2023-12-18 10:56:07 +01:00
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<!DOCTYPE html>
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<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
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<head>
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<meta charset="utf-8" />
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<meta name="generator" content="pandoc" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" />
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<meta name="author" content="Ruben van de Ven, Ildikó Zonga Plájás, Cyan Bae, Francesco Ragazzi" />
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<title>Algorithmic Security Vision: Diagrams of Computer Vision Politics</title>
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<style>
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/* div[data-custom-style='Body Text']{
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background: rgba(255,255,255,.5)
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} */
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code {
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white-space: pre-wrap;
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}
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span.smallcaps {
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font-variant: small-caps;
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}
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span.underline {
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text-decoration: underline;
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}
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div.column {
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display: inline-block;
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vertical-align: top;
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width: 50%;
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}
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div.hanging-indent {
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margin-left: 1.5em;
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text-indent: -1.5em;
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}
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ul.task-list {
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list-style: none;
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}
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.display.math {
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display: block;
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text-align: center;
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margin: 0.5rem auto;
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}
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/*Filenames with code blocks: https://stackoverflow.com/a/58199362*/
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div.sourceCode::before {
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content: attr(data-filename);
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display: block;
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background-color: #cfeadd;
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font-family: monospace;
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font-weight: bold;
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}
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#collage {
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position: fixed;
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z-index: -1;
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background-color: white;
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left: 0;
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top: 0;
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right: 0;
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bottom: 0;
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overflow: hidden;
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}
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#collage_window {
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position: absolute;
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top: -1000px;
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left: -1000px;
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}
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</style>
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<link rel="stylesheet" href="paper.css" />
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<script src="assets/wNumb-1.2.0.min.js"></script>
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<script src="assets/annotate.js"></script>
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<script>
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const centerPoints = [
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[2759, 6452],
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[14335, 5364],
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[5757, 10084],
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[7137, 3869], // left in practice is -5746px;, top: -2988px;:
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]
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const canvasCenter = [20077 / 2, 10331 / 2]
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let scale = .5;
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2023-12-18 11:50:20 +01:00
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const sheet = new CSSStyleSheet
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sheet.replaceSync(
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`
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:host(){
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--override-color: gray
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}
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:host(.active){
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--override-color: blue;
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}
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:host(.ending){
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--override-color: blue;
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--disactive-path: rgba(100,100,100,.7)
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}`);
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2023-12-18 10:56:07 +01:00
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function easeInOutQuart(x) {
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return x < 0.5 ? 8 * x * x * x * x : 1 - Math.pow(-2 * x + 2, 4) / 2;
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}
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let scroll_offsets = []
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function calculateScrollOffsets() {
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const anchorEls = document.getElementsByClassName('anchor');
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offsets = []
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for (let anchorEl of anchorEls) {
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const align_pos = centerPoints[anchorEl.dataset.i];
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const bbox = anchorEl.getBoundingClientRect()
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const scroll_y = bbox.top + (bbox.height / 2) + window.scrollY;
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offsets.push([scroll_y, anchorEl.dataset.i])
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}
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return offsets.sort((a, b) => a[0] - b[0]);
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}
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window.addEventListener('DOMContentLoaded', () => {
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scroll_offsets = calculateScrollOffsets()
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const windowEl = document.getElementById('collage_window')
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2023-12-18 11:50:20 +01:00
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const anchorEls = document.getElementsByClassName('anchor')
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const lastAnchorEl = anchorEls[anchorEls.length - 1];
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console.log(lastAnchorEl)
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for(const player of windowEl.children){
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player.shadowRoot.adoptedStyleSheets = [sheet];
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}
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2023-12-18 10:56:07 +01:00
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function updateScroll() {
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2023-12-18 11:50:20 +01:00
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// calculate the zooming & positioning of the plot
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2023-12-18 10:56:07 +01:00
