Transparency and accountability

Introduction to Data Ethics

Shalini Kurapati, PhD

Co-founder and CEO, Clearbox AI

Transparency

  • Clarity, disclosure of data use
  • Increases trust, a proof of accountability
  • Communication and engagement with stakeholders
  • How?
    • Privacy policies/statements
    • Certifications and third party audits
    • Disclosure of mishaps

Illustration of a pair of glasses through which one sees clearly.

1 Photo by Bud Helisson on Unsplash
Introduction to Data Ethics

Good example

Screenshot of NYT study about incomprehensible privacy policies.

  • Complicated statements are useless
  • Concise, clear, plain language, high-school level

Screenshot of the BBC privacy policy.

  • Reads well, short, clear, declarative
  • Examples:
    • "We'll only send you marketing emails if you've agreed to this."
    • "You can delete your account. Your account information will be deleted immediately."
1 https://www.nytimes.com/interactive/2019/06/12/opinion/facebook-google-privacy-policies.html 2 https://www.bbc.co.uk/usingthebbc/privacy-policy-outside-the-uk/
Introduction to Data Ethics

Don't hide or mislead

A mix of 1s and 0s with the word HACKED written in the middle in bold red.

  • Transparency is being honest with your users
  • When a data breach happens, don't hide or mislead
  • Have a robust communication- affected parties
  • Advice, provide mitigation measures and channels to communicate
Introduction to Data Ethics

Reality check

Screenshot of the McKinsey State of AI report

  • McKinsey report from 2021 on State of AI on data and AI practices
  • 1843 professionals across the globe and across industries
  • 30% aware of data ethics risks
  • 17% have data governance teams
  • 27% check for biased datasets
1 https://www.mckinsey.com/capabilities/quantumblack/our-insights/global-survey-the-state-of-ai-in-2021
Introduction to Data Ethics

What does being accountable mean?

  • Core principle necessary implementing all others
  • Responsible for third party collaborators
  • Data protection and individual rights
  • Social implications at large
Introduction to Data Ethics

Who is accountable?

Illustration of a hand balancing a scale within a gear representing a business process

  • Everyone :)
  • Not limited to a single role
  • Data scientists, legal and risk, IT, C-suite
  • Corporate Digital Responsibility- the ESR for data ethics
1 **CDR**: Lobschat, L., et al. (2021). Corporate digital responsibility. Journal of Business Research, 122, 875-888. 2 Icon made by Eucalyp from www.flaticon.com
Introduction to Data Ethics

Let's practice!

Introduction to Data Ethics

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