3 Fundamental Cautions to Ensure Privacy and Reliability when Using AI in Data Analysis

Introduction

With the launch of ChatGPT in November 2022, the world has seen a rapid rise in interest in understanding the benefits of using generative artificial intelligence in companies in all sectors. In Digital Marketing it’s no different, AI is on the rise and is set to gain more and more followers, and is even one of the Marketing trends for 2024 indicated by DP6 in this post. Among the areas being transformed by artificial intelligence is data analysis. Through the introduction of new tools and techniques, various processes related to data analysis are being optimized or automated.

Much is being said about the potential gains in productivity and profit that the use of artificial intelligence can bring to day-to-day work, and the big players in the market are inserting AI into their products, as is the case with GA4, which uses machine learning to help users understand the data collected by the tool and draw up action plans. GA4 offers automated insights, which detect unusual changes in the data, and personalized insights, in which users configure conditions to be analyzed by AI.

In addition, data visualization tools such as Microsoft Power BI and Tableau GPT are also moving in this direction. In Power BI, AI simplifies the user’s use of natural language to create visualizations through native functionalities, while in Tableau GPT, the technology is used with a focus on data preparation and governance, while also offering personalized insights based on the data available, along the same lines as GA4.

It’s important to note that the aim of these technologies is not to replace the data analyst, but to allow them to focus their efforts on more strategic aspects of the job, while the tools take on mechanical and time-consuming tasks. For example, Pencil — the leading generative AI platform for brands — is used by more than five thousand brands and agencies worldwide at various stages of campaign development, from ad creative to performance strategy. The SOMA Group is using the technology to suggest adaptations and improvements to its pieces according to trends and the performance history of its products, which has made the production process more efficient, as the group’s CTO explained in an interview with Forbes Brasil.

Despite the significant benefits of artificial intelligence applications in the field of data analysis, such as the processing of massive volumes of data and real-time analysis for large-scale databases, it is important to note that the use of artificial intelligence in data analysis also presents challenges. Ethical issues such as algorithm bias, privacy and data security become a growing concern as sensitive data is processed by automated systems.

Artificial intelligence has the potential to help data-driven decision-making processes, however, in order not to fall into the pitfalls of ethical issues, it is necessary to use it carefully and follow the recommended good practices. We’ve listed the main precautions below.

Privacy

You need to understand the terms of use and privacy of the tool you choose. ChatGPT, for example, offers the possibility of deactivating the chat history and, according to Open AI, its creator, when this is done the data is not used to train the tool. Conversations are saved for 30 days and only reviewed when necessary in order to detect abuse.

The world of generative AI is evolving rapidly, but still lacks transparency on issues related to consent and data processing. A crucial additional measure to guarantee privacy in data handling is to limit the amount of information shared with AI, along with the application of anonymization techniques where appropriate.

Security

In addition to the input of sensitive data, attention must be paid to the data output and provided by the AI tool chosen in the analysis process. Defining a process for extracting this data into secure environments is just as important as the previous step. It is very important to train the team to be aware of the risks of data leaks, such as the recent case of Samsung, which banned the use of generative AI tools due to concerns about information security after employees posted a confidential code on ChatGPT.

Ensuring robust access control with authentication mechanisms to restrict access to data and models is essential to ensure that only authorized people can access them. In addition to these practices, it is recommended to carry out audits at regular intervals to strengthen the security of the process as a whole.

Biases

Another risk factor to consider when using artificial intelligence tools in data analysis is algorithmic bias, as illustrated recently in the case of congresswoman Renata Souza, who was inappropriately portrayed with a firearm by an AI platform. Although one of the prerogatives of using algorithms is to eliminate human biases, if a model is trained with biased databases, then its analysis will be influenced by such biases.

To mitigate algorithmic bias, it is crucial to ensure that comprehensive, quality databases are collected and used, as well as employing appropriate methods and techniques for pre-processing, analyzing and interpreting the data. In addition, it is essential to adopt proactive measures to monitor the impartiality of the algorithm, including auditing the results for different data groups and developing indicators to identify instances of AI bias. The response to these identifications must be swift and guided by previously stipulated protocols.

Conclusion

The use of artificial intelligence in data analysis has evolved rapidly, but it is essential that professionals who use these tools are aware of the ethical and security pitfalls so that they do not put valuable data at risk or/and jeopardize the results of their analysis. With its vast experience and expertise in analytical intelligence, DP6 is ready to help you implement effective privacy and security practices for digital marketing tools, thus boosting the success and confidence of your data analysis initiatives.

Profile of the Author: Mariana Brandão | With a degree in Bioprocess Engineering and Biotechnology from UFPR and a passion for numbers, I seek to transform the way decisions are made through the power of data. I am currently a Business Analytics Consultant at DP6.

Profile of the Author: Mellise Dantas| She has a Master’s degree in Business Administration from COPPEAD-UFRJ and a degree in Fashion from USP. With a career spanning 7 years in the e-commerce sector, she is currently a Business Analytics Consultant at DP6.