# Discrimination

`kaal:entity:discrimination`

**Status.** derived

This node is assembled mechanically from the 7 claims that carry the concept tag `discrimination`. 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

7 claims across 6 works, 2017 to 2024.

**2017**

- [3071378-013](https://wulfkaal.github.io/claims/3071378-013) [mechanism/argued] -- Because of societal perceptions of minorities, minority groups face an uphill battle navigating the political environment of corporate America, which in turn significantly affects their positioning and success there.
  > societal perceptions of minorities,59 minority groups face an uphill battle in navigating the political environment in corporate America, which in turn can significantly affect their positioning and success in corporate America.
  Wulf A. Kaal, Blockchain Technology and Race in Corporate America (2017). SSRN: https://ssrn.com/abstract=3071378
- [3071378-033](https://wulfkaal.github.io/claims/3071378-033) [mechanism/argued] -- People who work for a DAO are free from existing corporate hierarchies and their possible discriminatory effects because they are not subject to a supervisor, boss, or CEO.
  > People who work for a DAO are free from existing corporate hierarchies and their possible discriminatory effects. People who work for a DAO would not be subject to a supervisor, boss, or CEO.
  Wulf A. Kaal, Blockchain Technology and Race in Corporate America (2017). SSRN: https://ssrn.com/abstract=3071378

**2020**

- [3709041-021](https://wulfkaal.github.io/claims/3709041-021) [failure/argued] *(failure mode)* -- Majority rule in a microdemocracy can produce discrimination, because a majority that is itself unaffected by a rule it enacts can impose a disproportionate burden on the minority the rule does affect.
  > Similarly, majority rule may mean discrimination of the minority. A majority that is unaffected by a rule they instantiate may have a discriminatory impact on a minority that is disproportionately affected by the change in the rules.
  Kaal, Blockchain Technology for Good (2020). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3709041

**2021**

- [3782198-035](https://wulfkaal.github.io/claims/3782198-035) [failure/argued] *(failure mode)* -- Anonymity can exacerbate second and third order discrimination because it makes such discrimination more difficult to detect, so other governance mechanisms in a decentralized organization must be used to combat it.
  > In fact, anonymity can exacerbate )nd and Grd-order effects, as it makes it more difficult to detect. To combat such effects, different mechanisms in the governance of a decentralized organization must be employed.
  Craig Calcaterra, Wulf A. Kaal, Contemporary Decentralization (2021). SSRN: https://ssrn.com/abstract=3782198

**2024**

- [4796714-011](https://wulfkaal.github.io/claims/4796714-011) [mechanism/evidenced] *(failure mode)* -- Bias in AI systems arises when algorithms incorporate discriminatory practices carried in their training data, and the resulting outputs reveal a profound misalignment between AI operations and societal values, ethics, and norms.
  > AI governance does encounter a critical challenge in mitigating biases within AI systems, where biases can inadvertently arise through algorithms incorporating discriminatory practices due to data used in training.
  Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714
- [4855607-003](https://wulfkaal.github.io/claims/4855607-003) [failure/evidenced] *(failure mode)* -- Deep learning models inadvertently learn and amplify whatever biases exist in their training data, so the composition of the training corpus, not the architecture, is the source of unfair or discriminatory outcomes.
  > Depending on the data used for training, deep learning models can inadvertently learn and amplify biases present in the training data, potentially leading to unfair or discriminatory outcomes.
  Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607
- [4941807-015](https://wulfkaal.github.io/claims/4941807-015) [failure/evidenced] *(failure mode)* -- A critical unsolved challenge for AI governance is bias mitigation, because biases enter inadvertently when algorithms incorporate discriminatory practices carried in the data used for training.
  > AI governance does encounter a critical challenge in mitigating biases within AI systems, where biases can inadvertently arise through algorithms incorporating discriminatory practices due to data used in training.
  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/discrimination.md | sha256sum

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