In recent years, there has been an increasing awareness of both the public and scientific community that algorithmic systems can reproduce, amplify, or even introduce unfairness in our societies. These lecture notes provide an introduction to some of the core concepts in algorithmic fairness research. We list different types of fairness-related harms, explain two main notions of algorithmic fairness, and map the biases that underlie these harms upon the machine learning development process.
翻译:近年来,公众和科学界都日益认识到算法系统可以复制、扩大甚至引入我们社会中的不公平现象。 这些演讲说明介绍了算法公平研究中的一些核心概念。 我们列举了与公平有关的各类伤害,解释了算法公正两个主要概念,并描绘了这些伤害背后的偏见,这些偏见是机器学习发展过程的基础。