PCMARK 10 INTEL BIAS HOW TO

Talman, A., Apidianaki, M., Chatzikyriakidis, S., Tiedemann, J.: NLI Data Sanity Check: Assessing the Effect of Data Corruption on Model Performance. вЂњThe Effects of Data Quality on Machine Learning Algorithms.вЂќ, Conference: Proceedings of the 11th International Conference on Information Quality, MIT, Cambridge, MA, USA, 10вЂ“12 November, 2006

PCMARK 10 INTEL BIAS HOW TO

This step allows the data scientists to make informed decisions on how to process such data to avoid several biases even before the actual training of the machine learning models begin.

With the help of said bias indices, a data scientist using such data can use such indices to determine whether there are biases existing in the datasets being using to train the machine learning models. Explainable AI and natural language processing techniques are applied to determine and quantify bias in the form of bias index in textual data. Once this context has been defined, the paper proposes a list of approaches to understand the causes and effects of such biases in data. Next, the paper maps the underlying causes of such biases that get trickled to artificial intelligence systems through the forms of data and how they impact decision making of the end-users. First, this paper provides references to the kinds of biases that exist in the world today, and how they impact how decisions are made in the real-world. This paper proposes the use of explainable AI features to quantify data bias, which can assist in better decision-making. With the ever-growing usage of artificial intelligence in business decisions, organizations, and the day-to-day lives of the society at large, it is imperative that different forms of biases that human beings are privy to do not get trickled to the machine learning and artificial intelligence systems being developed and used in abundance.

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