fix typos in lecture 1 social impact

introduction/index.md

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-            <p    >A system like COMPAS, that disproportionally denies black people parole is said to have <strong>a bias</strong>. This kind of bias can come from different places.<br></p><p    >One important source of bias is the distribution of the training data. Where we get our data has a tremendous impact on what the model learns. Since machine learning often requires large amounts of data, we usually can’t afford to control the gathering of data very carefully: unlike studies in life sciences, medicine and so on, we rarely make sure that all variables are carefully controlled. <br></p><p    >The result is that systems have unexpected biases. This is a picture of Joy Buolamwini. As a PhD student, she worked on existing face recognition systems. She found that if she tested them on her own face, they would not recognize her, and she needed to wear a light-colored mask to be recognized at all.<br></p><p    >One aspect of this problem is the bias in the data that face recognition systems are trained on. If, for instance, such data is gathered carelessly, we end up inheriting whatever biases our source has. If white people are overrepresented, then we end up training a system that works less well on non-white people.<br></p><p    >image source: <a href="https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html"><strong>https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html</strong></a></p><p    ><a href="https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html"><strong></strong></a></p>
+            <p    >A system like COMPAS, that disproportionately denies black people parole is said to have <strong>a bias</strong>. This kind of bias can come from different places.<br></p><p    >One important source of bias is the distribution of the training data. Where we get our data has a tremendous impact on what the model learns. Since machine learning often requires large amounts of data, we usually can’t afford to control the gathering of data very carefully: unlike studies in life sciences, medicine and so on, we rarely make sure that all variables are carefully controlled. <br></p><p    >The result is that systems have unexpected biases. This is a picture of Joy Buolamwini. As a PhD student, she worked on existing face recognition systems. She found that if she tested them on her own face, they would not recognize her, and she needed to wear a light-colored mask to be recognized at all.<br></p><p    >One aspect of this problem is the bias in the data that face recognition systems are trained on. If, for instance, such data is gathered carelessly, we end up inheriting whatever biases our source has. If white people are overrepresented, then we end up training a system that works less well on non-white people.<br></p><p    >image source: <a href="https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html"><strong>https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html</strong></a></p><p    ><a href="https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html"><strong></strong></a></p>
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@@ -1379,7 +1379,7 @@ slides: true
             <img src="11.Introduction.0.key-stage-0101.png" class="slide-image" />
 
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-            <p    >So, let’s return to our gender classifier, and ask some of these questions. Are sex and gender a sensitive attributes and if so, what should we do about gender classification?<br></p><p    >We've already seen, in the translation example, that data bias is an important problem when dealing with gender in data. Even if genders are carefully represented in your data, they may be associated in a biased way, such as associating doctors with men and nurses with women. As we saw, even if these biases are an accurate reflection of the state of society, we may still be in danger of amplifying them.<br></p><p    >Still, that does not in itself preclude us from using sex or gender as a target attribute for classification. To understand the controversy, we need to look at different questions.</p><p    ></p>
+            <p    >So, let’s return to our gender classifier, and ask some of these questions. Are sex and gender sensitive attributes and if so, what should we do about gender classification?<br></p><p    >We've already seen, in the translation example, that data bias is an important problem when dealing with gender in data. Even if genders are carefully represented in your data, they may be associated in a biased way, such as associating doctors with men and nurses with women. As we saw, even if these biases are an accurate reflection of the state of society, we may still be in danger of amplifying them.<br></p><p    >Still, that does not in itself preclude us from using sex or gender as a target attribute for classification. To understand the controversy, we need to look at different questions.</p><p    ></p>
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