Connect with us

Artificial Intelligence

Social Impact of Generative AI: Benefits and Threats




Featured image for generative AI

Today, Generative AI is wielding transformative power across various aspects of society. Its influence extends from information technology and healthcare to retail and the arts, permeating into our daily lives. 

As per eMarketer, Generative AI shows early adoption with a projected 100 million or more users in the USA alone within its first four years. Therefore, it is vital to evaluate the social impact of this technology.   

While it promises increased efficiency, productivity, and economic benefits, there are also concerns regarding the ethical use of AI-powered generative systems. 

This article examines how Generative AI redefines norms, challenges ethical and societal boundaries, and evaluates the need for a regulatory framework to manage the social impact. 

How Generative AI is Affecting Us

Generative AI has significantly impacted our lives, transforming how we operate and interact with the digital world. 

Let's explore some of its positive and negative social impacts. 

The Good

In just a few years since its introduction, Generative AI has transformed business operations and opened up new avenues for creativity, promising efficiency gains and improved market dynamics. 

Let’s discuss its positive social impact:

1. Fast Business Procedures

Over the next few years, Generative AI can cut SG&A (Selling, General, and Administrative) costs by 40%.

Generative AI accelerates business process management by automating complex tasks, promoting innovation, and reducing manual workload. For example, in data analysis, models like Google's BigQuery ML accelerate the process of extracting insights from large datasets. 

As a result, businesses enjoy better market analysis and faster time-to-market.

2. Making Creative Content More Accessible

More than 50% of marketers credit Generative AI for improved performance in engagement, conversions, and faster creative cycles. 

In addition, Generative AI tools have automated content creation, making elements like images, audio, video, etc., just a simple click away. For example, tools like Canva and Midjourney leverage Generative AI to assist users in effortlessly creating visually appealing graphics and powerful images. 

Also, tools like ChatGPT help brainstorm content ideas based on user prompts about the target audience. This enhances user experience and broadens the reach of creative content, connecting artists and entrepreneurs directly with a global audience.

3. Knowledge at Your Fingertips

Knewton’s study reveals students utilizing AI-powered adaptive learning programs demonstrated a remarkable 62% improvement in test scores.

Generative AI brings knowledge to our immediate access with large language models (LLM) like ChatGPT or They answer questions, generate content, and translate languages, making information retrieval efficient and personalized. Moreover, it empowers education, offering tailored tutoring and personalized learning experiences to enrich the educational journey with continuous self-learning. 

For example, Khanmigo, an AI-powered tool by Khan Academy, acts as a writing coach for learning to code and offers prompts to guide students in studying, debating, and collaborating.

The Bad

Despite the positive impacts, there are also challenges with the widespread use of Generative AI. 

Let's explore its negative social impact: 

1. Lack of Quality Control

People can perceive the output of Generative AI models as objective truth, overlooking the potential for inaccuracies, such as hallucinations. This can erode trust in information sources and contribute to the spread of misinformation, impacting societal perceptions and decision-making.

Inaccurate AI outputs raise concerns about the authenticity and accuracy of AI-generated content. While existing regulatory frameworks primarily focus on data privacy and security, it's difficult to train models to handle every possible scenario. 

This complexity makes regulating each model's output challenging, especially where user prompts may inadvertently generate harmful content. 

2. Biased AI

Generative AI is as good as the data it's trained on. Bias can creep in at any stage, from data collection to model deployment, inaccurately representing the diversity of the overall population. 

For instance, examining over 5,000 images from Stable Diffusion reveals that it amplifies racial and gender inequalities. In this analysis, Stable Diffusion, a text-to-image model, depicted white males as CEOs and women in subservient roles. Disturbingly, it also stereotyped dark-skinned men with crime and dark-skinned women with menial jobs. 

Addressing these challenges requires acknowledging data bias and implementing robust regulatory frameworks throughout the AI lifecycle to ensure fairness and accountability in AI generative systems.

3. Proliferating Fakeness

Deepfakes and misinformation created with Generative AI models can influence the masses and manipulate public opinion. Moreover, Deepfakes can incite armed conflicts, presenting a distinctive menace to both foreign and domestic national security.

The unchecked dissemination of fake content across the internet negatively impacts millions and fuels political, religious, and social discord.  For example, in 2019, an alleged deepfake played a role in an attempted coup d'état in Gabon.

This prompts urgent questions about the ethical implications of AI-generated information.

4. No Framework for Defining Ownership

Currently, there is no comprehensive framework for defining ownership of AI-generated content. The question of who owns the data generated and processed by AI systems remains unresolved. 

For example, in a legal case initiated in late 2022, known as Andersen v. Stability AI et al., three artists joined forces to bring a class-action lawsuit against various Generative AI platforms. 

The lawsuit alleged that these AI systems utilized the artists' original works without obtaining the necessary licenses. The artists argue that these platforms employed their unique styles to train the AI, enabling users to generate works that may lack sufficient transformation from their existing protected creations.

Additionally, Generative AI enables widespread content generation, and the value generated by human professionals in creative industries becomes questionable. It also challenges the definition and protection of intellectual property rights.

Regulating the Social Impact of Generative AI

Generative AI lacks a comprehensive regulatory framework, raising concerns about its potential for both constructive and detrimental impacts on society. 

Influential stakeholders are advocating for establishing robust regulatory frameworks.

For instance, the European Union proposed the first-ever AI regulatory framework to instill trust, which is expected to be adopted in 2024. With a future-proof approach, this framework has rules tied to AI applications that can adapt to technological change. 

It also proposes establishing obligations for users and providers, suggesting pre-market conformity assessments, and proposing post-market enforcement under a defined governance structure.

Additionally, the Ada Lovelace Institute, an advocate of AI regulation, reported on the importance of well-designed regulation to prevent power concentration, ensure access, provide redress mechanisms, and maximize benefits.

Implementing regulatory frameworks would represent a substantial stride in addressing the associated risks of Generative AI. With profound influence on society, this technology needs oversight, thoughtful regulation, and an ongoing dialogue among stakeholders.  

To stay informed about the latest advances in AI, its social impact, and regulatory frameworks, visit