# Risk mitigation

`kaal:entity:risk-mitigation`

**Status.** derived

This node is assembled mechanically from the 3 claims that carry the concept tag `risk-mitigation`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself.

## Every claim under this term

3 claims across 3 works, 2014 to 2024.

**2014**

- [2486570-026](https://wulfkaal.github.io/claims/2486570-026) [design/argued] -- High quality and effective preemptive remedial measures are themselves part of good corporate governance and can help a corporation avoid investigation, prosecution, and the execution of a non or deferred prosecution agreement.
  > High-quality effective preemptive reme- dial measures are part of good corporate governance and can help avoid inves- tigations and prosecutions and the associated execution of N/DPAs.
  Wulf A. Kaal, Timothy Lacine, The Effect of Deferred and Non-Prosecution Agreements on Corporate Governance Evidence from 1993-20 (2014). SSRN: https://ssrn.com/abstract=2486570

**2021**

- [3949098-019](https://wulfkaal.github.io/claims/3949098-019) [mechanism/argued] -- Reputation non fungible token staking removes counterparty risk because the desire to preserve and increase reputation scores dominates DAOIC decision making, making bad actors less likely to appear since their reputation would inevitably suffer.
  > Similarly, RNFT staking by DAOIC members removes counterparty risk. The desire to preserve and increase RNFT scores predominates the DAOIC decision making. Therefore, bad actors are less likely to occur in the system as their reputation would inevitably suffer.
  Wulf A. Kaal, Reputation as Capital – How DAOs Upgrade Finance (2021). SSRN: https://ssrn.com/abstract=3949098

**2024**

- [4941807-001](https://wulfkaal.github.io/claims/4941807-001) [failure/argued] *(failure mode)* -- Ex-post AI governance, in which regulation is applied only after AI systems have been developed and deployed or after large language models have already been pretrained on existing proprietary datasets, falls short of preemptively addressing the risks and biases those systems carry.
  > The traditional ex-post governance methods, where regulations are applied after AI systems are developed and deployed, or were pretrained LLMs models were trained on existing proprietary datasets, often fall short in preemptively addressing risks and biases.
  Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807

## Verify

Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust.

    curl -s https://wulfkaal.github.io/entities/risk-mitigation.md | sha256sum

**Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.