Human vs. algorithmic bias – is unbiased decision-making even a thing? – WS 07 2021: Difference between revisions

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Public policy in many countries favours the development and application of machine learning and other technologies broadly designated as “artificial intelligence” – with a view of boosting economy, streamlining the processes in the public sector and improving the peoples’ quality of life. To that end, human decision-making is replaced or supplemented by automation, and automated decision-making already affects millions of people in Europe and around the world.
Public policy in many countries favours the development and application of machine learning and other technologies broadly designated as “artificial intelligence” – with a view of boosting economy, streamlining the processes in the public sector and improving the peoples’ quality of life. To that end, human decision-making is replaced or supplemented by automation, and automated decision-making already affects millions of people in Europe and around the world.


The long-term result, however, might be a net harm, if the automated systems merely amplify the flaws of human decision-making due to inappropriate bias in the systems’ input data, design or operation.
The long-term result, however, might be a net harm, if automated systems merely reproduce the flaws of human decision-making due to inappropriate bias in the systems’ input data and generate new bias because deep learning even creates bias with perfect data


But if to err is human, is it even feasible to avoid bias altogether – either in human or automated decision-making?
But if to err is human, is it even feasible to avoid bias altogether – either in human or automated decision-making?

Revision as of 09:14, 8 June 2021

29 June 2021 | 16:30-17:30 CEST | Studio Belgrade
Consolidated programme 2021 overview / Day 1

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Working title: Human vs. algorithmic bias – is unbiased decision-making even a thing?
Proposals: #2

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Session teaser

Public policy in many countries favours the development and application of machine learning and other technologies broadly designated as “artificial intelligence” – with a view of boosting economy, streamlining the processes in the public sector and improving the peoples’ quality of life. To that end, human decision-making is replaced or supplemented by automation, and automated decision-making already affects millions of people in Europe and around the world.

The long-term result, however, might be a net harm, if automated systems merely reproduce the flaws of human decision-making due to inappropriate bias in the systems’ input data and generate new bias because deep learning even creates bias with perfect data

But if to err is human, is it even feasible to avoid bias altogether – either in human or automated decision-making?

And provided that the bias problem can be managed, are there any other substantial problems with using AI for taking significant decisions?

The goal of this workshop is to inform the discussion on AI policy and regulation in Europe and to further the understanding of these problems by the public at large.

Session description

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People

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Focal Point

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  • Elena Dodonova, Council of Europe
  • Yannick Meneceur, Council of Europe

Organising Team (Org Team) List Org Team members here as they sign up.

Subject Matter Expert (SME)

  • Jörn Erbguth

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  • André Melancia
  • Desara Dushi
  • Amali De Silva-Mitchell
  • Yannick Meneceur, Council of Europe

Proposed Key Participants

  • Representative from digital industry
  • Karine Gentelet, Associate professor at Université du Québec en Outaouais and Abeona-ENS-Obvia Chair on Artificial Intelligence and Social Justice (Québec) (TBC)
  • Representative from the CAHAI
  • Daniel Leufer, Access Now
  • Representative from CoE Observer State

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Proposed Moderator

  • Alexandr Tiulkanov, Special advisor to the Digital Development Unit, Council of Europe

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Messages

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