Writing on Medium, AI researcher Kate Crawford (previously) and Simply Secure (previously) co-founder Meredith Whittaker make the case for a new scholarly discipline that "measures and assesses the social and economic effects of current AI systems."
The core issue here isn’t that AI is worse than the existing human-led processes that serve to make predictions and assign rankings. Indeed, there’s much hope that AI can be used to provide more objective assessments than humans, reducing bias and leading to better outcomes. The key concern is that AI systems are being integrated into key social institutions, even though their accuracy, and their social and economic effects, have not been rigorously studied or validated.
There needs to be a strong research field that measures and assesses the social and economic effects of current AI systems, in order to strengthen AI’s positive impacts and mitigate its risks. By measuring the impacts of these technologies, we can strengthen the design and development of AI, assist public and private actors in ensuring their systems are reliable and accountable, and reduce the possibility of errors. By building an empirical understanding of how AI functions on the ground, we can establish evidence-led models for responsible and ethical deployment, and ensure the healthy growth of the AI field.
Artificial intelligence is hard to see
Ross Anderson (previously) is one of the world's top cryptographers; the British academic and practitioner was honored by having his classic, Security Engineering, inducted into The Cybersecurity Canon; however, he was not able to attend the awards gala himself because the US government sat on his visa application for months, and ultimately did not grant […]
In 2017, law student Lina Khan shifted the debate on Amazon and antitrust with a seminal paper called Amazon's Antitrust Paradox, which used Amazon's abusive market dominance to criticize the Reagan-era shift in antitrust enforcement, which rewrote the criteria for antitrust enforcement, so that antitrust no longer concerned itself with preventing monopoly, and only focused […]
An adversarial preturbation is a small, human-imperceptible change to a piece of data that flummoxes an otherwise well-behaved machine learning classifier: for example, there's a really accurate ML model that guesses which full-sized image corresponds to a small thumbnail, but if you change just one pixel in the thumbnail, the classifier stops working almost entirely.
Heads up: The clock is winding down on a free-entry contest to win not only one of the best smartphones on the market but a handy pair of earbuds. A simple sign-up is all you need to be eligible to win a 256 GB iPhone XS Max, along with AirPods. And while “free” is tough […]
Kudos to those of us who have chosen a less wasteful third option to “paper or plastic” at the supermarket or club stores. Tote bags are reusable, but they can be a pain to tote around. Here’s an upgrade to that planet-saving measure. The Club Cart Lotus Trolley Bag is that rare tote you’ll want […]
Looking for a career in IT, gaming or software development? In the ever-changing world of the internet, versatility is your biggest asset. In other words, mastering Java might not cut it in an interview if you don’t know C#. However, there’s a bundle that covers the essentials in most any language. The Legendary Learn to […]