Posts Tagged ‘organisational culture’

statistical noise

The heightened tendency to tune out some data as unimportant is a well-known side effect of expertise, which encourages leaders to become highly attuned to some signals and patterns at the expense of others.

Yet many things in life—academic publishing, health care, and housing policy among them—require addressing individual challenges within the context of complex systems. People engaged in designing systems, from business plans to public policy, must compel themselves to deeply and empathetically understand both the needs of the people they are designing for and the systems in which they operate, and critically question what their legitimate desire for fairness and consistency leaves out. If they don’t, their well-meaning efforts to reduce noise may inadvertently strip away essential signals, causing them to miss patterns, gaps, and perspectives in data that deserve their attention.

via Behavioral Scientist, 24 August 2020

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Belinda Duarte

Belinda Duarte is the CEO of Culture is Life, an organisation pushing forward Indigenous-led solutions to lower the rate of youth suicide. She’s also the recipient of the 2020 Chief Executive Women Vincent Fairfax Fellowship scholarship.

As a proud Wotjobaluk and Dja Dja Wurrung woman, Duarte points to the first time she experienced racism as a child, and how it woke her up to the inherent challenges and disadvantages Aboriginal people faced in their country compared to their white counterparts.

Since then, she has drawn on the strength and experience of her elders past and present, and her community, to inform her leadership and achieve change for Aboriginal and Torres Strait Islander people.

via PROBono, 24 August 2020

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AI bias

Engineers at Amazon created an AI hiring tool they hoped would change hiring for good, and for the better, by bypassing the biases and errors of human hiring managers.

Instead, the machine simply learned to make the kind of mistakes its creators wanted to avoid. It’s a good example of how AI is only as smart as the input it gets.

If biases are present in the data, machines will learn and replicate them. On the flip side, if AI can identify the subtle decisions that end up excluding people from employment, it can also spot those that lead to more diverse and inclusive workplaces.

via The New York Times, 10 March 2020

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