• 
• 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
• 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

• 
• 5 min
• GIS Lounge
• 2019

GIS, a relatively new form of computational analysis, can often contain algorithms with biases based on biases present in the training data from open data sources, with this case study focusing on the tendency of power-line identification data being centered around the Western world. This problem can be improved by approaching data collection with more intentionality, either broadening the pool of collected geographic data or inputting artificial images to help the tool recognize a greater number of circumstances and thus become more accurate.

• Global Impact of Technology
• Algorithmic Bias
• Machine Learning
• Object Recognition

• 
• 6 min
• n/a
• 2018

Through a series of interactions on a chat and a truth-or-dare type game, the user guesses if they are chatting with a bot or human.

• Deep Fake
• Limitations of Digital Technologies
• Machine Learning
• Robots