Generative artificial intelligence (AI) has become widely popular, but its adoption by businesses comes with a degree of ethical risk. Organizations must prioritize the responsible use of generative AI by ensuring it is accurate, safe, honest, empowering, and sustainable. Organizations need to be mindful of the ethical implications and take necessary steps to reduce risks. Specifically, they need to: use zero or first party data, keep data fresh and well labeled, ensure there’s a human in the loop, test and re-test, and get feedback.


Copyright: – “Managing the Risks of Generative AI”


Corporate leaders, academics, policymakers, and countless others are looking for ways to harness generative AI technology, which has the potential to transform the way we learn, work, and more. In business, generative AI has the potential to transform the way companies interact with customers and drive business growth. New research shows 67% of senior IT leaders are prioritizing generative AI for their business within the next 18 months, with one-third (33%) naming it as a top priority. Companies are exploring how it could impact every part of the business, including sales, customer service, marketing, commerce, IT, legal, HR, and others.

However, senior IT leaders need a trusted, data-secure way for their employees to use these technologies. Seventy-nine-percent of senior IT leaders reported concerns that these technologies bring the potential for security risks, and another 73% are concerned about biased outcomes. More broadly, organizations must recognize the need to ensure the ethical, transparent, and responsible use of these technologies.

A business using generative AI technology in an enterprise setting is different from consumers using it for private, individual use. Businesses need to adhere to regulations relevant to their respective industries (think: healthcare), and there’s a minefield of legal, financial, and ethical implications if the content generated is inaccurate, inaccessible, or offensive. For example, the risk of harm when an generative AI chatbot gives incorrect steps for cooking a recipe is much lower than when giving a field service worker instructions for repairing a piece of heavy machinery. If not designed and deployed with clear ethical guidelines, generative AI can have unintended consequences and potentially cause real harm. 

Organizations need a clear and actionable framework for how to use generative AI and to align their generative AI goals with their businesses’ “jobs to be done,” including how generative AI will impact sales, marketing, commerce, service, and IT jobs.

In 2019, we published our trusted AI principles (transparency, fairness, responsibility, accountability, and reliability), meant to guide the development of ethical AI tools. These can apply to any organization investing in AI. But these principles only go so far if organizations lack an ethical AI practice to operationalize them into the development and adoption of AI technology. A mature ethical AI practice operationalizes its principles or values through responsible product development and deployment — uniting disciplines such as product management, data science, engineering, privacy, legal, user research, design, and accessibility — to mitigate the potential harms and maximize the social benefits of AI. There are models for how organizations can start, mature, and expand these practices, which provide clear roadmaps for how to build the infrastructure for ethical AI development.

But with the mainstream emergence — and accessibility — of generative AI, we recognized that organizations needed guidelines specific to the risks this specific technology presents. These guidelines don’t replace our principles, but instead act as a North Star for how they can be operationalized and put into practice as businesses develop products and services that use this new technology.

Guidelines for the ethical development of generative AI

Our new set of guidelines can help organizations evaluate generative AI’s risks and considerations as these tools gain mainstream adoption. They cover five focus areas.[…]

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