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Data Ethics: Watching Out for Data Misuse

Data Ethics: Watching Out for Data Misuse

58mBeginner2021-06-16

Authors

Doug Rose

Doug Rose

Teaching Fortune 500s and professionals how to lead change

Course details

Technology has given organizations an opportunity to work with data in interesting new ways, but now governments, citizens, and customers are taking a close look at how companies use or misuse data. If one of your customers posts harmful information, should you take it down? Can you use data to manipulate your customer’s behavior? The answers to these questions have an enormous impact on how your customer views your organization. Yet, these decisions aren’t happening in the boardroom. Instead, they’re made in much smaller meetings by people just like you. Instructor Doug Rose gives you the understanding and skills you need to discuss these issues in a way that's both meaningful and productive. Doug begins with ethical views that you need to consider. He goes over ways data can be misused, by a company and by a customer. Then Doug goes over the responsibility to be accurate and what you can do when inaccurate materials are propagated. He concludes with an overview of data challenges. This course was created for LinkedIn Learning by Doug Rose. We are pleased to offer this training in our library.

Skills covered

Data GovernanceData PrivacyData ScienceOne-Off

Concepts

0. Introduction

  • 01 - Data accuracy and misuse

1. Thinking about Ethics

  • 02 - What are your ethical duties
  • 03 - Do the ends justify the means
  • 04 - How to be a virtuous organization
  • 05 - Create an ethical contract

2. Data Misuse

  • 06 - Data misuse
  • 07 - When your customer misuses the data
  • 08 - Is it ethical to micro-target
  • 09 - Can you exploit human needs
  • 10 - How to ethically engage your customer
  • 11 - Should you promote democracy

3. Accuracy

  • 12 - What does it mean to be accurate
  • 13 - Should you present the truth
  • 14 - What to do with fake news
  • 15 - What to do if your customer spreads propaganda
  • 16 - The danger of too much accuracy

4. Data Models

  • 17 - What are data models
  • 18 - Your customer could game the system

Conclusion

  • 19 - Next steps

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