fastbook/clean/03_ethics.ipynb
2020-03-06 10:19:03 -08:00

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"# Data Ethics"
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"### Sidebar: Acknowledgement: Dr Rachel Thomas"
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"### End sidebar"
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"## Key examples for data ethics"
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"### Bugs and recourse: Buggy algorithm used for healthcare benefits"
]
},
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"### Feedback loops: YouTube's recommendation system"
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"### Bias: Professor Lantanya Sweeney \"arrested\""
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"### Why does this matter?"
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"## Integrating machine learning with product design"
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"## Topics in Data Ethics"
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"### Recourse and accountability"
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"### Feedback loops"
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"### Bias"
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"#### Historical bias"
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"#### Measurement bias"
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"#### Aggregation Bias"
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"#### Representation Bias"
]
},
{
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"metadata": {},
"source": [
"## Addressing different types of bias"
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"metadata": {},
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"### Humans are biased, so does algorithmic bias matter?"
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"## Disinformation"
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"## Identifying and addressing ethical issues"
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},
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"metadata": {},
"source": [
"### Analyze a project you are working on"
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"### Processes to implement"
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},
{
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"metadata": {},
"source": [
"#### Ethical Lenses"
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},
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"metadata": {},
"source": [
"### The power of diversity"
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},
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"metadata": {},
"source": [
"### Fairness, accountability, and transparency"
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},
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"source": [
"## Role of Policy"
]
},
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"source": [
"### The effectiveness of regulation"
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"### Rights and policy"
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},
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"### Cars: a historical precedent"
]
},
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"## Conclusion"
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"## Questionnaire"
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"source": [
"### Further research:"
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"## Section 1: that's a wrap!"
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