Vibes are often seen as feelings that can’t be explained or narrativized; they instead emerge from chance concatenations of stimuli and cohere as a mood. Not coincidentally, this is basically how machine-learning models work, surfacing statistical correlations that can’t be explained in terms of causality but are nonetheless usable in algorithmic recommendations or predictions. Vibes, that is, are feelings divorced from causes or consequences— emotions rendered useless for action.

The U.S. Department of Commerce is developing an approach to defining people’s trust in AI. But rather than define trustworthy systems with respect to their consequences or differential impacts, it focuses on how they are perceived by “users” (not whom they are used on) and does not bother to ask if the systems themselves are worth trusting.

Liberalism’s presumption of an underlying universality (binary biological sex, for example, or “post-racial color-blindness”) makes the unruly, messy data of human life appear tractable and computable for algorithmic procedures. And increasingly comprehensive algorithmic systems in turn render life in the flattening image of proceduralist liberalism. In a sense, liberalism and artificial intelligence are converging.

Since the advent of AI, there have been coherent critiques of its fundamentally conservative nature and implementation, notably from apostate computer scientist Joseph Weizenbaum, the inventor of the first chat bot. But the counterprogramming from the tech industry and its affiliated researchers has been equally insistent, with apologists continuing to insist that only more tech solutions can fix the problems tech has already exacerbated.

AI researcher Eliezer Yudkowsky described the feeling of intuition as the way our “cognitive algorithms happen to look from the inside.” An intangibly human gut response is just as socialized (programmed) as anything an algorithm might “feel” on the inside, clinging to its intuitions as well. It should be enough to take the algorithms’ output at face value, the preferences they ascribe to me, or to trust that it is the best entity to relay its own experience. But I’m greedy; I want to know more. What does it see when it looks at me?

As Africa grapples with catching up with the latest technological developments, it must also protect the continent’s most vulnerable people from the harm that technology can cause. Part of that means not importing machine learning systems or any other AI tools without questioning what the underlying purpose is, who benefits, and who might be disadvantaged

A new, “genderless” digital voice aims to disrupt the association between female voices and servitude. In effect, it occupies the same gendered position often associated with robots: servile, responsive, not quite female — but certainly not male.

If “deepfakes” make us nostalgic for the supposedly automatic authenticity of documents, AI artworks posit a corollary nostalgia for the authenticity of artists. AI creativity appears as creativity with no human strategy behind it—art without ego. A clumsy show of computer creativity makes it seem as though humans once really were freely creative and might be again

AI has become a label startups use to make their service seem innovative and disruptive, whether it uses machine learning or not. The inflated claims of what AI can achieve has fed an investment bubble and helped normalize the ways “smart” systems use intrusive surveillance.

There’s a furtive quality to the utterances of bots. They are, after all, planted. The robot Sophia engages in the kind of making nice that indicates, in humans, some fearsome repression. To Jimmy Kimmel she remarked, “I’m on my favorite show, the Today show.”

We cannot know what it’s like to be a bot, for the same reason that we can’t know what it’s like to be a bat or what it’s like to be one another. But engaging these questions about nonhuman consciousness, knowledge, and what it means to be and to know helps us confront the often unconscious human tendency to believe that personhood is modeled after some perfect exemplar.