TradeBriefs Editorial

From the Editor's Desk

How Machine Learning Pushes Us to Define Fairness

Bias is machine learning's original sin. It's embedded in machine learning's essence: the system learns from data, and thus is prone to picking up the human biases that the data represents. For example, an ML hiring system trained on existing American employment is likely to "learn" that being a woman correlates poorly with being a CEO.

Cleaning the data so thoroughly that the system will discover no hidden, pernicious correlations can be extraordinarily difficult. Even with the greatest of care, an ML system might find biased patterns so subtle and complex that they hide from the best-intentioned human attention. Hence the necessary current focus among computer scientists, policy makers, and anyone concerned with social justice on how to keep bias out of AI.

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TradeBriefs Editorial

From the Editor's Desk

The new dot com bubble is here: it's called online advertising

In 2018 $273bn was spent on digital ads globally. We delve into the world of clicks, banners and keywords to find out if any of it is real. What do we really know about the effectiveness of digital advertising?

Picture this. Luigi's Pizzeria hires three teenagers to hand out coupons to passersby. After a few weeks of flyering, one of the three turns out to be a marketing genius. Customers keep showing up with coupons distributed by this particular kid. The other two can't make any sense of it: how does he do it? When they ask him, he explains: "I stand in the waiting area of the pizzeria."

It’s plain to see that junior's no marketing whiz. Pizzerias do not attract more customers by giving coupons to people already planning to order a quattro stagioni five minutes from now.

Economists refer to this as a "selection effect."

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TradeBriefs Editorial

From the Editor's Desk

Can You Be a Great Leader Without Technical Expertise?

There is a broad assumption in society and in education that the skills you need to be a leader are more or less transferable. If you can inspire and motivate people in one arena, you should be able to apply those skills to do the same in another venue.

But recent research is rightly challenging this notion. Studies suggest that the best leaders know a lot about the domain in which they are leading, and part of what makes them successful in a management role is technical competence. For example, hospitals managed by doctors perform better than those managed by people with other backgrounds. And there are many examples of people who ran one company effectively and had trouble transferring their skills to the new organization.

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