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center_y = window.scrollY + window.innerHeight / 2
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prev = null;
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next = null;
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for (let offset of scroll_offsets) {
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if (offset[0] > center_y) {
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next = offset
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break;
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}
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prev = offset
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}
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let source_pos, target_pos, source_scale, target_scale;
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if (prev === null) {
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prev = [next[0] - window.innerHeight / 2, null]
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source_scale = .1
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target_scale = .45
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source_pos = canvasCenter
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target_pos = centerPoints[next[1]]
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} else if (next === null) {
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next = [prev[0] + window.innerHeight / 2, null]
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source_scale = .45
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target_scale = .1
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source_pos = centerPoints[prev[1]]
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target_pos = canvasCenter
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} else {
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source_pos = centerPoints[prev[1]]
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target_pos = centerPoints[next[1]]
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target_scale = .45
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source_scale = .45
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}
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const t = Math.min(1, Math.max(0, (center_y - prev[0]) / (next[0] - prev[0])))
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t_ease = easeInOutQuart(t)
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const dx = target_pos[0] - source_pos[0];
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const dy = target_pos[1] - source_pos[1];
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const ds = target_scale - source_scale
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// console.log('twean from', source_pos, 'to', target_pos, 't', t_ease)
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// console.log('twean scale', source_scale, 'to', target_scale, 't', t_ease)
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scale = source_scale + t_ease * ds;
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x = -1 * (source_pos[0] + dx * t_ease) * scale + window.innerWidth / 2;
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y = -1 * (source_pos[1] + dy * t_ease) * scale + window.innerHeight / 2;
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// console.log('x', x, 'y', y, 'scale', scale)
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// console.log(x, y);
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windowEl.style.transform = `scale(${scale})`
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windowEl.style.left = `${x}px`
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windowEl.style.top = `${y}px`
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2023-12-18 11:50:20 +01:00
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// calculate whether we're nearing the conlusion, and color accordingly
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const last = Math.max(...Array.from(anchorEls).map((e) => e.getBoundingClientRect().bottom))
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console.log(last)
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if (last < 0) {
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for (const playerEl of windowEl.children) {
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playerEl.classList.add('ending')
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}
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} else {
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for (const playerEl of windowEl.children) {
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playerEl.classList.remove('ending')
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}
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}
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2023-12-18 10:56:07 +01:00
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}
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windowEl.style.transform = `scale(${scale})`
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window.addEventListener('resize', (ev) => {
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scroll_offsets = calculateScrollOffsets()
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updateScroll()
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})
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window.addEventListener('scroll', updateScroll)
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updateScroll()
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let options = {
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// root: document.querySelector("#scrollArea"), // viewport by default
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2023-12-18 11:50:20 +01:00
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rootMargin: `${-Math.round(innerHeight / 3)}px`, //"0px",
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2023-12-18 10:56:07 +01:00
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threshold: 0,
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};
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let observer = new IntersectionObserver((entries, observer) => {
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entries.forEach((entry) => {
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index = entry.target.dataset.i;
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console.log(entry)
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if (index >= windowEl.children.length) {
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2023-12-18 10:56:07 +01:00
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return;
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}
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playerEl = windowEl.children[index];
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2023-12-18 11:50:20 +01:00
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if (entry.isIntersecting) {
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2023-12-18 10:56:07 +01:00
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playerEl.classList.add('active')
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} else {
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playerEl.classList.remove('active')
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}
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})
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}, options);
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2023-12-18 11:50:20 +01:00
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// const anchorEls = document.getElementsByClassName('anchor');
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2023-12-18 10:56:07 +01:00
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for (const anchorEl of anchorEls) {
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observer.observe(anchorEl)
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}
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console.log(anchorEls)
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// .forEach(el => observer.observe());
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// console.log(anchorEl.dataset.title);
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// const toSelect = typeof anchorEl.dataset.title == 'undefined' || anchorEl.dataset.title == 'none' ? null : frameEl.contentWindow.getIdForTitle(anchorEl.dataset.title);
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// // navItemEl.hash url-encodes
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// // let targetEl = document.getElementById(navItemEl.attributes.href.value.substr(1));
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// // let wrapperEl = targetEl.parentNode;
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// let intersectionObserver = new IntersectionObserver(function (entries) {
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// console.log(entries);
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// // If intersectionRatio is 0, the target is out of view
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// // and we do not need to do anything.
