We’ve all heard of unconscious bias and how it can be all too easy to make judgements about someone without having enough relevant information.

The same can be true of predictive computer algorithms. You know the ones: "If you liked X, then you may like Y."

Predictive algorithms are designed to make it easy to match consumers with what they’re likely to buy. The algorithms are designed to create efficiency for both the advertisers and the consumers.

These predictive algorithms, often relying on “lookalike” data, are intended to remove implicit and explicit bias in critical decision making. However, it has been suggested that algorithms often reflect and amplify past racial and gender injustices.

Do you want to receive blog posts like this in your inbox? Click here to subscribe to Electives news.

How do you know if your processes are working for you or against you?

In today’s society, with so much information at our fingertips, it’s important to educate ourselves by asking the right questions and not just trusting the first answer we’re presented with. In fact, the more we educate ourselves on a topic, and the more we stay open to other points of view, the better we can evaluate our own performance, progress and success.

For teams with rich processes or processes that are ready for optimization, it’s important to know how decisions are made – no matter whether they’re made by person or computer. For teams just developing and implementing new processes, now is a great time to understand the pros and cons of algorithms, artificial intelligence and machine learning.

Data & Race: A cautionary tale, with Hany Fanid, is an Electives class that helps teams to learn how to ask the right questions and be open to new ways of thinking. This class looks at whether AI improves or further entrenches racial, gender and identity bias, and evaluates the claim that predictive computer algorithms are more accurate and fair than people tasked with making similar decisions.

Hany also explains the presence of racial bias in predictive algorithms used in the criminal justice system and discusses the broader applications of turning over key decision making to AI systems.

Hany is a professor at the University of California, Berkeley, and his research focuses on digital forensics, forensic science, misinformation, image analysis and human perception. He’s the recipient of an Alfred P. Sloan Fellowship and a John Simon Guggenheim Fellowship, and he’s also a Fellow of the National Academy of Inventors.

While not everyone is involved in implementing algorithms, we are all making decisions every day. Learning how to receive inputs to create fair outputs is important for all of us.

Are you looking for ways to create greater understanding and belonging throughout your company? Reach out to the Electives team.

The Electives Library of Live Learning is stocked with classes that are inclusive, experiential, impactful and unforgettable, including Data & Race and these classes focused on breaking down barriers and avoiding bias in the workplace:

Below The Surface: Getting to the heart of bias (conscious or unconscious)

Debias Training: Breaking barriers & increasing access

Profound Inclusion: How to avoid rock-star bias and leverage diverse strengths of your team

Diverse Women In Leadership: Reducing bias and structural barriers to optimize potential

Click here to search the Electives Library of Live Learning.

We’ve all heard of unconscious bias and how it can be all too easy to make judgements about someone without having enough relevant information.

The same can be true of predictive computer algorithms. You know the ones: "If you liked X, then you may like Y."

Predictive algorithms are designed to make it easy to match consumers with what they’re likely to buy. The algorithms are designed to create efficiency for both the advertisers and the consumers.

These predictive algorithms, often relying on “lookalike” data, are intended to remove implicit and explicit bias in critical decision making. However, it has been suggested that algorithms often reflect and amplify past racial and gender injustices.


Do you want to receive blog posts like this in your inbox? Click here to subscribe to Electives news.

How do you know if your processes are working for you or against you?

In today’s society, with so much information at our fingertips, it’s important to educate ourselves by asking the right questions and not just trusting the first answer we’re presented with. In fact, the more we educate ourselves on a topic, and the more we stay open to other points of view, the better we can evaluate our own performance, progress and success.

For teams with rich processes or processes that are ready for optimization, it’s important to know how decisions are made – no matter whether they’re made by person or computer. For teams just developing and implementing new processes, now is a great time to understand the pros and cons of algorithms, artificial intelligence and machine learning.

Data & Race: A cautionary tale, with Hany Fanid, is an Electives class that helps teams to learn how to ask the right questions and be open to new ways of thinking. This class looks at whether AI improves or further entrenches racial, gender and identity bias, and evaluates the claim that predictive computer algorithms are more accurate and fair than people tasked with making similar decisions.

Hany also explains the presence of racial bias in predictive algorithms used in the criminal justice system and discusses the broader applications of turning over key decision making to AI systems.

Hany is a professor at the University of California, Berkeley, and his research focuses on digital forensics, forensic science, misinformation, image analysis and human perception. He’s the recipient of an Alfred P. Sloan Fellowship and a John Simon Guggenheim Fellowship, and he’s also a Fellow of the National Academy of Inventors.

While not everyone is involved in implementing algorithms, we are all making decisions every day. Learning how to receive inputs to create fair outputs is important for all of us.

Are you looking for ways to create greater understanding and belonging throughout your company? Reach out to the Electives team.

The Electives Library of Live Learning is stocked with classes that are inclusive, experiential, impactful and unforgettable, including Data & Race and these classes focused on breaking down barriers and avoiding bias in the workplace:
Below The Surface: Getting to the heart of bias (conscious or unconscious)
Debias Training: Breaking barriers & increasing access
Profound Inclusion: How to avoid rock-star bias and leverage diverse strengths of your team
Diverse Women In Leadership: Reducing bias and structural barriers to optimize potential
Click here to search the Electives Library of Live Learning.


Do you want to receive blog posts like this in your inbox? Click here to subscribe to Electives news.

CALL TO ACTION

Predictive algorithms are designed to create efficiency. However, algorithms may often reflect and amplify past racial and gender injustices. 


Asking the right questions to avoid bias and discrimination

Mon Nov 14 2022

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