Any organization creating AI models and systems needs to establish a framework that protects an individual’s privacy and security. This article is a roadmap for AI leaders to guide their organizations toward adopting ethical and responsible AI through a holistic AI framework incorporating user and data privacy protections. Companies must take four steps to establish a strong foundation for responsible AI governance, address AI privacy concerns, and apply Privacy by Design best practices.
Firstly, they need to assess and minimize potential risks associated with AI. Secondly, they should create a roadmap for responsibly implementing AI. Thirdly, companies must define their principles of responsible AI, which will serve as their north star. Lastly, companies must stay up-to-date with the latest regulations. Companies can create an actionable roadmap for responsible AI by following these steps. To set their North Star architecture with responsible AI guardrails and governance, companies need to take four key steps:
- Assess and mitigate risks
- Set the roadmap for responsibly implementing AI
- Define your responsible AI principles in your North Star
- Keep up with regulations
To maximize business value while minimizing associated risks when implementing AI, it is essential to establish proper guardrails and governance mechanisms to incorporate AI risk management to ensure AI systems are secure and respect privacy. Companies can avoid negative consequences such as regulatory penalties and reputational damage by following the four steps and setting an actionable roadmap for responsibly implementing AI. Assessing and mitigating risks such as privacy, security, alignment, bias, fairness, accuracy, and accountability is imperative to achieve responsible AI.
Privacy is the Potential Risk Associated with AI that Companies Need to Assess and Minimize
The new Generative AI era is exciting but brings new privacy concerns. When consuming generative AI solutions, applications like Chat GPT, DALL-E, Stable Diffusion, Midjourney, Codex, or Bard have been capturing the imagination of both individual users and businesses. These aren’t just high-tech tools for the big players anymore. They are user-friendly, adaptable, and accessible solutions.
While democratizing AI, these solutions can come with significant security and data privacy risks. Security is critical since data used by AI systems are a treasure trove for hackers, and protecting against misuse of personal data is non-negotiable. Failure to adequately protect sensitive data jeopardizes individual privacy and can destroy long-built trust in seconds. It can have severe implications for businesses’ reputational damage and legal consequences.
In the digital age, the public’s perception of AI’s role in privacy is far from rosy. A Pew Research Center study shed light on three main growing concerns Americans have about AI. It is job loss, security and privacy, and lack of human connection. A striking 53% of Americans believe that AI is more of a hindrance than a help when safeguarding personal information. This isn’t a new sentiment; previous studies have consistently shown that most Americans are uneasy about online privacy and feel they’ve lost control over their personal data. For example, AI-driven facial recognition technology has been criticized for its potential misuse in mass surveillance. Addressing these privacy concerns and maintaining public trust is crucial as AI gets more integrated across various aspects of our lives.
When users or companies consume Generative AI models, they have essentially handed over proprietary data to an external platform, which could lead to data breaches, misuse, or unauthorized access. Samsung employees accidentally leaked sensitive corporate data and source code via Chat GPT, and there was no way to unlearn it. This risk is particularly heightened in sectors that frequently handle sensitive data since this practice could result in regulatory compliance issues, especially in industries with stringent data protection laws, such as healthcare, finance, or telecommunications. Understanding these risks is crucial before opting for different AI solutions to ensure robust AI governance.
How will Your AI System Protect and Manage Privacy?
It has been believed that only a few big companies have the resources to build custom models from scratch. Training Chat GPT 3 costs around 4 million dollars, and Chat GPT 4 around 100 million dollars. It requires significant time and expertise. Companies must have AI talent and infrastructure to build and train the model from the ground up. Creating custom models is expensive, and there has been a perception that only a few large models will rule them all.
However, some enterprises are interested in building their models on their data within their secure environments to drive the business value on top of their enterprise data. They want to use generative AI to tap into structured data currently used for insights and train their models on unstructured data that accounts for almost 80% of enterprise data. The value they extract from Generative AI in generating, summarizing, or translating content, understanding sentiment, clarifying documents, or answering questions brings impressive productivity gains.
Enacting strong data privacy regulations and enforcing comprehensive policies that safeguard personal information and uphold individuals’ rights to determine how their data is utilized is key for regulated industries such as financial or health care. You need to know what data was used for training the models, be able to explain how the output has been generated, and be able to monitor it throughout the cycle. The models can also be more cost-effective for specific use cases where smaller specialized models work best. You need to develop your capabilities based on your unique business needs and learn how to protect data by preserving privacy and ensuring confidentiality at the core of your business.
Is Your AI System Using Data Securely?
Privacy and security associated with a company’s proprietary data are critical to compete and innovate. So, companies are turning to ways of using new capabilities of foundational models in their secure environment to supercharge their capabilities based on their domain proprietary data. There are promising examples of accessible training of large language models, such as Replit, which utilized MosaisML platform tools and succeeded with only two engineers to train and an open-source code completion model for less than $ 100,000.
One noteworthy financial industry example is the Bloomberg GPT 50 billion parameter language model specifically designed for the financial domain. While Natural Language Processing (NLP) has been widely used in financial technology for various applications, a Large Language Model (LLM) has only recently been tailored for this sector. Bloomberg GPT has been trained on a massive (363 billion token) dataset derived from Bloomberg’s extensive data sources, supplemented with (345 billion tokens from) general-purpose datasets. It significantly outperforms other general models in financial tasks from sentiment analysis, named entity recognition, and suggestion of news headlines to financial question answering without compromising on general LLM benchmarks.
McKinsey recently introduced “Lilli,” a generative AI tool that primarily aims to assist consultants in their work by quickly providing accurate and insightful information, improving the quality of expertise. Lilli is designed to generate written responses, analyses, and recommendations in a coherent and natural language format. Its capabilities extend to various industries and sectors, from pharmaceutical to manufacturing, making it a versatile tool for accessing expert knowledge and problem-solving. Automating specific tasks and providing examples of successful past solutions in particular domains enhances consultants’ efficiency and improves client interactions and project outcomes. Overall, Lilli exemplifies how AI is becoming integral to the consulting landscape, augmenting human expertise and enabling more informed decision-making.
In conclusion, responsible AI governance ensures that artificial intelligence is used ethically, safely, and responsibly. It involves implementing a roadmap for responsible AI that addresses the potential risks and negative impacts of AI while also promoting its benefits and potential. By Incorporating AI risk management and responsible AI governance, we can ensure that AI systems will be secure and respect privacy. That AI is developed and used fairly, transparently, and with accountability, safeguarding privacy, safety, and security. This will not only benefit individuals and society as a whole but also help us to build trust.