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// // if (entries[0].intersectionRatio <= 0) {
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// // // navItemEl.classList.remove('active');
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// // } else {
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// // if (toSelect === null) {
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// // frameEl.contentWindow.mapGraph.triggerReset();
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// // // frameEl.contentWindow.mapGraph.deselectNode();
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// // // frameEl.contentWindow.mapGraph.resetZoom();
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// // } else {
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// // frameEl.contentWindow.mapGraph.triggerSelect(toSelect);
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// // // frameEl.contentWindow.mapGraph.selectNode(node);
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// // }
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// // // navItemEl.classList.add('active');
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// // }
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// });
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// // start observing
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// intersectionObserver.observe(anchorEl);
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// }
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// const linkEls = document.getElementsByClassName('maplink');
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// for (let linkEl of linkEls) {
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// linkEl.addEventListener('click', (ev) => {
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// const toSelect = typeof linkEl.dataset.title == 'undefined' || linkEl.dataset.title == 'none' ? null : frameEl.contentWindow.getIdForTitle(linkEl.dataset.title);
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// if (toSelect === null) {
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// frameEl.contentWindow.mapGraph.deselectNode();
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});
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</script>
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</head>
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<body>
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<header id="title-block-header">
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<h1 class="title">Algorithmic Security Vision: Diagrams of Computer
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Vision Politics</h1>
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<p class="author"><em>Ruben van de Ven, Ildikó Zonga Plájás, Cyan Bae,
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Francesco Ragazzi</em></p>
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<p class="date">December 2023</p>
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</header>
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</div>
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<section id="part1">
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<section id="managing-error-from-the-sublime-to-the-risky-algorithm" class="level2">
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<h2>3. Managing error: from the sublime to the risky algorithm</h2>
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<div data-custom-style="Body Text">
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<p>Our third emerging figuration concerns the place of the error. A
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large body of literature examines actual and speculative cases of
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algorithmic prediction based on self-learning systems (Azar et al.,
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2021). Central to these analyses is the boundary-drawing performed by
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such algorithmic devices, enacting (in)security by rendering their
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subjects as more- or less-risky others (Amicelle et al., 2015: 300;
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Amoore and De Goede, 2005; Aradau et al., 2008; Aradau and Blanke, 2018)
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based on a spectrum of individual and environmental features (Calhoun,
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2023). In other words, these predictive devices conceptualize risk as
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something produced by, and thus external to, security technologies.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>In this critical literature on algorithmic practices, practitioners
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working with algorithmic technologies are often critiqued for
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understanding software as “sublime” (e.g. Wilcox, 2017: 3). However, in
|
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our diagrams, algorithmic vision appears as a practice of managing
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error. The practitioners we interviewed are aware of the error-prone
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nature of their systems but know it will never be perfect, and see it as
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a key metric that needs to be acted upon.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>The most prominent way in which error figures in the diagrams is in
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|
its quantified form of the true positive and false positive rates, TPR
|
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and FPR. The significance and definition of these metrics is stressed by
|
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|
|
|
CTO Gerwin van der Lugt (Diagram 6). In camera surveillance, the false
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|
positive rate could be described as the number of fales positive
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|
classifications relative to the number of video frames being analyzed.
