Exploring the Ethical Dimensions and Dilemmas of GenAI | NTT DATA

Thu, 16 November 2023

Exploring the Ethical Dimensions and Dilemmas of GenAI

Generative Artificial Intelligence (GenAI) has emerged as a transformative technology with a seemingly limitless range of applications and the promise of widespread benefits to society.

Generative Artificial Intelligence (GenAI) has emerged as a transformative technology with a seemingly limitless range of applications and the promise of widespread benefits to society. 

Just five days after its public launch in November 2022, ChatGPT, the most popular GenAI tool, had gained 1m users and it currently attract more than 1.5bn visits to its website each month.  The popularity of GenAI tools stems not just from the wide range of applications but also their accessibility. As well as often being free to use, these services are highly intuitive to use through a familiar chatbot interface and a communication based in natural language

However, beneath the considerable promise of GenAI lie a multitude of ethical and business challenges that demand careful consideration.

As the use of GenAI grows and more companies incorporate GenAI into their products and services, its societal impact becomes increasingly apparent. For that reason, there is an urgent need to address the ethical dimensions of the widespread use of these tools to ensure that the development and deployment aligns with our core values as a society.

In a short series of blogposts, we hope to explore in some detail the ethical landscape surrounding GenAI—an exploration into the profound implications, complex dilemmas, and potential resolutions.

Before we explore the ethical dimensions and dilemmas associated with GenAI, it is important that we understand how GenAI works if we are to overcome the misconceptions and confusion that often accompanies debates about this new technology.

What is GenAI?

At its core, GenAI refers to a family of deep learning models endowed with the extraordinary capacity to create content—be it text, images, or other data types—that closely resembles the information on which they were trained. By scrutinizing patterns within the training data, these algorithms acquire the ability to generate fresh, innovative outputs, essentially producing new samples within the same data format.

Figure 1 Diffusion versus Autoregressive Models

 GenAI models come in diverse forms, categorized based on the type of output they produce or the underlying architecture they employ. Models geared towards generating visual content are often identified as Generative Adversarial Networks (GANs) or diffusion models. 

Conversely, those designed for generating text or audio typically fall under the autoregressive category, predicting forthcoming values using previous data as their input. Each of these approaches has led to the development of innovative products and services that are not just advancing our understanding of AI but also offer significant advantages when compared to products and services that do not incorporate GenAI.  

Diffusion models shine when it comes to creating visual and multimedia content or executing tasks like image inpainting and outpainting, particularly when provided with a textual prompt detailing the desired outcome. Prominent diffusion models for image generation include DALL·E 2, Image GPT, Midjourney, and Stable Diffusion.

On the other hand, autoregressive models drive Large Language Models (LLMs) such as the widely-used ChatGPT. These models generate text incrementally, one word at a time, utilizing the text input by the user in conjunction with the text they've previously generated. LLMs demonstrate exceptional performance across a myriad of natural language processing tasks, buoyed by their training on vast datasets of internet-scale data.

Use Cases for GenAI in Different Sectors

The potential applications of GenAI span many sectors, including financial services, education and healthcare. In banking, GenAI can prove invaluable in the detection of fraudulent transactions, the generation of synthetic data for training machine learning models, protecting customer data using GANs to estimate Value at Risk, and predicting potential losses in specific scenarios. 

In education, it has the capacity to revolutionize personalized course design, enhance student learning through virtual simulations, and restore historical learning materials. Moreover, in healthcare, the applications include drug discovery and development, personalized treatments, medical imaging, and population health management, among other transformative applications.


Ethical Dilemmas of GenAI Lie Beneath the Surface

Amidst this dazzling promise and seemingly limitless potential, GenAI is not without its drawbacks. The utilization of this technology introduces a range of ethical predicaments, including the perpetuation of pre-existing biases, concerns surrounding ownership, and accountability, the potential to generate fake news, and the disturbing capacity to impersonate individuals.  These critical issues necessitate careful examination and thoughtful solutions.

In future posts, we will delve deeper into these ethical concerns, exploring the complexities they present and offering insights into how we can strike a balance between technological advancement and ethical responsibility. Join us as we navigate the intricate ethical terrain of GenAI, where the future converges with ethics, and together, we endeavour to shape a responsible, equitable, and promising tomorrow.

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