import{s as it,n as nt,o as st}from"../chunks/scheduler.35aab934.js";import{S as at,i as lt,e as l,s,c as _,h as ot,a as o,d as i,b as a,f as tt,g as I,j as x,k as et,l as rt,m as n,n as A,t as P,o as E,p as L}from"../chunks/index.de17b971.js";import{C as ut}from"../chunks/CopyLLMTxtMenu.48bc5cc2.js";import{H as N,E as pt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.8fdece89.js";function mt(U){let r,T,C,M,u,F,p,j,m,O="Welcome to the Ethics and Bias unit of our Computer Vision Course! 📸✨ This segment is designed to explore the critical elements of ethics and bias within the domain of computer vision.",H,h,S,d,D=`In this unit we’ll understand the ethical dimensions and potential biases within AI models and how it is crucial for responsible development and deployment of computer vision systems.
A brief outline of the unit is given below:`,V,c,K="<li>We will start off this unit with Chapter 1, where we discuss the implications of the popular ImageNet Roulette Case Study.</li> <li>In Chapter 2, we’ll discuss about the ethical considerations connected with AI and computer vision technologies and why it is important to be fair, while developing CV systems.</li> <li>In Chapter 3, we’ll learn how bias can infiltrate AI models across various modalities such as text, vision, and speech.</li> <li>In Chapter 4, we’ll discuss various types of biases and their implications on computer vision models.</li> <li>In Chapter 5, we’ll discuss different ways to spot bias, and metrics for evaluating biases in CV models with the help of practical case studies.</li> <li>In Chapter 6, we’ll learn about the strategies and methods to mitigate biases specifically within computer vision models.</li> <li>Finally, we close with Chapter 7 and discuss HuggingFace’s mission and initiatives toward fostering ethical AI for society.</li>",z,f,W,g,Q="Let’s begin our journey that merges theoretical foundations, practical case studies, and ethical concerns inherent in the landscape of computer vision. From exploring real-world examples like the ImageNet Roulette case study to evaluating biases in AI models recognizing “Gay Face”, Twitter’s Saliency Algorithm and similar case studies, this unit dives deep into understanding, assessing, and mitigating biases in computer vision systems.",k,v,X="By the conclusion of this unit, you’ll have gained insights into recognizing biases, evaluating them in CV models, and effectively employing strategies to mitigate these biases. Additionally, you’ll explore Hugging Face’s efforts to promote ethical practices within AI, providing a roadmap towards responsible and transparent AI development.",q,w,Y=`Join us as we navigate the domain of ethics and bias in computer vision, equipping ourselves to contribute ethically and responsibly to the future of AI and society.
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