Nowadays “Artificial Intelligence” is everywhere! And rightly so, it does enable us to do really cool things, things we couldn’t even imagine doing just a decade ago. In fact, it sometimes just feels like magic. This ‘magic’ behind it is often powered by “Machine Learning”. But even “AI” has its limitations.
I’ll show examples where “AI” and ML have failed (sometimes with horrible consequences) and will explain why failures are unavoidable in ML but also mention what we can do to reduce them in the future.
Furthermore, I’ll showcase how current AI implementations discriminate against minorities and how that in some cases even leads to a higher risk of death for those groups. I’ll cover the bias that humans introduce and I’ll explain how poor choice of data makes our world even more unjust than it already is.
The takeaway for the audience: AI can fail and sometimes it has horrible consequences. Why is AI so hard to “do right”? How can we make AI better?
You can watch the talk by clicking any of links or the video below.
The talk was presented at the following conferences:
- NDC Sydney 2020
- Remote Chaos Communication Congress
- C# corner AMA (interview and presentation)
- Java Day Istanbul 2020
- Light Up 2020 (Unicef fundraiser)
- Tech community day
- JAX 2020
- DevTalks Romania
- Stackconf 2020
- DataEngBytes Australia
- Global AI on tour (Belgium edition)
- Clearwater Development Conference 2020
- AI DevWorld 2020
- Update conference 2020
- Stack 2020
- API days Paris 2020
- FOSSASIA Summit 2021
- CodeFest Ru (upcoming, probably 2021)