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Every Single Cognitive Bias in One Infographic

@machinelearnbot

The human brain is capable of incredible things, but it's also extremely flawed at times. Science has shown that we tend to make all sorts of mental mistakes, called "cognitive biases", that can affect both our thinking and actions. These biases can lead to us extrapolating information from the wrong sources, seeking to confirm existing beliefs, or failing to remember events the way they actually happened! To be sure, this is all part of being human – but such cognitive biases can also have a profound effect on our endeavors, investments, and life in general. For this reason, today's infographic from DesignHacks.co is particularly handy.


AI Bias Adds Complexity To AI Systems

#artificialintelligence

One of the biggest issues with Artificial Intelligence and Data Science is the integrity of our data. Even if we did all the right things in our models, and our testing, data might conform to some technical standard of "cleanliness," there might still be biases in our data as well as "common sense" issues. With Big Data, it is difficult to get to a certain granularity of data validity without proper real-world testing. By real-world testing, we mean that when data is being used to make decisions, as consumers, as testers, as programmers, as data scientists, we look at groups of scenarios to see if the decisions made conform to a kind of "common sense" standard. This is when we discover the most important biases in our data.


AI Bias Adds Complexity To AI Systems

#artificialintelligence

One of the biggest issues with Artificial Intelligence and Data Science is the integrity of our data. Even if we did all the right things in our models, and our testing, data might conform to some technical standard of "cleanliness", there might still be biases in our data as well as "common sense" issues. With Big Data, it is difficult to get to a certain granularity of data validity without proper real-world testing. By real-world testing, we mean that when data is being used to make decisions, as consumers, as testers, as programmers, as data scientists, we look at groups of scenarios to see if the decisions made conform to a kind of "common sense" standard. This is when we discover the most important biases in our data.


AI Bias Adds Complexity To AI Systems

#artificialintelligence

One of the biggest issues with Artificial Intelligence and Data Science is the integrity of our data. Even if we did all the right things in our models, and our testing and data might conform to a technical standard of "cleanliness," there might still be biases as well as "common sense" issues" that may come up. With Big Data, it is difficult to get to a certain granularity of data validity without proper, real-world testing. By real-world testing, we mean that when data is being used to make decisions, as consumers, testers, programmers, and data scientists, we look at groups of scenarios to see if the decisions are made to conform to a standard of "common sense". This means when we discover the most important biases in our data.


Council Post: Human Cognitive Bias And Its Role In AI

#artificialintelligence

Daniel Fallmann is Founder and CEO of Mindbreeze, a leader in enterprise search, applied artificial intelligence and knowledge management. When faced with a challenge, human beings are generally quick to first try to develop creative solutions. We tend to pick the most logical explanation we can find, ignoring all contradictory or unprovable hypotheses in the process. However, this irrational pattern of thinking could eventually sabotage our efforts to create an actual intelligent machine. A cognitive bias known as rationalization is one such phenomenon that is tricky or even dangerous for AI.