Generative AI is reshaping the landscape of businesses and society, marking a significant leap from years spent on proof of concepts (POCs) and pushing the boundaries of AI capabilities. Propelled by advanced models and generative AI, applications have matured, and their progression unveils the intricate challenges of current AI solutions, notably issues like data privacy and model hallucination. Navigating this terrain further involves adapting to the evolving regulatory landscape as new laws and guidelines emerge to address the ethical dimensions and potential risks associated with AI advancements. 

What is digital trust and responsible AI? 

While exploring responsible AI and digital trust, businesses may grapple with ensuring fairness, interpretability, and compliance with standards in a dynamic regulatory environment. The focus should be on cultivating responsible AI practices under the umbrella of corporate digital responsibility. Within generative AI models, accuracy coexists with the challenge of being perceived as “black-box” models. Ethical considerations are intricately linked to quality and balanced training datasets, emphasizing the need for ethically sound data practices. Caution is paramount when incorporating third-party datasets to avert biases that could impact a company’s standing. 

Subsequently, governance frameworks emerge as essential tools for sustainably managing AI models and ensuring the cultivation of fair and ethical AI decision-making. We advocate for implementing responsible AI practices as a strategic approach to meeting regulatory requirements, mitigating risks, and fostering trust. Investing in responsible AI is both a legal obligation and a strategic move toward establishing trustworthiness and maintaining a credible and compliant presence. 

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With its 5 dimensions, the BearingPoint approach to responsible AI addresses all aspects required to maximize the value of AI sustainably.

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