|
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Upon writing down these definitions, van der Lugt corrected his initial
|
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definitions, as these definitions determine the work of his development
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team, the ways in which his clients — security operators — engage with
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the technology, and whether they perceive the output of the system as
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trustworthy.</p>
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</div>
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<div data-custom-style="Figure">
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<div class="anchor" data-i="0" style="height:2.3in"></div>
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</div>
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<div data-custom-style="Caption">
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<p>Diagram 6. Gerwin van der Lugt corrects his initial definitions of
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the true positive and false positive rates, and stresses the importance
|
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of their precise definition.</p>
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</div>
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<div data-custom-style="Body Text">
|
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<p>The figuration of algorithmic security vision as inherently imprecise
|
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affects the operationalization of security practices. Van der Lugt’s
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example concerns whether the violence detection algorithm developed by
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Oddity.ai should be trained to categorize friendly fighting
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(<em>stoeien</em>) between friends as “violence” or not. In this
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context, van der Lugt finds it important to differentiate what counts as
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false positive in the algorithm’s evaluation metric from an error in the
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algorithm’s operationalization of a security question.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>He gives two reasons to do so. First, he anticipates that the
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exclusion of <em>stoeien</em> from the category of violence would
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negatively impact TPR. In the iterative development of self-learning
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systems, the TPR and FPR, together with the true and false
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<em>negative</em> rates must perform a balancing act. Van der Lugt
|
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outlines that with their technology they aim for fewer than 100 false
|
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positives per 100 million frames per week. The FPR becomes indicative of
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the algorithm’s quality, as too many faulty predictions will desensitize
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the human operator to system alerts.
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</p>
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</div>
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<div data-custom-style="Body Text">
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<p>This leads to van der Lugt’s second point: He fears that the
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exclusion of <em>stoeien</em> from the violence category might cause
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unexpected biases in the system. For example, instead of distinguishing
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violence from <em>stoeien</em> based on people’s body movements, the
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algorithm might make the distinction based on their age. For van der
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Lugt, this would be an undesirable and hard to notice form of
|
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discrimination. In developing algorithmic (in)security, error is figured
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not merely as a mathematical concept but (as shown in Diagram 6) as a
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notion that invites pre-emption — a mitigation of probable failure — for
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which the developer is responsible. The algorithmic condition of
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security vision is figured as the pre-emption of error.</p>
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</div>
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<div data-custom-style="Figure">
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<div class="anchor" data-i="1" style="height:6in"></div>
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</div>
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<div data-custom-style="Caption">
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<p>Diagram 7. By drawing errors on a timeline, van Rest calls attention
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to the pre-emptive nature of error in the development process of
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computer vision technologies.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>According to critical AI scholar Matteo Pasquinelli, “machine
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learning is technically based on formulas for error correction” (2019:
|
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2). Therefore, any critical engagement with such algorithmic processes
|
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needs to go beyond citing errors, “for it is precisely through these
|
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variations that the algorithm learns what to do” (Amoore, 2019: 164),
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pushing us to reconsider any argument based on the inaccuracy of the
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systems.</p>
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</div>
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<div data-custom-style="Body Text">
|
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|
<p>The example of <em>stoeien</em> suggests that it is not so much a
|
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|
question if, or how much, these algorithms err, but how these errors are
|
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|
anticipated and negotiated. Thus, taking error as a hallmark of machine
|
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|
|
learning we can see how practices of (in)security become shaped by the
|
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|
|
notion of mathematical error well beyond their development stages. Error
|
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|
|
figures centrally in the development, acquisition and deployment of such
|
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|
|
devices. As one respondent indicated, predictive devices are inherently
|
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|
erroneous, but the quantification of their error makes them amenable to
|
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|
"risk management.”</p>
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|
</div>
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|
<div data-custom-style="Body Text">
|
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|
<p>While much has been written about security technologies as a device
|
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|
<em>for</em> risk management, little is known about how security
|
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|
|
technologies are conceptualized as objects <em>of</em> risk management.
|
|
|
|
|
|
What happens then in this double relation of risk? The figure of the
|
|
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|
|
|
error enters the diagrams as a mathematical concept, throughout the
|
|
|
|
|
|
conversations we see its figure permeate the discourse around
|
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|
|
|
algorithmic security vision. By figuring algorithmic security vision
|
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|
|
through the notion of error, risk is placed at the heart of the security
|
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|
apparatus.
