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 and 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 women 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.
SaaS pricing inflation growing 4x faster than market inflation
Inflation has dominated the financial news landscape in 2022. In many markets, the consumer price index (CPI), has reached its highest point in a generation. This growth in the cost of ‘things’ also applies to software.
Almost every organisation has come to rely on SaaS to conduct business, from communications tools like Slack and Zoom to productivity suites like Microsoft 365 and Google Workspace, as well as department-specific platforms like Atlassian, Workday, NetSuite or Salesforce.
This is according to a report into SaaS inflation pricing from Vertice, a SaaS purchasing and spend management platform.
Spending on SaaS products grew more than tenfold between 2010 and 2020, from $13b to $157b annually. Investment accelerated even faster at the onset of the coronavirus pandemic, as companies raced to support remote working. SaaS spending increased by 26% in the months following the initial lockdown in 2020 and has only continued to grow in the years since.
Unlike many other significant overheads, like payroll and rent, the selection, management and renewal of SaaS are decentralised in nearly every organisation. This is for a variety of reasons, but buying power plays the most important role. Buying power typically sits across several individuals and departments, with finance leaders managing budget requirements, IT teams assessing systems and compliance considerations, and department heads selecting based on functionality. It’s a complex web of decision making and, even with the best intentions, it can be a struggle to gain a single view of all of the SaaS products a company uses.
This ‘wild west’ of a cost centre is a significant problem when the share of the total cost is considered. A growing percentage of all expenditures for businesses goes to SaaS, with around 12.7% of total spending now used on software investments. That means $1 in every $8 that modern organisations spend is now dedicated to SaaS. To translate that into dollars — as of 2022, companies spend around $3,112 per employee each year on SaaS. This figure rises to $4,552 for technology companies, who spend more than firms in any other category.
It has taken only five years for average SaaS spending to double. Based on the economic inflation rate over the same period, it would take 18 years for the cost of SaaS to double. This growth has far outpaced the rate of general economic inflation, even after factoring in recent periods of an uncharacteristically high CPI.
Clearly, the impact of SaaS in terms of productivity, collaboration and inclusion has been significant – but the accompanying cost has also been quietly spiralling upwards.
Analysis of more than 10,000 SaaS contracts shows that 74% of vendors have increased their list pricing since 2019. Among the quarter of vendors that have not, almost all have reduced the size of the average discount afforded to customers – effectively raising the spend without touching the list price.
A comparison of regional inflation rates with the SaaS inflation rate by geography reveals that over the past five years the cost of SaaS for US organisations has grown 3.5x faster than the general inflation rate – even after accounting for an exceptionally high national inflation rate in 2022.
SaaS inflation has outstripped general inflation rates even more rapidly elsewhere; spending at British and Australian firms has risen at a rate five times greater than regional economic inflation.
Joel Windels, VP of marketing at Vertice, said: “It’s become clear that not only is SaaS critical to modern businesses, but also that it represents a growing cost centre that can rapidly spiral out of control without strategic management. Even without investing in new tools or added licences, the data shows that spending on SaaS is exploding. With an uncertain economic outlook for 2023, finance leaders absolutely have to start taking a more considered approach to SaaS spending if they are to maintain growth and streamline their operations”