• 
• 5 min
• The New York Times
• 2019

As the case study of Baltimore demonstrates, cyber crime against governments for ransom of digital cash is not an uncommon practice.

• Cyber Crime
• Governing Technologies
• Digital Cash
• Malware

• 
• 27 min
• Cornell Tech
• 2019

Solon Barocas discusses his relatively new course on ethics in data science, following a larger trend of developing ethical sensibility in this field. He shares ideas of spreading out lessons across courses, promoting dialogue, and making sure we are really analyzing problems while learning to stand up for the right thing. Offers a case study of technological ethical sensibilities through questions raised by predictive policing algorithms.

• Computing Ethics Issues
• Algorithmic Bias
• Machine Learning

• 
• 28 min
• Cornell Tech
• 2019

Pre-trial risk assessment is part of an attempted answer to mass incarceration. Data sometimes answers a different question than the ones we’re trying to answer (data based on riskiness before incarceration, not how dangerous they are later). Essentially, technologies and algorithms which end up in contexts of social power differentials can often be abused to further cause injustice against people accused of a crime, for example. Numbers are not neutral and can even be a “moral anesthetic,” especially if the sampled data has confounding variables that collectors ignore. Engineers designing technology do not always envisage ethical questions when making decisions that ought to be political.

• Civil Surveillance
• Algorithmic Bias
• Machine Learning

• 
• 10 min
• MEL Beta
• 2019

The continued existence of pirating websites such as The Pirate Bay demonstrates how digital technologies can be used against institutions such as copyright, and further designates the idea of a completely free and open internet.

• Cyber Crime
• Technology Based Entertainment and Leisure
• Data Streaming
• Networks

• 
• 10 min
• The New Yorker
• 2019

Great breakdown of the concerns that come with automating the world without understanding why it works. Provides the principal concerns with the “hidden layer” of artificial neural networks, and how the lack of human understanding of some AI decision making makes these machines susceptible to manipulation.

• Limitations of Digital Technologies
• Scientific Innovation
• Machine Learning
• Data Mining

• 
• 7 min
• Mad Scientist Laboratory
• 2018

The combination of the profit motive for tech companies and the vague language of non-binding ehtical agreements for coders means that there must be a higher regulation for ethical deployment and use of technology. Argues that there must be clear demarcations between what is considered real and human versus fake and virtual. Digital technologies should be regulated in a manner similar to other technologies, such as guns, cars, or nuclear weapons.

• Computing Ethics Issues
• Scientific Innovation
• Machine Learning
• Robots