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|
</p>
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|
</div>
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|
</section>
|
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</section>
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|
<section id="con-figurations-of-algorithmic-security-vision-fragmenting-accountability-and-expertise"
|
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|
class="level1">
|
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|
<h1>Con-figurations of algorithmic security vision: fragmenting
|
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|
accountability and expertise</h1>
|
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<div data-custom-style="Body Text">
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|
<p>In the previous section we explored the changing <em>figurations</em>
|
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|
|
of key dimensions of algorithmic security vision, in this section we
|
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|
|
|
examine how these figurations <em>configure</em>. For Suchman, working
|
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|
with configurations highlights “the histories and encounters through
|
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which things are figured <em>into meaningful existence</em>, fixing them
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|
through reiteration but also always engaged in ‘the perpetuity of coming
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to be’ that characterizes the biographies of objects as well as
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subjects” (Suchman, 2012: 50, emphasis ours) In other words, we are
|
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|
|
|
interested in the practices and tensions that emerge as figurations
|
|
|
|
|
|
become embedded in material practices. We focus on two con-figurations
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|
|
that emerged in the interviews: the delegation of accountability to
|
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|
|
externally managed benchmarks, and the displacement of responsibility
|
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|
|
through the reconfiguration of the human-in-the-loop.</p>
|
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|
</div>
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|
<section id="delegating-accountability-to-benchmarks" class="level2">
|
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|
<h2>Delegating accountability to benchmarks</h2>
|
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|
|
<div data-custom-style="Body Text">
|
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|
|
<p>The first configuration is related to the evaluation of the error
|
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|
|
|
|
rate in the training of algorithmic vision systems: it involves
|
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|
|
datasets, benchmark institutions, and the idea of fairness as equal
|
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|
|
|
representation among different social groups. Literature on the ethical
|
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|
and political effects of algorithmic vision has notoriously focused on
|
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|
|
the distribution of errors, raising questions of ethnic and racial bias
|
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|
(e.g. Buolamwini and Gebru, 2018). Our interviews reflect the concerns
|
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|
of much of this literature as the pre-emption of error figured
|
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|
repeatedly in relation to the uneven distribution of error across
|
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|
minorities or groups. In Diagram 8, Ádám Remport draws how different
|
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|
visual traits have often led to different error rates. While the general
|
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|
error metric of an algorithmic system might seem "acceptable," it
|
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|
actually privileges particular groups, which is invisible when only the
|
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|
whole is considered. Jeroen van Rest distinguishes such errors from the
|
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|
inherent algorithmic imprecision in deep machine learning models, as
|
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|
systemic biases (Diagram 7), as they perpetuate inequalities in the
|
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|
society in which the product is being developed.</p>
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|
</div>
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|
<div data-custom-style="Figure">
|
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|
<div class="anchor" data-i="2" style="height:4in"></div>
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|
</div>
|
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|
<div data-custom-style="Caption">
|
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|
|
<p>Diagram 8. Ádám Remport describes that facial recognition
|
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|
technologies are often most accurate with white male adult faces,
|
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|
reflecting the datasets they are trained with. The FPR is higher with
|
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|
people with darker skin, children, or women, which may result in false
|
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|
flagging and false arrests.</p>
|
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|
</div>
|
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|
<div data-custom-style="Body Text">
|
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|
|
<p>To mitigate these concerns and manage their risk, many of our
|
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|
|
interviewees who develop and implement these technologies, externalize
|
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|
the reference against which the error is measured. They turn to a
|
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|
|
benchmark run by the American National Institute of Standards and
|
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|
Technology (NIST), which ranks facial recognition technologies by
|
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|
different companies by their error metric across groups. John Riemen,
|
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|
|
|
|
who is responsible for the use of forensic facial recognition technology
|
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|
|
|
|
at the Center for Biometrics of the Dutch police, describes how their
|
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|
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|
choice for software is driven by a public tender that demands a "top-10"
|
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|
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|
|
score on the NIST benchmark. The mitigation of bias is thus outsourced
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|
to an external, and in this case foreign, institution.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>We see in this outsourcing of error metrics a form of delegation that
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brings about a specific regime of (in)visibility. While a particular
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kind of algorithmic bias is rendered central to the NIST benchmark, the
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mobilization of this reference obfuscates questions on how that metric
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was achieved. That is to say, questions about training data are
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invisibilized, even though that data is a known site of contestation.
