iPads, MakerEd and More in Education

Bias is a fundamental human characteristic. We are all biased, by our very nature, and every day we make countless decisions based on our gut feelings. We all have preconceived ideas, prejudices, and opinions. And that is fine, as long as we recognize it and take responsibility for it.

The fundamental promise of AI, besides the dramatic increase of data processing power and business efficiency, is to help reduce the conscious or unconscious bias of human decisions. At the end of the day, this is what we expect from algorithms, isn’t it? Objectivity, mathematical detachment rather than fuzzy emotions, fact-based rather than instinctive decisions. Algorithms are supposed to alert people to their cognitive blind spots, so they can make more accurate, unbiased decisions.

At least that’s the theory…

Part 1 of the series ‘Designing responsibly with AI’ to help design teams understand the ethical challenges when working with AI and have better control over the possible consequences of their design on people and society.

When students are learning about research topics and current events, they must also learn about how perspective and bias may affect the information they are reading. Teaching these lessons explicitly is critical in this era of “fake” news.

The following tips and activities are designed to help students understand the choices that journalists make that may affect how readers interpret a story.

Perhaps the biggest challenge we face when accessing information is confronting our biases before we are able to unpack the opinions and insights of other people. Often these biases are unconscious or implicit, meaning we might not even be aware we have them.

But these implicit biases have real implications, and educators are no less immune than students.

""Jmmy Gomez is a California Democrat, a Harvard graduate and one of the few Hispanic lawmakers serving in the US House of Representatives. But to Amazon's facial recognition system, he looks like a potential criminal.

Gomez was one of 28 US Congress members falsely matched with mugshots of people who've been arrested, as part of a test the American Civil Liberties Union ran last year of the Amazon Rekognition program.  Nearly 40 percent of the false matches by Amazon's tool, which is being used by police, involved people of color.

The findings reinforce a growing concern among civil liberties groups, lawmakers and even some tech firms that facial recognitioncould harm minorities as the technology becomes more mainstream. A form of the tech is already being used on iPhones and Android phones, and police, retailers, airports and schools are slowly coming around to it too. But studies have shown that facial recognition systems have a harder time identifying women and darker-skinnedpeople, which could lead to disastrous false positives."