Mass Adoption of Generative AI Tools Disrupts Vital Factor
AI is getting really advanced, and that’s causing some problems. A study from MIT Sloan Management Review and BCG says it’s making it tough to use AI responsibly because of something called “shadow AI.”
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Every day, new AI platforms and tools come out to help developers, data scientists, and business analysts. But all this rapid growth in technology is making AI systems really complex, and it’s hard to keep them responsible and accountable.
A recent survey of 1,240 executives conducted by MIT Sloan Management Review and Boston Consulting Group (MIT SMR and BCG) has highlighted some concerning findings.
They looked at how companies are dealing with responsible AI initiatives and the use of AI tools, both built in-house and from external sources, which they call “shadow AI.”
The authors of the study, Elizabeth Renieris (from Oxford’s Institute for Ethics in AI), David Kiron (from MIT SMR), and Steven Mills (from BCG), suggest that while AI offers many benefits, it also comes with consequences.
They point out that generative AI, which can create text, images, and audio, has proven to be challenging and can pose unpredictable risks for organisations that aren’t prepared for its various uses.
Many companies were taken by surprise when they found that shadow AI was being used throughout their organisations. The rapid advancement of AI is making it difficult to use AI responsibly and is putting pressure on responsible AI programs to keep up.
The authors also caution that the risks associated with the growing prevalence of shadow AI are on the rise. As companies increasingly rely on third-party AI tools and rapidly adopt generative AI, they are exposed to new commercial, legal, and reputational risks that are hard to monitor and manage.
The researchers emphasise the importance of responsible AI, which they define as a framework consisting of principles, policies, tools, and processes aimed at ensuring that AI systems are developed and operated for the benefit of individuals and society while still delivering significant business outcomes.
Another challenge arises from the fact that some companies seem to be reducing their internal resources dedicated to responsible AI, possibly due to industry-wide layoffs. The researchers warn that these cutbacks in responsible AI investments are occurring at a time when they are most needed.
For instance, the widespread use of the ChatGPT chatbot by employees has taken many organisations by surprise and could have security implications.
According to the researchers, responsible AI frameworks have not been prepared to address the sudden and unforeseen array of risks introduced by generative AI tools.
The research indicates that 78% of organisations report using third-party AI tools, such as commercial APIs, pretrained models, and data, either by accessing, buying, licensing, or other means.
More than half (53%) rely exclusively on third-party AI tools and do not have AI technologies developed in-house.
The researchers stress that responsible AI programs should encompass both internally developed and third-party AI tools. They argue that the same ethical principles should apply regardless of the source of the AI system because, in the end, it wouldn’t matter to someone negatively affected whether the tool was built in-house or purchased.
The co-authors advise that while there is no one-size-fits-all solution for managing the risks associated with third-party AI, or AI risks in general, they recommend adopting a multifaceted approach to ensure responsible AI in today’s complex landscape. Some of these approaches could include:
Vendor Assessment: Evaluating a vendor’s responsible AI practices to ensure they align with your organisation’s ethical standards.
Contractual Obligations: Incorporating specific contractual language that requires vendors to adhere responsible principles in their products & services.
Vendor Certification & Audits: Whenever possible, opting for vendors who have received pre-certification or conducting audits to verify their compliance with responsible AI guidelines.
Internal Product Review: Conducting in-house evaluations, especially when integrating third-party AI tools into your own products or services, to ensure they meet your ethical standards and requirements.
Compliance with Regulations and Standards: Ensuring that your AI initiatives adhere to relevant regulatory requirements and industry standards to mitigate risks and liabilities.
Comprehensive Policies and Procedures: Developing a comprehensive set of policies and procedures that encompass ethical AI development, risk assessment frameworks, and monitoring and auditing protocols.
The co-authors also point out that the potential for government regulations and mandates in the field of AI could make these actions a necessity as AI systems continue to be introduced and adopted.
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