Data Detox Kit/DDK-AI_Bias-web — Italian @ Weblate: Now that you know about some of the weaknesses that …

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Key	English	Italian
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[0]/text:list-item[0]/text:p[0]/text:a[0]/text:span[0]	Some of the data	Some of the data
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[0]/text:list-item[0]/text:p[0]/text:span[0]	they’re trained on might be personal, copyrighted, or used without permission.	they’re trained on might be personal, copyrighted, or used without permission.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[1]/text:list-item[0]/text:p[0]/text:span[0]	Depending on the data sets, they might be full of	Depending on the data sets, they might be full of
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[1]/text:list-item[0]/text:p[0]/text:a[0]/text:span[0]	hate speech	hate speech
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[1]/text:list-item[0]/text:p[0]/text:span[1]	,	,
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[1]/text:list-item[0]/text:p[0]/text:a[1]/text:span[0]	conspiracy theories	conspiracy theories
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[1]/text:list-item[0]/text:p[0]/text:span[2]	, or information that’s	, or information that’s
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[1]/text:list-item[0]/text:p[0]/text:a[2]/text:span[0]	just plain wrong	just plain wrong
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[1]/text:list-item[0]/text:p[0]/text:span[3]	.	.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[1]/text:list-item[1]/text:p[0]	The data might be biased against certain people, genders, cultures, religions, jobs, or circumstances.	The data might be biased against certain people, genders, cultures, religions, jobs, or circumstances.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[16]/text:span[0]	Did you know?	Did you know?
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[16]/text:span[1]	AI tools are trained on data that also leaves stuff out altogether. If there’s little or no information about a group of people, language, or culture in the training data, it won’t be able to generate any answers about them. A key 2018 study by Joy Buolamwini called “	AI tools are trained on data that also leaves stuff out altogether. If there’s little or no information about a group of people, language, or culture in the training data, it won’t be able to generate any answers about them. A key 2018 study by Joy Buolamwini called “
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[16]/text:a[0]/text:span[0]	Gender Shades	Gender Shades
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[16]/text:span[2]	” identified how widespread facial recognition systems struggled to identify the faces of People of Color, especially Black women.	” identified how widespread facial recognition systems struggled to identify the faces of People of Color, especially Black women.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[17]	Shine a spotlight on bias	Shine a spotlight on bias
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[18]	Now that you know about some of the weaknesses that can exist in AI data sets, which are built by people like us, let’s take a look at ourselves. How can the way our human brains work shed light on AI's biases?	Now that you know about some of the weaknesses that can exist in AI data sets, which are built by people like us, let’s take a look at ourselves. How can the way our human brains work shed light on AI's biases?
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[19]	Imagine this scenario: you see a news headline about a topic you care about and before even clicking on it, you can imagine what it will say. It’s not because we’re all fortune tellers – it’s because we hold preconceived notions about certain topics. Perhaps you sometimes perpetuate biases by over-generalizing or jumping to conclusions:	Imagine this scenario: you see a news headline about a topic you care about and before even clicking on it, you can imagine what it will say. It’s not because we’re all fortune tellers – it’s because we hold preconceived notions about certain topics. Perhaps you sometimes perpetuate biases by over-generalizing or jumping to conclusions:
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[2]/text:list-item[0]/text:p[0]/text:span[0]	Over-generalizing	Over-generalizing
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[2]/text:list-item[0]/text:p[0]/text:span[1]	means reaching hasty judgments from insufficient information.	means reaching hasty judgments from insufficient information.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[2]/text:list-item[1]/text:p[0]/text:span[0]	Jumping to conclusions	Jumping to conclusions
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[2]/text:list-item[1]/text:p[0]/text:span[1]	describes making conclusions with little or no evidence.	describes making conclusions with little or no evidence.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[20]	If you’ve done either of these, you’re not alone! These thought patterns are not uncommon. The key is to become aware of them and take concrete steps to ensure they stay in check.	If you’ve done either of these, you’re not alone! These thought patterns are not uncommon. The key is to become aware of them and take concrete steps to ensure they stay in check.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[21]/text:span[0]	Try it!	Try it!
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[21]/text:span[1]	Think back to an earlier example: people who let their dog sleep in their bed. Imagine who those people are, generally speaking. What do people who do that have in common?	Think back to an earlier example: people who let their dog sleep in their bed. Imagine who those people are, generally speaking. What do people who do that have in common?
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[3]/text:list-item[0]/text:p[0]	Where do people who do that tend to live?	Where do people who do that tend to live?
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[3]/text:list-item[1]/text:p[0]	How much money do people who do that tend to make?	How much money do people who do that tend to make?
odf///office:document-content[0]/office:body[0]/office:text[0]/text:list[3]/text:list-item[2]/text:p[0]	How clean or messy do they tend to keep their houses?	How clean or messy do they tend to keep their houses?
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[22]	If you could imagine certain characteristics or behaviors of people who wear their ‘outside shoes’ in the house, you may have just over-generalized or jumped to conclusions about them.	If you could imagine certain characteristics or behaviors of people who wear their ‘outside shoes’ in the house, you may have just over-generalized or jumped to conclusions about them.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[23]/text:span[0]	If you did	If you did
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[23]/text:span[1]	not	not
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[23]/text:span[2]	make generalizations, and recognize that a habit does not necessarily define other aspects of their lives – that’s great! But hold on! Before you think you’re above bias, stay with us until the end because there is much more to explore.	make generalizations, and recognize that a habit does not necessarily define other aspects of their lives – that’s great! But hold on! Before you think you’re above bias, stay with us until the end because there is much more to explore.

