AI bias will explode. But only the unbiased AI will survive.

Within five years, the number of biased AI systems and algorithms will increase. But we will deal with them accordingly – coming up with new solutions to control bias in AI and champion AI systems free of it.

 

AI bias will explode. But only the unbiased AI will survive.

Within five years, the number of biased AI systems and algorithms will increase. But we will deal with them accordingly – coming up with new solutions to control bias in AI and champion AI systems free of it

 

Many AI systems are trained using biased data

AI systems are only as good as the data we put into them. Bad data can contain implicit racial, gender, or ideological biases. Many AI systems will continue to be trained using bad data, making this an ongoing problem. But we believe that bias can be tamed and that the AI systems that will tackle bias will be the most successful.

A crucial principle, for both humans and machines, is to avoid bias and therefore prevent discrimination. Bias in AI system mainly occurs in the data or in the algorithmic model. As we work to develop AI systems we can trust, it’s critical to develop and train these systems with data that is unbiased and to develop algorithms that can be easily explained.

Challenges facing us today

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More than 180 human biases have been defined and classified, and any one of which can affect how we make decisions.

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Biases find their way into the AI systems we design, and are used to make decisions by many, from governments to businesses.

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Bad data used to train AI can contain implicit racial, gender, or ideological biases.

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Bias in AI systems could erode trust between humans and machines that learn.

Live presentation

IBM researcher Francesca Rossi discusses AI bias and its importance in building systems that make decisions or guide humans to making better decisions.

 

Live presentation

IBM researcher Francesca Rossi discusses AI bias and its importance in building systems that make decisions or guide humans to making better decisions.

 


Mitigating human bias in AI

As humans and AI increasingly work together to make decisions, researchers are looking at ways to ensure human bias does not affect the data or algorithms used to inform those decisions. 

The MIT-IBM Watson AI Lab’s efforts on shared prosperity are drawing on recent advances in AI and computational cognitive modeling, such as contractual approaches to ethics, to describe principles that people use in decision-making and determine how human minds apply them. The goal is to build machines that apply certain human values and principles in decision-making. IBM scientists also devised an independent bias rating system can determine the fairness of an AI system.

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Identifying and mitigating bias in AI systems is essential to building trust between humans and machines that learn. As AI systems find, understand, and point out human inconsistencies in decision making, they could also reveal ways in which we are partial, parochial, and cognitively biased, leading us to adopt more impartial or egalitarian views. In the process of recognizing our bias and teaching machines about our common values, we may improve more than AI. We might just improve ourselves.

Reducing Unfair Discrimination in AI

IBM researchers developed a methodology to reduce the bias that may be present in a training dataset, such that any AI algorithm that later learns from that dataset will perpetuate as little inequity as possible.

 

Reducing Unfair Discrimination in AI

IBM researchers developed a methodology to reduce the bias that may be present in a training dataset, such that any AI algorithm that later learns from that dataset will perpetuate as little inequity as possible.

 

Reducing Bias in AI

Visit this online experience to learn how models are deciding the future of US defendants.

 

Reducing Bias in AI

Visit this online experience to learn how models are deciding the future of US defendants.

 

Illustration of AI neural network learning from data

Predictions

IBM 5 in 5 predictions