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The business logic in debiasing

@machinelearnbot

Debiasing business decision making has drawn board-level attention, as companies doing it are achieving marked performance improvements.


How to address and prevent machine bias in AI Logikk

#artificialintelligence

Machine bias is when a machine learning process makes erroneous assumptions due to the limitations of a data set. Data sets can create machine bias when human interpretation and cognitive assessment may have influenced it, thereby the data set can reflect human biases. The data set may also create machine learning bias if there are problems related to the collection and quality of the data leading to improper conclusions being made during the machine learning process. In this article, I discuss what machine bias looks like and how we can go about preventing and mitigating these biases. In the past few years, words like machine learning and artificial intelligence have become ubiquitous in the media.


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.