import{s as L,n as B,o as O}from"../chunks/scheduler.7bc62968.js";import{S as U,i as k,g,s as c,r as x,A as q,h as m,f as a,c as h,j as T,u as C,x as A,k as H,y as D,a as s,v as S,d as P,t as M,w as F}from"../chunks/index.2f8492b0.js";import{H as N,E as j}from"../chunks/EditOnGithub.2a9ce03a.js";function z(v){let o,p,f,d,n,w,i,E="We hope that you had an excited learning journey throughout the unit of Ethics and Bias in CV models. To explore more about the field in general, you can go through these learning resources:",b,r,I=`<li><a href="https://huggingface.co/blog?tag=ethics" rel="nofollow"><strong>Ethics and Society Newsletter</strong></a> by Hugging Face. This newsletter discusses the efforts of Hugging Face in the domain of Ethical AI. Hugging Face also has a separate space dedicated to collections, spaces, datasets and models involving ethical AI, <a href="https://huggingface.co/society-ethics" rel="nofollow">here</a>.</li> <li><a href="https://ethics.fast.ai/" rel="nofollow"><strong>Data Ethics</strong></a> course by fast.ai.</li> <li><a href="https://www.kaggle.com/learn/intro-to-ai-ethics" rel="nofollow"><strong>Intro to AI Ethics</strong></a> course on Kaggle Learn. This is a short course with exercises for beginners in the field.</li> <li><a href="https://dl.acm.org/doi/abs/10.1145/3351095.3375709" rel="nofollow"><strong>Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy</strong></a>
This paper discusses about the ImageNet dataset and how the bias in dataset was removed by filtering most of the synsets and balancing according to age, gender and color.</li> <li><a href="https://brief.montrealethics.ai/" rel="nofollow"><strong>The AI Ethics Brief</strong></a> newsletter by Montreal AI Ethics Institute, with a section on <a href="https://montrealethics.ai/category/columns/the-ethics-of-computer-vision/" rel="nofollow">Computer Vision</a>. The newsletter is a must read for learners and practitioners in the field.</li> <li><a href="https://ethics-of-ai.mooc.fi/" rel="nofollow"><strong>Ethics of AI</strong></a> MOOC by University of Helsinki.</li> <li><a href="https://stanfordaiethics.github.io/" rel="nofollow"><strong>CS 281</strong></a> Ethics of AI Course by Stanford University.</li> <li><a href="https://ocw.mit.edu/courses/res-10-002-ethics-of-ai-bias-spring-2023/" rel="nofollow"><strong>Ethics of AI Bias</strong></a> course by MIT OCW.</li>`,y,l,_,u,$;return n=new N({props:{title:"Supplementary reading and resources 🤗🌎",local:"supplementary-reading-and-resources-",headingTag:"h1"}}),l=new j({props:{source:"https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit12/supplementary-material.mdx"}}),{c(){o=g("meta"),p=c(),f=g("p"),d=c(),x(n.$$.fragment),w=c(),i=g("p"),i.textContent=E,b=c(),r=g("ul"),r.innerHTML=I,y=c(),x(l.$$.fragment),_=c(),u=g("p"),this.h()},l(e){const t=q("svelte-u9bgzb",document.head);o=m(t,"META",{name:!0,content:!0}),t.forEach(a),p=h(e),f=m(e,"P",{}),T(f).forEach(a),d=h(e),C(n.$$.fragment,e),w=h(e),i=m(e,"P",{"data-svelte-h":!0}),A(i)!=="svelte-1evq1v1"&&(i.textContent=E),b=h(e),r=m(e,"UL",{"data-svelte-h":!0}),A(r)!=="svelte-tqz9id"&&(r.innerHTML=I),y=h(e),C(l.$$.fragment,e),_=h(e),u=m(e,"P",{}),T(u).forEach(a),this.h()},h(){H(o,"name","hf:doc:metadata"),H(o,"content",V)},m(e,t){D(document.head,o),s(e,p,t),s(e,f,t),s(e,d,t),S(n,e,t),s(e,w,t),s(e,i,t),s(e,b,t),s(e,r,t),s(e,y,t),S(l,e,t),s(e,_,t),s(e,u,t),$=!0},p:B,i(e){$||(P(n.$$.fragment,e),P(l.$$.fragment,e),$=!0)},o(e){M(n.$$.fragment,e),M(l.$$.fragment,e),$=!1},d(e){e&&(a(p),a(f),a(d),a(w),a(i),a(b),a(r),a(y),a(_),a(u)),a(o),F(n,e),F(l,e)}}}const V='{"title":"Supplementary reading and resources 🤗🌎","local":"supplementary-reading-and-resources-","sections":[],"depth":1}';function W(v){return O(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class J extends U{constructor(o){super(),k(this,o,W,z,L,{})}}export{J as component};
