{
  "docId": "019dd923-5e88-73ef-bd5c-f812573a947a",
  "docSlug": "eea7524c557036f4",
  "documentTitle": "2020 Air Street Capital The State of AI Report 2020",
  "authorId": "AirStreetCapital",
  "authorName": "Air Street Capital",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "vc_research",
  "sourceTypeLabel": "VC research",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.777,
  "pageNumber": 131,
  "pageCount": 177,
  "prevPage": 130,
  "nextPage": 132,
  "slideType": "industry_trends",
  "function": "establish_context",
  "density": "balanced",
  "nDataPoints": 0,
  "notes": "The slide highlights academic and investigative work that brought AI ethics into the mainstream.",
  "elementsJson": [
    "headline_text",
    "bullet_list",
    "paragraph",
    "screenshot"
  ],
  "metadataConfidence": 0.9,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f812573a947a/131",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f812573a947a",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5c-f812573a947a.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f812573a947a#slide-131",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "Examples include policing, the judiciary and the military.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-47c5-73ac-aa6c-d85fb27b05ae",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.35,
        "w": 0.43,
        "x": 0.52,
        "y": 0.345
      },
      "kind": "image",
      "text": "The Perpetual Line-Up: Unregulated Police Face Recognition in America",
      "attrs": null,
      "subkind": "screenshot",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "2c7f4c66-3bae-4a5c-a6b0-1a2e400714da",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.6,
        "w": 0.45,
        "x": 0.05,
        "y": 0.28
      },
      "kind": "list",
      "text": "Examples include policing, the judiciary and the military. A few trailblazing researchers include: Joy Buolamwini, Timnit Gebru, Gender Shades; Clare Garvie, Alvaro Bedoya, and Jonathan Frankle, The Perpetual Line-Up; Adam Harvey, Megapixels; P Allo, M Taddeo, S Wachter, L Floridi, The ethics of algorithms; Margaret Boden, Joanna Bryson, Alan Winfield et al., Principles of robotics.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "542f043d-c2c3-4316-995a-642bf70620ef",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.12,
        "w": 0.9,
        "x": 0.05,
        "y": 0.13
      },
      "kind": "title",
      "text": "Ethical risks: A group of researchers have spent years helping to frame the ethical risks of deploying ML in certain sensitive contexts. This year those issues went mainstream.",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "2f6f3005-ea01-44a6-b0b6-18b76d2ae5d9",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Emotional Appeal",
      "slug": "emotional-appeal",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "5034c473-5339-445c-9f90-7d1f1cc157f3",
      "evidence": "The slide mentions specific researchers and their work.",
      "confidence": 0.5
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 170,
      "from": 130,
      "beatId": "019dd95a-0682-776c-8e34-f2fc34acb214",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Implications (So What)",
      "beatSlug": "triple-take-the-implications-so-what",
      "evidence": "Politics section unpacks ethical, geopolitical and labor consequences.",
      "position": 2,
      "confidence": 60,
      "parentBeatName": "Reflection",
      "parentBeatSlug": "reflection"
    },
    {
      "to": 170,
      "from": 130,
      "beatId": "019dd95a-0682-776c-8e35-01ef1d0aee03",
      "arcName": "The Mountain",
      "arcSlug": "mountain",
      "beatName": "Climax",
      "beatSlug": "mountain-climax",
      "evidence": "Politics section dramatises facial recognition, military, US/China conflicts.",
      "position": 3,
      "confidence": 40,
      "parentBeatName": "Turn",
      "parentBeatSlug": "turn"
    }
  ],
  "loops": [
    {
      "to": 140,
      "from": 131,
      "name": "Ripple Effect",
      "slug": "26-ripple-effect",
      "bestFor": "Change management, scaling initiatives, demonstrating broad value",
      "matchId": "019dd95a-07fe-70ce-8d3d-270e46b4c916",
      "evidence": "Risks -> wrongful arrests -> Facebook $650M -> Clearview -> UK ruling -> WA law -> China lawsuit.",
      "position": 16,
      "objective": "Trace ripple of facial recognition harms from arrests to lawsuits to legislation",
      "structure": "Individual Impact -> Team Impact -> Organizational Impact -> Market/Industry Impact",
      "confidence": 70,
      "description": "Show how impact spreads from individual to team to organization to market"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}