Tackling Bias in AI: Organisational  Shortcomings

Organizations are enthused about Gen AI’s potential for enhancing productivity, but according to SAS research, data protection fears remain.

The rapid emergence of generative artificial intelligence (Gen AI) models such as ChatGPT has created fascinating new opportunities for companies across fields. These strong language models may help with a broad range of jobs, including content development, coding, and customer support. However, the fast adoption of Gen AI is exposing organizations to major data privacy vulnerabilities that need to be addressed immediately.

One major worry is the training data used to build these AI systems. Large language models consume huge amounts of online material, such as websites, books, papers, and social media postings, many of which may contain personal data, intellectual property, and other sensitive information. Businesses that use these models must deal with the significant legal and ethical issues of using data that was not intended for this purpose.

Every day, the world generates five exabytes of information. By 2025, this is expected to reach 463 exabytes per day, thanks in part to greater usage of Gen AI. However, as organizations continue to embrace AI at a rapid speed, they are growing concerned about how the technology may harm their important data. 

SAS Innovate study revealed that 80% of CEOs are worried about data privacy and security, with company leaders admitting to a lack of governance structures.

According to the survey, US organizations are enthused about Gen AI’s potential to increase company and employee efficiency. However, behind the current excitement, leaders see understanding gaps, a lack of strategic planning, and a skill shortage as impediments to achieving and quantifying the technology’s full worth.

Organizations Possess Challenges in Using Gen AI

Organizations face many major challenges as they attempt to use Gen AI. To begin, they are aiming to build confidence in their data utilization while still achieving compliance. According to the SAS report, only one in every ten organizations has a reliable system in place to measure bias and privacy risk in large language models (LLMs), and an astonishing 93% of US businesses lack a comprehensive governance framework for Gen AI, putting the majority at risk of non-compliance with emerging regulations.

Second, businesses face compatibility challenges when attempting to integrate Gen AI into their existing systems and procedures. The smooth integration of these new technologies with existing infrastructure remains a major challenge.

A third problem is talent and abilities. Organizations are discovering a significant shortage of in-house GenAI knowledge, as HR departments struggle to identify qualified personnel. Organizational executives are concerned that they lack the essential capabilities to properly leverage their Gen AI investments.

Finally, estimating the expenses of adopting LLMs has proven to be a huge difficulty. While model makers give preliminary cost estimates, leaders report exorbitant direct and indirect costs associated with private knowledge preparation, training, and model operations management. The exact financial ramifications of GenAI deployment are complicated and sometimes underestimated.

“Organisations are realising that large language models alone do not solve business challenges,” stated Marinela Profi, SAS’ Strategic AI Advisor. Gen AI should be viewed as an excellent contributor to hyper-automation and the acceleration of current processes and systems, rather than a new shiny toy that will help organizations realize all of their business goals. Developing a progressive policy and investing in technology that provides integration, governance, and explainability of LLMs are critical steps that all businesses should take before leaping in with both feet and being ‘locked in.’

“It will come down to discovering real-world use cases that provide the most value and meet human needs in a sustainable and scalable way. We’re continuing our commitment to assisting organizations in staying relevant, investing intelligently, and being resilient with this research. In an era where AI technology is evolving virtually daily, competitive advantage is heavily reliant on the capacity to accept resilience rules.”

Source- technologymagzine