Awujọ Itọsọna

AI & Awujọ

AI ni ipa lori awujọ nipasẹ awọn ile-iṣẹ, awọn iṣẹ, awọn eto alaye, ati awọn ibi iṣẹ ti o gba.

Gbọ si itọsọna yiiAI-ṣe afihan · ~2 min

Lori iwe yi2 min ka
  1. Akopọ
  2. Awọn gbigba bọtini
  3. Jin Dive
  4. Include people outside the average user
  5. Real-World imuse
  6. Fifi o sinu iwa
  7. Awọn orisun ati siwaju kika
  8. Tesiwaju Ṣiṣawari
  9. Awọn ibeere ti a beere nigbagbogbo

Akopọ

Benefits and harms are not necessarily shared equally. Evaluation needs to consider access, power, accountability, and the choices made around the technology, as well as technical performance.

Awọn gbigba bọtini

  1. Include affected non-users in the analysis.
  2. Assess access and the ability to challenge outcomes.
  3. Measure real impacts and revise the system.

Jin Dive

Identify who is affected, including people who do not directly use the system. An automated service can change access to assistance, the information people see, or the work expected from staff. Those effects may be missed by measuring only the operator’s efficiency. Consider distribution and participation. Language coverage, disability access, connectivity, cost, and the ability to challenge an outcome can determine who benefits. Consult affected communities when defining the problem and evaluating the proposed solution. Information environments require particular care. Generated content can increase access to explanations while also amplifying misleading material. Provenance, corrections, and transparent evidence help readers evaluate claims without requiring blind trust in a platform. Treat governance as a continuing process. Track actual outcomes, investigate complaints, and revise systems when uses or impacts change. Avoid assuming that broad social outcomes follow automatically from one model improvement. Institutions and policy choices shape how capabilities are deployed and who bears their consequences.

04Apẹẹrẹ ṣiṣẹ

Include people outside the average user

  1. Imagine a service moving important assistance to an AI-only chat interface.

  2. Test the experience for people with unreliable connectivity, uncommon questions, and assistive technology.

  3. Measure whether a staffed or alternative route remains available and whether unresolved cases receive effective follow-up.

Ohun ti o fihan

The constructed example expands evaluation from average interaction speed to access and accountability.

Real-World imuse

Evaluate a public information service across languages and accessibility needs.

Publish correction procedures and source links alongside generated explanations.

Fifi o sinu iwa

Kini idi ti o ṣe pataki

  • Ewu ati ailewu. Ajalu ati awọn ipalara AI lojoojumọ da lori tani o loye awọn ewu ati tani o le ṣe.
  • Awọn ipinnu diẹ sii. Imọwe ti gbogbo eniyan ati ọjọgbọn ṣe apẹrẹ boya eto imulo aabo to lagbara jẹ iṣe iṣelu ṣee ṣe.
  • Gige nipasẹ hype. Awọn alaye ti ko o dinku gbigba nipasẹ aruwo, PR lab, ati ile iṣere iṣere aiduro.

Ṣọra fun

  • Itoju eewu ayeraye bi sci-fi lakoko awọn agbo ogun agbara.
  • Aabo ọja dada iruju pẹlu titete labẹ adase to gaju.
  • Nlọ kuro ni ti kii ṣe Gẹẹsi ati awọn olugbo ti kii ṣe alamọja pẹlu awọn orisun didara kekere nikan.

Next awọn igbesẹ

  1. Awọn ipalara ọja lọtọ, ilokulo, ati isonu-iṣakoso / awọn eewu aiṣedeede.
  2. Beere ẹri wo ni yoo yi wiwo rẹ pada lori awọn akoko akoko ati idiwo.
  3. Ṣe ayanfẹ awọn orisun akọkọ ati awọn igbelewọn nija lori awọn ẹtọ tita.
  4. Ṣe idanimọ ọna iṣe kan: iṣẹ, eto imulo, igbeowosile, tabi awọn ọgbọn — kii ṣe akiyesi nikan.

Awọn orisun ati siwaju kika

  1. UNESCORecommendation on the Ethics of Artificial Intelligence

Tesiwaju Ṣiṣawari

Ìwé ìròyìn ọfẹ

Gba ìkànìyàn AI ojoojúmọ́

Ìtàn AI mẹta tó jẹ́rìí ní gbogbo owurọ́ ọjọ́ iṣẹ́, kọ́wé ní Gẹ̀ẹ́sì tó rọrùn. Ọfẹ́ fún gbogbo àkókò, kò sí ìpolówó.

Ìmeèlì kan ní gbogbo ọjọ́ iṣẹ́. Yọkúrò ní ìkànìyàn ní ìkànìyàn kan. A kò ta tàbí pín àdírẹ́sì rẹ.

Ṣe idanwo fun ara rẹ

Take the AI & Society quiz

Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.

Bẹrẹ adanwo

Ṣe àtìlẹ́yìn ẹ̀kọ́ AI ọfẹ́. AI Understanding jẹ́ àjọ àìní èrè 501(c)(3) — kò sí ìpolówó, kò sí àkúnya owó, ní gbogbo àkókò. Ṣe ẹbun kan

Awọn ibeere ti a beere nigbagbogbo

Will the same AI technology have the same social effect everywhere?

No. Institutions, resources, language, policy, and deployment choices can produce very different outcomes.