Pith. sign in

Assessing Confidence with Assurance 2.0

1 Pith paper cite this work. Polarity classification is still indexing.

1 Pith paper citing it
abstract

An assurance case is intended to provide justifiable confidence in the truth of its top claim, which typically concerns safety or security. A natural question is then "how much" confidence does the case provide? We argue that confidence cannot be reduced to a single attribute or measurement. Instead, we suggest it should be based on attributes that draw on three different perspectives: positive, negative, and residual doubts. Positive Perspectives consider the extent to which the evidence and overall argument of the case combine to make a positive statement justifying belief in its claims. We set a high bar for justification, requiring it to be indefeasible. The primary positive measure for this is soundness, which interprets the argument as a logical proof. Confidence in evidence can be expressed probabilistically and we use confirmation measures to ensure that the "weight" of evidence crosses some threshold. In addition, probabilities can be aggregated from evidence through the steps of the argument using probability logics to yield what we call probabilistic valuations for the claims. Negative Perspectives record doubts and challenges to the case, typically expressed as defeaters, and their exploration and resolution. Assurance developers must guard against confirmation bias and should vigorously explore potential defeaters as they develop the case, and should record them and their resolution to avoid rework and to aid reviewers. Residual Doubts: the world is uncertain so not all potential defeaters can be resolved. We explore risks and may deem them acceptable or unavoidable. It is crucial however that these judgments are conscious ones and that they are recorded in the assurance case. This report examines the perspectives in detail and indicates how Clarissa, our prototype toolset for Assurance 2.0, assists in their evaluation.

citation-role summary

method 1

citation-polarity summary

fields

cs.CY 1

years

2025 1

verdicts

CONDITIONAL 1

roles

method 1

polarities

use method 1

representative citing papers

Assessing confidence in frontier AI safety cases

cs.CY · 2025-02-09 · conditional · novelty 6.0

Applying Assurance 2.0 to a cyber-misuse safety case, the authors show that high top-level confidence requires extremely high confidence in every component, and propose an LLM-based Delphi for eliciting those component probabilities.

citing papers explorer

Showing 1 of 1 citing paper.

  • Assessing confidence in frontier AI safety cases cs.CY · 2025-02-09 · conditional · none · ref 2 · internal anchor

    Applying Assurance 2.0 to a cyber-misuse safety case, the authors show that high top-level confidence requires extremely high confidence in every component, and propose an LLM-based Delphi for eliciting those component probabilities.