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For example, the NIST benchmark datasets are known to include faces of
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wounded people (Keyes, 2019). The Clearview company is known to use
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images scraped illegally from social media, and IBM uses a dataset that
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is likely in violation of European GDPR legislation (Bommasani et al.,
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2022: 154). Pasquinelli (2019) argued that machine learning models
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ultimately act as data compressors: enfolding and operationalizing
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imagery of which the terms of acquisition are invisibilized.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>Attention to this invisibilization reveals a discrepancy between the
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developers and the implementers of these technologies. On the one hand,
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the developers we interviewed expressed concerns about how their
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training data is constituted to gain a maximum false positive rate/true
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positive rate (FPR/TPR) ratio, while showing concern for the legality of
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the data they use to train their algorithms. On the other hand,
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questions about the constitution of the dataset have been virtually
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non-existent in our conversations with those who implement software that
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relies on models trained with such data. Occasionally this knowledge was
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considered part of the developers' intellectual property that had to be
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kept a trade secret. A high score on the benchmark is enough to pass
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questions of fairness, legitimizing the use of the algorithmic model.
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Thus, while indirectly relying on the source data, it is no longer
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deemed relevant in the consideration of an algorithm. This illustrates
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well how the invisibilization of the “compressed” dataset, in
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Pasquinelli’s terms, into a model, with the formalization of guiding
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metrics into a benchmark, permits a bracketing of accountability. One
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does not need to know how outcomes are produced, as long as the
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benchmarks are in order.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>The configuration of algorithmic vision’s bias across a complex
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network of fragmented locations and actors, from the dataset, to the
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algorithm, to the benchmark institution reveals the selective processes
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of (in)visibilization. This opens up fruitful alleys for new empirical
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research: What are the politics of the benchmark as a mechanism of
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legitimization? How does the outsourcing of assessing the error
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distribution impact attention to bias? How has the critique of bias been
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institutionalized by the security industry, resulting in the
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externalization of accountability, through dis-location and
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fragmentation?</p>
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</div>
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</section>
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<section id="reconfiguring-the-human-in-the-loop" class="level2">
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<h2>Reconfiguring the human-in-the-loop</h2>
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<div data-custom-style="Body Text">
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<p>A second central question linked to the delegation of accountability
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is the configuration in which the security operator is located. The
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effects of delegation and fragmentation in which the mitigation of
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algorithmic errors is outsourced to an external party, becomes visible
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in the ways in which the role of the security operator is configured in
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relation to the institution they work for, the software’s assessment,
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and the affected publics.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>The public critique of algorithms has often construed the
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<em>human-in-the loop</em> as one of the last lines of defense in the
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resistance to automated systems, able to filter and correct erroneous
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outcomes (Markoff, 2020). The literature in critical security studies
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has however problematized the representation of the security operator in
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algorithmic assemblages by discussing how the algorithmic predictions
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appear on their screen (Aradau and Blanke, 2018), and how the embodied
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decision making of the operator is entangled with the algorithmic
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assemblage (Wilcox, 2017). Moreover, the operator is often left guessing
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at the working of the device that provides them with information to make
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their decision (Møhl, 2021).
