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Hur upptäcker och eliminerar man bias i naturlig språkbehandling (NLP)?


How to Detect and Eradicate Bias in Natural Language Processing (NLP)?

Natural Language Processing (NLP) is one of the main components of artificial intelligence (AI) and machine learning (ML).

Like those two, NLP-powered systems also reinforce certain biases primarily because of the data used for machine learning. The element of bias in NLP systems can be reduced if organizations and AI experts work in coordination with one another for the same.

Natural Language Processing (NLP) systems are highly useful in our day-to-day lives as well as digitized and automated business operations. NLP, a powerful subfield of AI, computer science and linguistics, can be applied in fields as disparate as supply chain management och turism.

Despite its vast number of use cases, NLP, like AI, has tendencies to reinforce and exploit certain biases present in the underlying data used for NLP-based systems. Even powerful NLP tools such as the mighty GPT-3 show this problem. To understand this better, suppose an NLP tool is used as a resume filtering system in a recruitment campaign conducted by an organization. Biased NLP may exclude kvinnor and candidates of color, and only select a certain type of individual (white male, in this example) for employment. Recruitment-related biases, in either NLP or otherwise, can be disastrous for organizations.

As we know, biases are mostly prevalent due to a lack of training datasets and, more importantly, in the case of NLP, because social biases are ever-present in the languages we speak and write. As a result, such biases can surreptitiously enter NLP systems too. To detect and reduce the element of bias in NLP systems, the following ideas can be used:

1. Implement Audits to Track Biases

AI experts need to carry out audits to discover biases—and their magnitude—in data generated from NLP systems. For example, such audits can be highly useful for understanding the underlying biases that social media users show in their posts. Additionally, such audits can also allow concerned officials in public or private agencies to know about text related to racism or other marginalization-fueled speeches on a public platform.

2. Establish AI Model Training Standards

As stated earlier, biases in NLP are mainly caused by the kinds of data that are used in model training. As a result, organizations need to monitor the datasets that are being used for the purpose of NLP model training. If the bias-related elements are removed from AI or NLP models, then the overall bias in NLP systems will also reduce drastically.

Apart from these, data analysts can also use data security assessments to find if NLP datasets are trained with authentic and valid natural language data and not other, possibly contaminated data. Eliminating bias in NLP systems and models is a seemingly impossible task. However, by adopting intelligent data governance and quality control during the early phases of AI and NLP implementation, it can be reduced up to a great extent.



Den tråkiga teknikförvandlingen


The Boring Technology Transformation

Is the word disruption over-used or overrated? Paul Saunders thinks so.

Paul is head of product strategy and chief evangelist on S/4HANA at SAP. Prior to SAP, Paul was a CIO at multiple manufacturing businesses, and an executive at a university and Gardner research. With a strong technology, manufacturing and global background, Paul offers unique and profound perspectives.

With all the supply chain, geopolitical and pandemic disruptions coming together, the smart way of doing business has not changed. It is about flexibility, agility, process, and mindset. Business, people, and technology often move at different rates. Still technology cannot be perceived as “long after I need it, and for way more money than I’m willing to pay”.

There are exciting technologies like metaverse and blockchain, but business flow does not change day to day from procuring supplies, providing goods and services, and getting paid. The goal of SAP cloud ERP transformation is to simplify and standardize processes that do not require differentiation, to reduce variability, and to drive operational efficiency. As companies expand their network of intelligent business enterprise, they can focus more on creating true differentiation to win the future. Paul’s message on cloud ERP transformation resonates with me.

I agree with Paul that technology is a journey not a destination. When asked about the future workforce, here is his prediction. People are not expected to have the same job for 20 years and retire with a clock. A person will be more likely enjoying a portfolio of careers. That is insightful and exciting.

For those who are interested in hearing more from Paul and on “how to outsmart inflation”, find out more here


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