The breakneck speed with which Artificial intelligence (AI) is transforming our world has been nothing short of exciting. A report by KPMG Global Tech highlights AI and machine learning as the most crucial technologies for achieving short-term goals for tech leaders today.
However, similar to any emerging technology, inherent risks exist. One survey, encompassing over 17,000 individuals, found 61% of respondents expressed apprehension about trusting AI systems, with only half convinced that the benefits outweigh the potential risks. A major concern revolves around personal data privacy.
AI systems are data-hungry beasts. To learn and function effectively, they require massive datasets to train their algorithms and fuel performance. This data often includes personal information like names, addresses, financial details, and even sensitive data like medical records and social security numbers. The collection, processing, and storage of such data raise significant questions regarding its usage and vulnerability.
The Road to Data Privacy
The widespread and unregulated use of AI poses a significant threat to human rights and personal privacy. For example, generative AI (GenAI) uses powerful foundation models trained on massive volumes of unlabeled data— which may or may not take personal data privacy into account.
That’s one of the reasons why AI leaders have issued open letters advocating for a temporary pause in GenAI development. They urge policymakers to establish “guardrails” to ensure their responsible use in the future. In response, there have been increased government efforts to ensure that AI is not being used indiscriminately. For example, the EU’s GDPR (passed in 2018) and the AI Act are two separate legislations aiming to regulate how AI is being used to gather data within the European Union.
The reach of AI governance extends beyond developers and AI pioneers. Companies that integrate AI products and services into their operations hold significant responsibilities. These companies must prioritize ethical considerations when selecting and using AI tools. For instance, ensuring AI doesn’t perpetuate biases present in training data is critical. Additionally, companies must comply with relevant regulations, such as the EU AI Act if they operate within the European market.
As we navigate the complex intercourse between AI innovation and personal data privacy, it is incumbent upon all stakeholders— business leaders, policymakers, technologists, and consumers— to engage in a constructive dialogue aimed at forging a path forward that maximizes the benefits of AI while mitigating its potential risks. It is right at this intersection that companies like Lerna AI shine brightest.
Data Privacy the Lerna Way
In a post-cookies world where third-party data is dwindling, the imperative for businesses to leverage their first-party data potential has never been more critical. Lerna AI, a game-changer in mobile hyper-personalization recommender systems, offers a compelling solution to this pressing challenge. The innovative mobile SDK empowers apps to personalize content for each user, leveraging a combination of content metadata and on-device user data, including demographic and sensor data.
By training models on this rich trove of first-party data, Lerna AI enables apps to predict optimal content recommendations tailored to each user’s preferences and interests while preserving user privacy. In an era marked by heightened concerns over data privacy and security, Lerna AI’s approach is cold water over parched throats, ensuring businesses can reach their objectives without having to go outside the box of ethics. As Lerna AI´s CTO, Georgios Kellaris, highlights, “thanks to advances in privacy-preserving technologies like federating learning and differential privacy, training AI models on sensitive data is now possible, allowing us to learn from richer than before data, while protecting user privacy.”
The model is also highly functional; in tests, the AI dusted double click-through rates and all of these happened without any infringement on user data or rights. The tailored approach that Lerna AI brings to the process helps users to stay longer on the app. Personal recommendation aligns with what the average mobile device wants. More than 56% of customers want personalized recommendations and user experiences with 62% of business leaders citing improved customer as a benefit of personalization efforts, according to a report by Segment.
Moving ahead, the convergence of AI and privacy will continue to shape the digital landscape, challenging conventional norms and necessitating adaptive regulatory frameworks. By fostering a culture of responsible AI development and promoting transparency, accountability, and user-centric design principles, we all can harness the transformative potential of AI while upholding the fundamental principles of privacy and individual rights.
Published by: Martin De Juan