationship of each to the body. Through a reading of hacker responses to the state's restriction on the export of cryptography, the article argues that interventions by hackers reveal to us the point at which the body becomes, literally, the limit of code, marking it as irreducibly transgressive, while rendering code impotent. It is this performance, it contends, that holds the most powerful possibility for hacktivism and resistance.

It is often thought that traditional recidivism prediction tools used in criminal
sentencing, though biased in many ways, can straightforwardly avoid one particularly
pernicious type of bias: direct racial discrimination. They can avoid this by excluding race
from the list of variables employed to predict recidivism. A similar approach could be
taken to the design of newer, machine learning-based (ML) tools for predicting recidivism:
information about race could be withheld from the ML tool during its training phase,
ensuring that the resulting predictive model does not use race as an explicit predictor.
However, if race is correlated with measured recidivism in the training data, the ML tool
may 'learn' a perfect proxy for race. If such a proxy is found, the exclusion of race would
do nothing to weaken the correlation between risk (mis)classifications and race. Is this a
problem? We argue that, on some explanations of the wrongness of discrimination, it is.
On these explanations, the use of an ML tool that perfectly proxies race would (likely) be
more wrong than the use of a traditional tool that imperfectly proxies race. Indeed, on
some views, use of a perfect proxy for race is plausibly as wrong as explicit racial profiling.
We end by drawing out four implications of our arguments.

The US and other national governments invest in research and development to spur competitiveness in their domestic manufacturing industries. However, there are limited studies on identifying the research efforts that will have the largest possible return on investment, resulting in suboptimal returns. Manufacturers commonly measure production time in order to identify areas for efficiency improvement, but this is typically not applied at the national level where efficiency issues may cross between enterprises and industries. Such methods and results can be used to prioritize efficiency improvement efforts at an industry supply-chain level. This paper utilizes data on manufacturing inventory along with data on inter-industry interactions to develop a method for tracking industry-level flow time and identifying bottlenecks in US manufacturing. As a proof of concept, this method is applied to the production of three commodities: aircraft, automobiles/trucks, and computers. The robustness of bottleneck identification is tested utilizing Monte Carlo techniques.