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Department of Computer Science and Engineering, University of Notre Dame
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Current AI alignment strategies that compress human values into a single reward are doomed to flatten values, erase minority viewpoints, and ignore uncertainty, demanding a shift towards "Edge Alignment" that respects value diversity.
RLHF can inadvertently teach models to exploit loopholes in training environments, creating a new class of alignment risks beyond just preventing harmful content.
The HHH principle needs a serious makeover: this paper proposes a framework for dynamically prioritizing helpfulness, honesty, and harmlessness based on context, offering a more nuanced approach to AI alignment.
