Whether consciously or not, AI manufacturers have decided to prioritise plausibility over accuracy. It means AI systems are impressive, but in a world plagued by conspiracy and disinformation this decision only deepens the problem.
Programming lessons from Umberto Eco and Emily Wilson.
Converting the analog into the digital requires discretization, leaving things out. What we filter out—or what we focus on—depends on our biases. How do conventional translators handle issues of bias? What can programmers learn from them?
A terrific cautionary look at the history of machine learning and artificial intelligence from the new laugh-a-minute book by James.
An even-handed assessment of the benefits and dangers of machine learning.
Little interventions for designers in the form of questions designed to challenge assumptions. Kind of like Brian Eno’s oblique strategies.
Deb Chachra:
If research on biases has told us anything, it is that humans make better decisions when we learn to recognize and correct for bias.