Deploying AI Effectively and Responsibly
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Key Takeaways
⇨ Organizations should ensure that their data is accurate and their algorithm is transparent. This will help companies ensure that they are free from bias and can update their AI models quickly and effectively.
⇨ Companies must ensure that they are informed and trained, acting as a final failsafe to keep any AI deployments from displaying bias, using improper or inaccurate data, and following all regulations.
⇨ Companies must be honest and accountable both to external regulators and internal workers. When installing AI, companies should ensure that transparency.
As we covered previously, more and more SAP organizations are turning to Large Language Models (LLMs) to help bolster their operations, particularly customer service. While the promise this technology holds is exciting, there are some potential issues with ethics and privacy that companies need to be aware of.
Businesses are beginning to recognize the need for Responsible AI, which is defined as the “ethical and moral framework that guides the development, deployment, and use of AI systems to ensure they align with human values and societal norms.” The guardrails ensure companies are acting ethically and avoiding risk in their use of AI and LLMs.
“Alongside these innovations comes the growing need for Responsible AI—ensuring that these powerful models are deployed in ways that prioritize ethical considerations, fairness, and transparency. Issues such as bias, privacy, misinformation, and accountability have emerged as critical challenges that must be addressed to mitigate risks and align LLMs development with societal values,” said Warren Norris, Managing Partner of Titan Consulting.
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Responsible AI Guidelines
To help companies deploy their AI solutions responsibly, Titan Consulting highlighted several key tenants of a holistic strategy that ensures ethics in new AI initiatives.
Ethical Principles – Companies that utilize artificial intelligence must ensure that they have specific guidelines outlining how they intend to use AI ethically.
Data Quality – AI models should be trained on high-quality data. Users need to ensure that there is no bias or discrimination, ensuring all scenarios are weighted fairly.
Transparency – One essential piece of the AI responsibility puzzle is that companies need to know what is going on behind the scenes with their algorithm, ensuring all relevant parties can understand how it functions.
Consent and Compliance – With the advent of the age of the AI, there are new regulations following all legal privacy rules. Businesses need to be on top of all applicable rules to avoid inadvertently exposing sensitive data or failing to meet compliance standards.
Monitor and Improve Deployments – AI is a journey, not a destination. Companies need to analyze output and make needed changes as they better understand how AI fits into their organization.
Keep Humans in the Loop – While it is tempting to leave everything to AI, leading businesses make sure there is a human fallback in place to make any necessary corrections or adjustments to AI models. The best validator is a human.
What This Means for SAPinsiders
Data underpins everything. Organizations should ensure that their data is accurate and their algorithm is transparent. This will help companies ensure that they are free from bias and can update their AI models quickly and effectively.
Don’t forget about the human aspect of AI. People must be a key consideration for every AI endeavor. Companies must ensure that they are informed and trained, acting as a final failsafe to keep any AI deployments from displaying bias, using improper or inaccurate data, and following all regulations.
Be accountable internally and externally – Though AI holds significant promise for businesses, it also has some associated risks. Companies must be honest and accountable both to external regulators and internal workers. When installing AI, companies should ensure that transparency is embedded throughout.
