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Artificial intelligence (AI) promises to automate and scale solutions to perennial accessibility challenges (e.g., generating image descriptions for blind users). However, research shows that AI-bias disproportionately impacts people already marginalized based on their race, gender, or disabilities, raising questions about potential impacts in addition to AI’s promise. In this talk, Cynthia Bennett will overview broad concerns at the intersection of AI, disability, and accessibility. She will then share details about one project in this research space that led to guidance on human and AI-generated image descriptions that account for subjective and potentially sensitive descriptors around race, gender, and disability of people in images.

When someone is in a conscious state, must they be aware of that state?  The Buddhist philosopher Dignāga offers a brilliant route to answering this question by leveraging the role awareness might play as a constraint on memory.  I begin by clarifying his strategy and what conclusions it might be used to establish.  Here I examine different candidate directions of explanation between consciousness and inner awareness.  I interpret the metaphor of consciousness as a lamp that lights itself, and use the metaphor to distinguish between his view and contemporary higher-order theories of consciousness.  I then turn to explain why the memory argument fails.  The first main problem is that, contrary to Dignāga’s contemporary defenders, there is no good way to use the argument to reach a conclusion about all conscious states.  The second main problem is that the proposed awareness constraint on memory is highly problematic, in tension both with ancient objections as well as current psychology.

With responses from Lu Teng (NYU Shanghai)