AI outperforms humans in prediction and classification tasks. Until you notice, that AI algorithms have denied women access to credit despite being more creditworthy than their male counterparts. Or that they fail to distinguish between black-skinned individuals and Gorillas. If you read the news, it quickly becomes obvious: AI has a bias problem. And not only predictive AI, but also generative AI like media darling ChatGPT, which - infamously - wrote a piece of code suggesting that only "White" or "Asian" men would be good scientists to hire. But is it the machines who are inherently biased or evil? Spoiler alert: no, it's not! Yet, AI fairness is one of the most pressing AI challenges to solve. Join Alexandra for this session to learn how bias finds its ways into AI and you, your organization and society at large should take to help overcome it.

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Alexandra Ebert
Chief TrustOfficer| MOSTLY AI
Host of the Data Democratization Podcast
Chair of the IEEE Synthetic Data IC Expert Group
(Un)Ethical Machines?
Why AI is Biased and What to Do About It

2. Bias in AI is nothing new

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6. But with ChatGPT & Co. it
gets even more apparent

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12. Why is AI biased in the first
place?

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Because AI is evil!

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Because AI is evil!
Oh, and AI is the same as robots, ofc!

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Because AI is evil!
And it wants to take over the world!

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NO!

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NO!
AI is not inherently evil

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NO!
AI is not a person

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NO!
AI is not sentient

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NO!
AI is built by humans

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NO!
And it’s humans who need to
get AI Fairness right

22. So, why is AI really biased?

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1. Insufficient training data (e.g. facial recognition systems, UK health app,…)
10 (out of many) reasons for bias in AI

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1. Insufficient training data (e.g. facial recognition systems, UK health app,…)
10 (out of many) reasons for bias in AI

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1. Insufficient training data (e.g. facial recognition systems, UK health app,…)
2. Humans are biased – and so is the data AI is trained on
10 (out of many) reasons for bias in AI

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1. Insufficient training data (e.g. facial recognition systems, UK health app,…)
2. Humans are biased – and so is the data AI is trained on
10 (out of many) reasons for bias in AI

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1. Insufficient training data (e.g. facial recognition systems, UK health app,…)
2. Humans are biased – and so is the data AI is trained on (e.g. Amazon’s HR algorithm)
3. De-biasing data is exceptionally hard to do (removing sensitive attributes is not a good option!)
4. De-biasing AI models is very difficult too
5. Diversity amongst AI professionals is not as high as it should be
6. Fairness comes at a cost (that companies may not be willing to pay)
7. External AI audits could help – if privacy would not be an issue
8. Fairness is hard to define
9. What was fair yesterday could be biased tomorrow (e.g. Microsoft’s Tay)
10.The vicious bias cycle: biased AI will lead to more bias in data
10 (out of many) reasons for bias in AI

28. What to do about AI bias?

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What to do about AI bias?
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Regulation

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What to do about AI bias?
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Regulation
Research

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What to do about AI bias?
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Regulation
RAI Assurance
Ecosystems
Research

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What to do about AI bias?
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Regulation
Education
& Training
RAI Assurance
Ecosystems
Research

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What to do about AI bias?
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Regulation
Education
& Training
RAI Assurance
Ecosystems
Diversity
in AI Teams
Research

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What to do about AI bias?
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Regulation
Education
& Training
RAI Assurance
Ecosystems
Diversity
in AI Teams
Research
Data
Demo-
cratization

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What to do about AI bias?
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Regulation
Education
& Training
RAI Assurance
Ecosystems
Diversity
in AI Teams
Diversity
in Data
Research
Data
Demo-
cratization

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