While the potential for growth and innovation is enormous, risks and ethical implications also exist.
Generative AI produces content that blurs the line between the real and artificial, raising questions about the ethical responsibilities of the creators and users. A growing web of ethical considerations emerges as the technology becomes more pervasive and humans rely more on AI-generated content.
Addressing the issues requires a thorough understanding of generative AI’s underlying principles and processes because it’s here that the ethical considerations are integrated into the frameworks.
Without a deep understanding and careful regulation of AI, we’re open to risks, such as accuracy and misinformation, bias, intellectual property risks and hallucinations. In addition, a solid ethical foundation for generative AI is vital to building societal trust and nurturing future advancements.
Let’s look in more detail at some of these risks:
According to a recent McKinsey report, one of the most significant risks companies experience using generative AI is lack of accuracy (56%).
This can range from sending a customer the wrong information to having incorrect data on compliance documents, which could have far more costly and legal consequences.
Generative AI models like ChatGPT are trained on data sets consisting of hundreds of billions of parameters. However, responses to questions and prompts may contain inaccuracies if, for example, the inputs used are outdated, incomplete or inaccurate.
Many generative AI models, particularly large language ones, depend on older inputs and operate from a specific 'knowledge cut-off date’ – when the AI model was last updated. Any information available after that date would not appear in the outputs. Therefore, transparency about the cut-off date and user awareness is essential in minimizing such risks.
Information inaccuracies may also arise due to the limitations of the training data. Generative AI only operates within the constraints of the data it has available. It may have trouble providing comprehensive contextual awareness where the information required lies beyond its training data. This can result in responses that lack nuance and contextual understanding.
There may be societal biases embedded within the algorithms and data sets of generative AI models, resulting in biased content generation or decision-making, perpetuating prejudices and discriminatory narratives against certain groups.
Addressing bias is a major challenge as generative AI becomes more widespread. Some mitigation strategies include more diverse data selection and exercising human vigilance with continual monitoring of outputs.
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Intellectual property risks
Intellectual copyright infringement is also high on the McKinsey risk report at 46%. Several high-profile cases exist where AI tools have incorporated proprietary material without the creator’s consent. Most recently, the New York Times sued OpenAI and Mircosoft for copyright infringement over millions of newspaper articles being used to train chatbots.
Due to its complex nature, generative AI presents many challenges to traditional intellectual property norms and regulations. Courts are now considering applying the existing laws to generative AI content.
Apart from copyright infringement, there are the questions of licensing, usage rights, plagiarism, and ownership of AI-generated works.
AI models use hundreds of billions of data sets for training and learn to make predictions by finding patterns. However, suppose we feed it incorrect or incomplete assumptions. In that case, the model may learn incorrect patterns and present glaringly false information as facts, termed hallucinations. The models lack the reasoning to apply logic or consider the data inconsistencies they provide.
Hallucinations can mislead people, spread bias and misinformation and, in a business environment, cause reputational damage and erode trust.
The appearance of generative models like ChatGPT and DALL-E from OpenAI has brought the debate on ethics surrounding AI to the forefront.
Such considerations include transparency and accountability – AI systems often operate in a 'black box’ where users cannot see how deep learning systems arrive at their decisions. This can have consequences in, for example, healthcare, where understanding how decisions are made is vital. Likewise, clarifying accountability and taking corrective action is necessary should things go wrong.
There are risks around data privacy and security. Because generative AI handles large amounts of personal and sensitive data, Organizations failing to prioritize this may run up against regulatory requirements such as Europe’s GDPR and face serious consequences. To avoid this, they must implement robust safeguards such as data encryption and access controls and conduct regular security reviews.
Sustainability is also part of the ethical discussion – Training large language models (LLMs) consumes enormous amounts of water and electricity and has a large carbon footprint. Before implementing such a system, companies must weigh the environmental impact of their actions.
The economic and operational implications
We’ve seen generative AI’s enormous potential to increase productivity and efficiency in all sectors. What are the economic implications of this?
In a 2023 report, McKinsey estimates that the total economic benefit of generative AI could amount to $6.1 trillion to $7.9 trillion annually. 75% of the value will come from customer service, marketing and sales, software engineering and research and development.
A substantial benefit will come from increased productivity, specifically in internal processes, where generative AI could augment workers’ capabilities by automating some of their activities. This can change the nature of work and have a societal impact as knowledge workers transfer to other tasks and readjust to new roles.
While it’s tempting, amid the hype and furore, to want to implement generative AI fully in your business, it’s first worth considering a few factors.
Be sure that the benefit of implementing the system will justify the cost. Training a large generative AI model is expensive. It may also need extra infrastructure and maintenance, adding to the costs. In addition, these activities may negatively impact your business’s sustainability goals.
Therefore, carrying out a cost-benefit analysis beforehand is vital. You may discover, for example, that a smaller LLM can also supply your needs for a fraction of the cost with less environmental impact.
Every business has different needs and goals. It’s advisable to see generative AI as a strategic tool in your toolkit and find the best fit for your business objectives.