We are witnessing an era of unprecedented technological advancement, where Conversational AI in general, and large-scale language models in particular, are becoming increasingly prominent. These models, based on transformer architectures, are powerful tools that have the potential to radically change the way we communicate, create, and learn. However, as the power and complexity of these models increase, we must be aware of the necessity to use them in a Secure and Responsible manner.
At AI-ris, Secure and Responsible use of Conversational AI are front and centre to everything we do. We distinguish ourselves from our competitors by prioritizing these values throughout the entire software development, implementation, and maintenance process. From the choice of the models we run, to the way we handle data and the locations where our processes run.
With AI-ris, you're assured a partner that not only intensly collaborates with you concerning how to use Conversational AI Securily and Responsibly, but who also proactively maps potential risks, proposes mitigation measures, and ensures that the execution of these measures is thoroughly implemented. AI-ris constantly strives to stay one step ahead in terms of Secure and Responsible AI use, by focusing on the latest research and standards in our field. We believe that building strong, Secure, and Responsible AI systems is not just a necessity, but also an obligation towards our customers and society. With AI-ris by your side, you can trust that you're harnessing the power of generative AI, while maintaining a safe, ethical, and socially responsible approach.
Safety First
Safety is a critical aspect in the development and implementation of Conversational AI. These models are capable of generating content that feels impressively human, which means they can potentially be misused to disseminate false information or manipulative messages. To prevent this, it's important to implement safety measures.
One of these is monitoring the model's output. Automated monitoring mechanisms can be used to detect when the model is possibly generating harmful or misleading information. Also, measures can be taken to prevent the AI from revealing sensitive information, such as personal data.
In addition, it can be beneficial to restrict or regulate access to such models. This could mean that the model is only available to authorized users, or that there are stricter restrictions on what the model can do.
Responsible Use
The responsible use of large-scale language models goes beyond safety alone. We also need to consider the wider societal implications of this technology. It's important to think about ways to help people adapt their skills and prepare for a changing job market.
Also, we should consider the potential consequences of bias in AI. Large-scale language models are trained on vast amounts of text from the internet, which means they can learn and reinforce the existing biases in these texts. It's crucial to develop ways to detect and reduce this bias.
AI-ris employs multiple techniques and methodologies that are unique to our product offering and enable us to offer our clients Conversational AI in the safest and most responsible way possible.
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Avoiding hallucinations By training Large Language Models (LLMs) on our own dataset(s) and limiting the question settings to this, hallucinations can be almost completely avoided (*depending on the data quality of the source).
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Anonimization of user data By anonymizing input data (locally), we can prevent personal data or other sensitive business information from being shared with third parties.
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Specification of target group By specifying the tooling settings to its target audience, we can prevent the generation of answers that do not correspond to the information needs of the customer and/or target group.
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Insight & manageability Through user-friendly monitoring of the use of the tooling, misuse can be prevented and its use can remain insightful and manageable. |
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Comprehensive testing
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Local models By running local models at critical stages in the process (e.g., personal data, sensitive business data), legal requirements regarding data protection can be met.
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Change management By focusing on understanding, training, and creating support within the organization, misuse of the tooling can be minimized, and the workforce can be deployed more flexibly.
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Proactive maintenance By applying proactive maintenance, regular updates and extensive testing before any release the stability, reliability and consistancy of the existing processes are ensured and enhanced. |
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