Recent experimental work enables neuroscientists to decode mental content from brain data. The work is impressive, but, according to some, it is also dangerous, because it threatens our right to mental privacy. I’ll argue that current experiments do not threaten mental privacy, but that some commercial applications of decoding techniques might, and explain where the ethical difference lies.

Psychological descriptions of LLMs can be literally true, yet are often misleading—because ordinary mental terms carry human assumptions current systems do not support. We need new construals.

Responsible AI use in science is not primarily about transparency, but about the demonstrable reliability of AI-assisted research workflows.

I examine whether alignment faking in Claude 3 Opus constitutes genuine deception, arguing it represents shallow deception - genuine intentional behavior that differs systematically from human deception.

Scientific workflows can discipline generative AI to support reliable inference despite hallucination risks.

I examine when attributing beliefs to language models helps explain and predict their behavior, and where assumptions drawn from human psychology mislead us.

Generative AI is increasingly used in science, but is unavoidably prone to hallucination. I develop a reliabilist account of how generative AI nevertheless gives rise to new scientific knowledge. I analyze hallucinations as non-strategic misrepresentations of the target phenomenon, introduced by a model’s generative activity, rather than inherited from training data. Through case studies of AlphaFold and SEEDS, I show how scientific workflows draw on pre-existing knowledge of target phenomena to filter or qualify hallucinatory outputs, thereby preventing their erroneous content from propagating into downstream inference. Finally, I show that workflows are units of epistemic evaluation in their own right.

This paper identifies two overlooked forms of bias in LLM evaluation: auxiliary oversight (failing to account for factors that impede performance despite competence) and mechanistic chauvinism (dismissing non-human problem-solving strategies as illegitimate). We propose addressing these through empirically-driven approaches combining behavioral experiments with mechanistic analysis.

We examine virtual reality’s potential as a tool for moral learning and empathy development. While we reject the ’empathy machine hypothesis’ that VR promotes empathy by simulating others’ perspectives, we propose that avatar use may support moral learning through a different mechanism: self-fragmentation.

A popular article arguing that, when evaluating LLMs, anthropocentrism is just as misleading as anthropomorphism.

When we use methods from experimental psychology to test the capacities of LLMs, we are prone to transfer assumptions about the human case to the LLM case, and to do so without justification. By drawing attention to these assumptions we can make more informed comparisons.

In order to tell whether someone is culpable for an action initiated by a brain-computer interface, it is not necessary to work out whether the brain-computer interface correctly decoded their intention.

Where ML models are used as the centerpiece of an epistemic classification procedure, reliability is not sufficient for ethical use. The nature of classification errors should be taken into account.

Argument maps represent some kinds of arguments better than others.

Reading minds is easier than you might think.

Network models support novel forms of discovery, prediction, and explanation. They also raise a philosophical puzzle about unification.

Neural reuse helped to liberate humans from evolutionary constraints faced by our ancestors.

In the brain, semantic information is intertwined with Shannon information.

The concept of neural coding makes sense, if the codes can be learned by neurons.

If meta-cognition evolved, there is probably something like semi-meta-cognition.

There can be an objective fact about the number of bits in a biological signal, despite the fact that the signal is receiver-relative.

Network representation compresses information about complex systems without abstracting away from the properties that make them complex.

If there are localized functions in the brain, they can only be articulated by abstracting away from functions associated with particular experimental tasks.