TED: The era of blind faith in big data must end | Cathy O'Neil

The video discusses the pervasive use of algorithms in society, highlighting their potential biases and consequences. It explains how algorithms are built and trained, often embedding human opinions and biases. Through case studies, it illustrates the real-world impact of biased algorithms in education, employment, and justice. The video advocates for algorithmic audits to ensure fairness and accountability, urging viewers to demand transparency and ethical considerations in algorithmic design.

What is a key component needed to build an algorithm?

• A large team of developers

• Data and a definition of success

• A powerful computer

• A secret formula

How are algorithms often perceived by the public?

• As subjective and biased

• As objective and scientific

• As simple and easy to understand

• As irrelevant to daily life

What was the issue with the value-added model used for teacher assessments?

• It was too simple

• It was a random number generator

• It was publicly available

• It was too expensive

What can happen if algorithms are applied without considering fairness?

• They can become more efficient

• They can automate the status quo

• They can create new opportunities

• They can eliminate all biases

What is a potential consequence of biased data in algorithms?

• Increased accuracy

• Faster processing times

• Perpetuation of existing inequalities

• More user-friendly interfaces

What is a 'weapon of mass destruction' in the context of algorithms?

• A highly efficient algorithm

• An algorithm that is open-source

• An algorithm with destructive impact

• A military-grade algorithm

What is the speaker's view on the free market solving algorithmic bias?

• It will create more competition

• It will have no effect

• It will make it worse

• It will solve the problem

What is an algorithmic audit?

• A way to make algorithms more complex

• A process to speed up algorithms

• A method to check algorithms for fairness

• A tool to hide algorithmic biases

What is the role of data scientists according to the speaker?

• To be the arbiters of truth

• To create secret algorithms

• To ignore societal issues

• To translate ethical discussions

What is the speaker's message to non-data scientists?

• To ignore algorithms

• To learn coding

• To trust all data

• To demand accountability