Key	English	Italian
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[10]/text:span[2]	”, (3) “	”, (3) “
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[10]/text:span[3]	”, (4) “	”, (4) “
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[10]/text:span[4]	”, (5) “	”, (5) “
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[10]/text:span[5]	”, (6) “	”, (6) “
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[10]/text:span[6]	”.	”.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[11]	Expose the cracks in AI	Expose the cracks in AI
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[12]	Now that we’ve looked at some popular AI tools, let’s zoom in on how chatbots work, which are tools that generate texts.	Now that we’ve looked at some popular AI tools, let’s zoom in on how chatbots work, which are tools that generate texts.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[13]	AI is designed by people and trained on data sets. Just like you, the people who build it have certain beliefs, opinions and experiences that inform their choices, whether they realize it or not. The engineers and companies that build and train AI may think certain information or goals are more important than others. Depending on which data sets they ‘feed’ to the AI tools they build – like algorithms or chatbots – those machines might serve up biased results. That’s why AI can produce inaccurate data, generate false assumptions, or make the same bad decisions as a person.	AI is designed by people and trained on data sets. Just like you, the people who build it have certain beliefs, opinions and experiences that inform their choices, whether they realize it or not. The engineers and companies that build and train AI may think certain information or goals are more important than others. Depending on which data sets they ‘feed’ to the AI tools they build – like algorithms or chatbots – those machines might serve up biased results. That’s why AI can produce inaccurate data, generate false assumptions, or make the same bad decisions as a person.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[14]	Chatbots have been fed so much data that they can write computer code and ace exams. But they also present things as facts that aren’t always true. They can also generate texts that repeat the biases that already existed in their training data or in the programmers who trained them.	Chatbots have been fed so much data that they can write computer code and ace exams. But they also present things as facts that aren’t always true. They can also generate texts that repeat the biases that already existed in their training data or in the programmers who trained them.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[15]	While some experts believe text generation tools are getting ‘smarter’ on their own, others say they’re not actually understanding the words they repeat. Here are some reasons why you might want to think about the biases behind what chatbots tell you:	While some experts believe text generation tools are getting ‘smarter’ on their own, others say they’re not actually understanding the words they repeat. Here are some reasons why you might want to think about the biases behind what chatbots tell you:
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[16]/text:a[0]/text:span[0]	Gender Shades	Gender Shades
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[16]/text:span[0]	Did you know?	Did you know?
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[16]/text:span[1]	AI tools are trained on data that also leaves stuff out altogether. If there’s little or no information about a group of people, language, or culture in the training data, it won’t be able to generate any answers about them. A key 2018 study by Joy Buolamwini called “	AI tools are trained on data that also leaves stuff out altogether. If there’s little or no information about a group of people, language, or culture in the training data, it won’t be able to generate any answers about them. A key 2018 study by Joy Buolamwini called “
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[16]/text:span[2]	” identified how widespread facial recognition systems struggled to identify the faces of People of Color, especially Black women.	” identified how widespread facial recognition systems struggled to identify the faces of People of Color, especially Black women.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[17]	Shine a spotlight on bias	Shine a spotlight on bias
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[18]	Now that you know about some of the weaknesses that can exist in AI data sets, which are built by people like us, let’s take a look at ourselves. How can the way our human brains work shed light on AI's biases?	Now that you know about some of the weaknesses that can exist in AI data sets, which are built by people like us, let’s take a look at ourselves. How can the way our human brains work shed light on AI's biases?
