This special issue collects six articles tackling artificial intelligence (AI) from a social science perspective.
Innovation can only occur in the right environment. While organizations can attempt to hire for innovation, there is little that can blossom in a restrictive and discouraging physical setting - even if the space holds the most creative and vibrant thinkers.
The gulf between the technical brilliance claimed for Google's deep learning model and its real-world application points to a common problem that has hindered the use of AI in medical settings.
The first book to call for the end of the data economy. Carissa Veliz exposes how our personal data is giving too much to big tech and governments, why that matters, and what we can do about it.
It wasn't just technical work but also significant social and emotional labor that turned Sepsis Watch, a Duke University deep-learning model, into a success story.
Fast Company spoke with tech pioneer Jaron Lanier, Microsoft CVP Emma Williams and Stanford professor Jeremy Bailenson.
The Gogle Wellbeing Lab joined up with the company's Pixel team to run an ethnographic study across four countries, examining the relationship people in the United States, Germany, India, and South Korea had with their selfies.
This essay by AI specialist Jessy Lin explores some of the possibilities to rethink how humans and "intelligent" machines interact today.
The main lesson is that even a nearly imperceptible deviation from the full inclusion of all relevant parties in every aspect of the project can result in large deviations from the expected outcomes
Drawing on the ideas of the "slow movement", Slow Computing sets out numerous practical and political means to take back control and counter the more pernicious effects of living digital lives.