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</p>
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</div>
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<div data-custom-style="Body Text">
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<p>What our participants’ diagrams emphasized is how a whole spectrum of
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system designs emerges in response to similar questions, for example the
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issue of algorithmic bias. A primary difference can be found in the
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degree of understanding of the systems that is expected of security
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operators, as well as their perceived autonomy. Sometimes, the human
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operator is central to the system’s operation, forming the interface
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between the algorithmic systems and surveillance practices. Gerwin van
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der Lugt, developer of software at Oddity.ai that detects criminal
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behavior argues that “the responsibility for how to deal with the
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violent incidents is always [on a] human, not the algorithm. The
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algorithm just detects violence—that’s it—but the human needs to deal
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with it.”</p>
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</div>
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<div data-custom-style="Body Text">
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<p>Dirk Herzbach, chief of police at the Police Headquarters Mannheim,
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adds that when alerted to an incident by the system, the operator
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decides whether to deploy a police car. Both Herzbach and Van der Lugt
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figure the human-in-the-loop as having full agency and responsibility in
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operating the (in)security assemblage (cf. Hoijtink and Leese,
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2019).</p>
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</div>
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<div data-custom-style="Body Text">
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<p>Some interviewees drew a diagram in which the operator is supposed to
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be aware of the ways in which the technology errs, so they can address
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them. Several other interviewees considered the technical expertise of
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the human-in-the-loop to be unimportant, even a hindrance.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>Chief of police Herzbach prefers an operator to have patrol
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experience to assess which situations require intervention. He is
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concerned that knowledge about algorithmic biases might interfere with
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such decisions. In the case of the Moscow metro, in which a facial
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recognition system has been deployed to purchase tickets and open access
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gates, the human-in-the-loop is reconfigured as an end user who needs to
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be shielded from the algorithm’s operation (c.f. Lorusso, 2021). On
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these occasions, expertise on the technological creation of the suspect
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becomes fragmented.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>These different figurations of the security operator are held
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together by the idea that the human operator is the expert of the
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subject of security, and is expected to make decisions independent from
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the information that the algorithmic system provides.</p>
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</div>
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<div data-custom-style="Figure">
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<div class="anchor" data-i="3" style="height:6in"></div>
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</div>
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<div data-custom-style="Caption">
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<p>Diagram 9. Riemen explains the process of information filtering that
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is involved in querying the facial recognition database of the Dutch
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police.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>Other drivers exist, however, to shield the operator from the
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algorithm’s functioning, challenging individual expertise and
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acknowledging the fallibility of human decision making. In Diagram 9,
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John Riemen outlines the use of facial recognition by the Dutch police.
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He describes how data from the police case and on the algorithmic
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assessment is filtered out as much as possible from the information
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provided to the operator. This, Riemen suggests, might reduce bias in
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the final decision. He adds that there should be no fewer than three
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humans-in-the-loop who operate independently to increase the accuracy of
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the algorithmic security vision.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>Instead of increasing their number, there is another configuration of
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the human-in-the-loop that responds to the fallibility of the operator.
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For the Burglary-Free Neighborhood project in Rotterdam, project manager
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Guido Delver draws surveillance as operated by neighborhood residents,
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through a system that they own themselves. By involving different
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stakeholders, Delver hopes to counter government hegemony over the
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surveillance apparatus. However, residents are untrained in assessing
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algorithmic predictions raising new challenges. Delver illustrates a
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scenario in which the algorithmic signaling of a potential burglary may
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have dangerous consequences: “Does it invoke the wrong behavior from the
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citizen? [They could] go out with a bat and look for the guy who has
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done nothing [because] it was a false positive.” In this case, the worry
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is that the erroneous predictions will not be questioned. Therefore, in
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Delver’s project the goal was to actualize an autonomous system, “with
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as little interference as possible.” Human participation or
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“interference” in the operation are potentially harmful. Thus, figuring
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the operator — whether police officer or neighborhood resident — as
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risky, can lead to the relegation of direct human intervention.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>By looking at the figurations of the operator that appear in the
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diagrams we see multiple and heterogeneous configurations of
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regulations, security companies, and professionals. In each
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configuration, the human-in-the-loop appears in different forms. The
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operator often holds the final responsibility in the ethical functioning
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of the system. At times they are configured as an expert in
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sophisticated but error-prone systems; at others they are figured as end
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users who are activated by the alerts generated by the system, and who
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need not understand how the software works and errs, or who can be left
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out.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>These configurations remind us that there cannot be any theorization
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of “algorithmic security vision,” both of its empirical workings and its
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ethical and political consequences without close attention to the
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empirical contexts in which the configurations are arranged. Each
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organization of datasets, algorithms, benchmarks, hardware and operators
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has specific problems. And each contains specific politics of
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visibilization, invisibilization, responsibility and accountability.</p>
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</div>
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</section>
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</html>
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