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[19]	Imagine this scenario: you see a news headline about a topic you care about and before even clicking on it, you can imagine what it will say. It’s not because we’re all fortune tellers – it’s because we hold preconceived notions about certain topics. Perhaps you sometimes perpetuate biases by over-generalizing or jumping to conclusions:	Imagine this scenario: you see a news headline about a topic you care about and before even clicking on it, you can imagine what it will say. It’s not because we’re all fortune tellers – it’s because we hold preconceived notions about certain topics. Perhaps you sometimes perpetuate biases by over-generalizing or jumping to conclusions:
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[20]	If you’ve done either of these, you’re not alone! These thought patterns are not uncommon. The key is to become aware of them and take concrete steps to ensure they stay in check.	If you’ve done either of these, you’re not alone! These thought patterns are not uncommon. The key is to become aware of them and take concrete steps to ensure they stay in check.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[21]/text:span[0]	Try it!	Try it!
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[21]/text:span[1]	Think back to an earlier example: people who let their dog sleep in their bed. Imagine who those people are, generally speaking. What do people who do that have in common?	Think back to an earlier example: people who let their dog sleep in their bed. Imagine who those people are, generally speaking. What do people who do that have in common?
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[22]	If you could imagine certain characteristics or behaviors of people who wear their ‘outside shoes’ in the house, you may have just over-generalized or jumped to conclusions about them.	If you could imagine certain characteristics or behaviors of people who wear their ‘outside shoes’ in the house, you may have just over-generalized or jumped to conclusions about them.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[23]/text:span[0]	If you did	If you did
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[23]/text:span[1]	not	not
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[23]/text:span[2]	make generalizations, and recognize that a habit does not necessarily define other aspects of their lives – that’s great! But hold on! Before you think you’re above bias, stay with us until the end because there is much more to explore.	make generalizations, and recognize that a habit does not necessarily define other aspects of their lives – that’s great! But hold on! Before you think you’re above bias, stay with us until the end because there is much more to explore.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[24]	There are types of biases that are more deeply ingrained in individuals, organizations, cultures, and societies. Shine a light on them by reflecting on these questions:	There are types of biases that are more deeply ingrained in individuals, organizations, cultures, and societies. Shine a light on them by reflecting on these questions:
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[25]/text:span[0]	The biases you just reflected on often rely on assumptions, attitudes, and stereotypes that have been part of cultures for a very long time and can influence your decision-making in	The biases you just reflected on often rely on assumptions, attitudes, and stereotypes that have been part of cultures for a very long time and can influence your decision-making in
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[25]/text:span[1]	unconscious ways. This is why they’re called	unconscious ways. This is why they’re called
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[25]/text:span[2]	implicit biases	implicit biases
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[25]/text:span[3]	– they’re often hard-wired into your mindset, difficult to spot, and uncomfortable to confront.	– they’re often hard-wired into your mindset, difficult to spot, and uncomfortable to confront.
odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[26]	Common implicit biases include:	Common implicit biases include:

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English	Italian
Now that you know about some of the weaknesses that can exist in AI data sets, which are built by people like us, let’s take a look at ourselves. How can the way our human brains work shed light on AI's biases?	Now that you know about some of the weaknesses that can exist in AI data sets, which are built by people like us, let’s take a look at ourselves. How can the way our human brains work shed light on AI's biases?	DDK-AI_Bias-web

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odf///office:document-content[0]/office:body[0]/office:text[0]/text:p[18]

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ddk-ai_bias-web/it.odt, string 58