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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 tourism.

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.



Augmented Reality Can Prevent Blindspot-Based Vehicular Mishaps On Busy Motorways


Augmented Reality Can Prevent Blindspot-Based Vehicular Mishaps On Busy Motorways

Augmented Reality Can Prevent Blindspot-Based Vehicular Mishaps On Busy Motorways

The involvement of augmented reality (AR) in smart cities and the safety systems of modern vehicles promises to resolve a frequent cause of vehicular accidents—blindspots.

In automotive speak, blindspots are defined as the external spaces that the driver of a vehicle cannot see while driving. External visibility is generally poor for drivers because, apart from the zones visible through the glass areas and the ones reflected by the rear-view and wing mirrors in vehicles, they cannot see much else outside. This may seem like an insignificant problem at first, but dive deeper into the numbers, and you’ll find that about 840,000 crashes and 300 deaths occur every year due to blindspot-related accidents. 

The implementation of AR in smart cities and modern vehicles can be seen as a remedy for this issue. Recently, AR and VR-driven simulations have steadily emerged in vehicular safety testing. So, if vehicle manufacturers need to resolve the blindspot problem, they can look towards a few promising AR-driven concepts and solutions.


Heads-Up Windshield Display

Several vehicles manufactured by companies such as BMW, Bentley, Audi and many others have AR-based heads-up windshield systems that allows drivers to focus on the road ahead instead of being distracted by information on their vehicle’s dashboard infotainment systems. Heads-up display systems involve road signage as well as pedestrian and vehicle proximity data being displayed on the windshield of the vehicle. As you know, the possibilities are endless when it comes to AR, safety and visibility tools. So, manufacturers can configure such systems to project the visuals of pedestrians and other vehicles approaching or standing in blindspot areas of their vehicles (regardless of whether they’re static or in motion). This will let drivers see a simulation of pedestrians or vehicles in blindspots on the windshield of their vehicles, avoiding several accidents in the process.

See-Through Pillars and Panels

This is a slightly more unrealistic way of using AR for blindspot elimination. According to this concept, AR can be used to project the simulated visuals of a vehicle’s external environment on its pillars and other internal panels, vastly improving external visibility for drivers. Several concepts similar to this are currently in various stages of development. The success of this idea will greatly enhance the safety aspect of vehicles of the future.


On a basic level, blindspots are a result of the anatomy of vehicles today and will cease to exist if future vehicles are built differently. Until then, the use of AR in smart cities and connected modern vehicles can help reduce the number of accidents caused by blindspots.



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