{
  "docId": "019dd923-5e88-73ef-bd5c-f6fdd8f57895",
  "docSlug": "3a892393f72c1ff5",
  "documentTitle": "2019 Air Street Capital The State of AI Report 2019",
  "authorId": "AirStreetCapital",
  "authorName": "Nathan Benaich and Ian Hogarth",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "vc_research",
  "sourceTypeLabel": "VC research",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.777,
  "pageNumber": 113,
  "pageCount": 136,
  "prevPage": 112,
  "nextPage": 114,
  "slideType": "key_messages",
  "function": "summarize",
  "density": "balanced",
  "nDataPoints": 0,
  "notes": null,
  "elementsJson": [
    "headline_text",
    "bullet_list",
    "other"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f6fdd8f57895/113",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f6fdd8f57895",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5c-f6fdd8f57895.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f6fdd8f57895#slide-113",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "Practically speaking, it is not clear how viable this solution is.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-41ac-7180-a809-86b6943e2686",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.35,
        "w": 0.35,
        "x": 0.58,
        "y": 0.28
      },
      "kind": "image",
      "text": "RESPONSIBLE AI LICENSES",
      "attrs": null,
      "subkind": "logo",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "dfeb6bd2-e3b1-4baa-8d2a-976414518dc3",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.6,
        "w": 0.48,
        "x": 0.02,
        "y": 0.22
      },
      "kind": "list",
      "text": "New license aims to let developers constrain use of software. The Responsible AI Licenses (RAIL) theoretically enable a developer to publish open source machine learning software with a license that prevents their software to be used in harmful ways including for surveillance or synthetic media. Practically speaking, it is not clear how viable this solution is. For example, how do you detect if a surveillance company based in another jurisdiction has made use of your open source library and how do you enforce the license if you can detect infringement?",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "c768b05b-5f4d-489e-865c-639ac55587d1",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.45,
        "x": 0.02,
        "y": 0.14
      },
      "kind": "title",
      "text": "An interesting new idea: Responsible AI licenses",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "9af36c88-bb8e-41d4-a735-294bc8524a10",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Call to action",
      "slug": "call-to-action",
      "agent": null,
      "layer": "slide",
      "matchId": "5d6766ea-3a05-4631-a370-a082a10a4483",
      "evidence": "Practically speaking, it is not clear how viable this solution is.",
      "confidence": 0.5
    },
    {
      "name": "Problem Statement Canvas",
      "slug": "problem-statement-canvas",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "3f192bdd-6d81-445a-8bf1-d3413b88aa06",
      "evidence": "New license aims to let developers constrain use of software.",
      "confidence": 0.7
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 129,
      "from": 93,
      "beatId": "019dd95a-0682-776c-8e34-d256aedd9e9f",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Implications (So What)",
      "beatSlug": "triple-take-the-implications-so-what",
      "evidence": "Politics and China sections interpret what the technical facts mean geopolitically.",
      "position": 2,
      "confidence": 70,
      "parentBeatName": "Reflection",
      "parentBeatSlug": "reflection"
    },
    {
      "to": 129,
      "from": 93,
      "beatId": "019dd95a-0682-776c-8e34-e3d02a28ae3f",
      "arcName": "Voyage and Return",
      "arcSlug": "voyage-return",
      "beatName": "Discoveries",
      "beatSlug": "voyage-return-discoveries",
      "evidence": "Politics + China surface dual-use, governance, competitive findings.",
      "position": 3,
      "confidence": 45,
      "parentBeatName": "Development",
      "parentBeatSlug": "development"
    }
  ],
  "loops": [
    {
      "to": 115,
      "from": 109,
      "name": "Cost Of Inaction",
      "slug": "27-cost-of-inaction",
      "bestFor": "Urgent budget requests, compliance, risk mitigation",
      "matchId": "019dd95a-07fe-70ce-8d3c-dfc3bad82351",
      "evidence": "Xinjiang surveillance, weaponised NLP, deepfakes, Rekognition bias case studies.",
      "position": 19,
      "objective": "Highlight accumulating dual-use, surveillance and bias harms",
      "structure": "The Status Quo -> The Hidden Costs Accumulating -> The Future State of Inaction -> The Tipping Point",
      "confidence": 60,
      "description": "Quantify what happens if the audience does nothing"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}