The document highlights a discussion led by Toby Beresford on algorithmic accountability at Magic Freebies, a platform serving 2 million members. Key topics include algorithmic aversion, accountability principles such as awareness and explainability, and the responsibilities associated with deploying algorithms. It also emphasizes what Magic Freebies and major companies like Google and Facebook are doing to address these issues.

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Algorithmic
Accountability
is coming
Toby Beresford
Head of Client Engagement
Magic Freebies

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Magic Freebies
• MagicFreebies is a daily
publisher serving 2M members.
• For clients, Magic Freebies
offers a curated introduction
bringing them affordable
consumer leads at scale.

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Today we’ll cover…
• Algorithmic Aversion and Appreciation
• When Algorithms Go Wrong
• Algorithmic Accountability & Principles
• What MagicFreebies are doing
• What Google and Facebook are doing

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Algorithmic Aversion

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Photo: David Sugden

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Algorithmic Appreciation

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When Algorithms Go Wrong

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Algorithmic Accountability

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Getting Ready

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Algorithmic Accountability Principles
• Awareness
• Access and redress
• Accountability
• Explanation
• Data provenance
• Audit-ability
• Validation and testing

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Awareness
Users know where automated decisions have
been made, and owners are aware of the
potential harm automated decision making can
do.

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Access and redress
Users have access to a way to correct wrong
decisions.

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Accountability
If you deploy an algorithm you are responsible for
its decisions.

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Explanation
You should be able to explain the algorithm in
human terms.

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Data Provenance
You should be able to say where did the data
come from (and also where did training set data
come from).

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Auditability
You need to keep records of decisions made.

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Validation and Testing
Keep checking the algorithm is behaving as it
should.

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What we’re doing at
MagicFreebies

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What are Google &
Facebook doing?

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Time for a Magic
Freebie!
Pick up an Algorithmic
Accountability Principles info
sheet and take it back to your
team