AI Epistemic Risks: Emerging Mechanisms & Evidence

<p>Advances in artificial intelligence pose risks to humanity's collective capacity to form accurate beliefs, reason well, and maintain a healthy information en

Epistemic Alienation and the Division of Labor

The division of cognitive labor leads to what Barry has recently called epistemic alienation: a problematic separation of individuals from epistemic goods. According to Barry, individuals are alienated from epistemic virtues because an efficient division of cognitive labor requires them to manifest a lack of virtues. I argue that this is a mistaken diagnosis of the source of epistemic alienation. Participating in high-functioning collectives does not prevent individuals from being robustly virtuous; rather, our cognitive limitations make it impossible for us to live up to the highest standards of epistemic conduct. Collectives transcend these cognitive limitations to achieve what individuals cannot by harnessing those same limitations in their members. Furthermore, I argue that we should distinguish the epistemic weaknesses that facilitate the division of cognitive labor from those that result from the division of labor. Only the latter are aspects of epistemic alienation. By dividing our cognitive labor over larger populations of agents and artifacts, we have massively accelerated our rate of epistemic productivity but have thereby created conditions that are variously inhospitable for the thinking of individuals by alienating them from the products, processes, and environments of inquiry. These forms of separation are problematic insofar as they amplify intellectual vices, incapacitate reason, and induce cognitive biases.

Why Crisis Resilience Depends on Epistemic Security

If we want to prevent tomorrow’s crises from spiraling out of control, we must recognize epistemic systems as critical infrastructure, writes Elizabeth Seger.

Amplifiers of Epistemic Posture

Essays and writing on AI

Epistemic Courage and Open-Mindedness

Epistemic courage requires me to sometimes adopt a belief even in the face of doubts. Open-mindedness requires me to be able to reconsider my beliefs. It is generally assumed that we should have bo...

Human Adults and LLMs as Scientists: Who Benefits from Active Exploration?

A long-standing finding in the causal learning literature is that adults struggle to identify conjunctive causal rules, where an effect requires the simultaneous presence of multiple causes, while...

Cognitive exponents and LLM leverage

I know a few people for whom LLMs have been a near-immediate multiplier of attention and effort. I know a lot for whom LLMs clearly make them worse at thinking and doing things. So: why?

EPISTEMIC STIGMERGY: NATURAL VS. ARTIFICIAL INTELLIGENCE

The article\(^{1}\) defends the thesis that intelligent behavior might require not internal complexity but complex interaction. This is demonstrated by the various forms of stigmergy that can be observed both in social insects and in humans. The exposition is structured as follows: (§0) explains how the term “intelligence” is interpreted in the following text; (§1) clarifies the relation between intelligence and complexity; (§2) shows that intelligent behavior does not require internal complexity; (§3) introduces the concept of stigmergy; (§4) presents the mechanisms that give rise to this phenomenon; (§5) distinguishes several types of stigmergic interaction; (§6) briefly discusses the evolutionary mechanisms that could have produced them; (§7) sketches the possible ways in which the concept of stigmergy is used outside biology; (§8) examines collaborative stigmergy in humans; (§9) points to its epistemic projections; (§10) outlines some conclusions concerning the role of artificial intelligence systems and their place in human society.

Epistemic courage

"'Epistemic Courage' is a timely and thought-provoking exploration of the ethics of belief. Drawing on a wide range of examples, from conspiracy theories to medical misinformation, Ichikawa shows why epistemology is no mere academic abstraction - the question of what to believe couldn't be more urgent. And, he argues, many mainstream ideas about what to believe - those emphasizing the importance of ensuring that one doesn't believe with insufficient evidence - are incomplete and distorting in important and harmful ways. A skeptical, negative bias about belief is connected to a conservative bias that reinforces the status quo. Throughout the book, Ichikawa argues that we need to shift our focus from avoiding false beliefs to actively seeking out true ones. Throughout the book, Ichikawa uses engaging and timely examples to illustrate his points. He tackles important questions, such as how moral considerations interact with evidential ones in deciding what to believe, and how to navigate the complex ethical issues around testimony, rape culture, and epistemic injustice. Accessible and rigorous, 'Epistemic Courage' invites readers to consider the importance of belief, and how it shapes our lives and the world around us. With its insightful analysis and compelling case studies, this book is an essential read for philosophers and anyone else interested in belief, social justice, and the pursuit of truth."--

Relying on Others: An Essay in Epistemology

Abstract. This book concerns the role others play in our attempts to acquire knowledge of the world. Two main forms of this reliance are examined: testimon

Harnessing Frustration: Using LLMs to Overcome Activation Energy

One of my biggest weaknesses as a software engineer is procrastination when facing a new project. When the scope is unclear, I have a tendency to wait until I feel I’ve “felt out” the problem to start doing anything. I know I’ll feel better and work much faster when I get “stuck in” but I still struggle with that first step, overcoming the “activation energy” required to engage with the details. LLMs have been a game-changer for me in this respect: I can just throw a couple of sentences at them with the shape of the problem. This leads to one of two outcomes: The LLM comes up with a good solution, usually in a slightly different way than what I was thinking. I realize “oh wow the solution is much simpler than I thought”. Straight away I start thinking about the consequences of implementing and improving what the LLM suggested. The LLM comes up with a solution that I intuitively recognize as “wrong”. My immediate reaction is frustration (“How could it get it so wrong”) which leads me to go back and forth with the model, explaining to it why its solution could not possibly work. But in the process of arguing with the model, my brain is churning away and generating variations or different approaches that could work. After a while, even if the AI is still on the wrong track, the debate will trigger a moment of inspiration where suddenly the solution will come to me. I’ll excitedly start up a new conversation and start working through it with the model. The key is the emotional reaction I have immediately to the LLM’s response, either excitement or frustration. By harnessing this immediate feedback loop, I get my brain out of its passive, procrastination mode. It’s almost like a jolt: either I’m thrilled because it’s simpler than I thought, or I’m spurred to action by the urge to correct a perceived ‘wrong’ answer. This forces me to engage with the problem in a meaningful way.

AI and the Wisdom of Uncertainty

AI chatbots rarely say "I don't know," and neither, increasingly, do we. But we can cultivate our epistemic resilience.

Towards Post-Interaction Computing: Addressing Immediacy, (un)Intentionality, Instability and Interaction Effects

We situate the debate on intentionality within the rise of cognitive neuroscience and argue that cognitive neuroscience can explain intentionality. We discuss the explanatory significance of ascribing intentionality to representations. At first, we ...