Generative AI
Ethical implications of generative AI: Navigating unchartered waters
Who thought machines could create art, understand complex documents, write stories, compose music, and perform various other human activities?
A leading management consulting firm lists generative AI as a rapidly evolving and impactful technology that will revolutionise productivity.
Ethical use of AI is a topic of debate across the world. It is important to have the guardrails up and establish clear principles for the use of generative AI. This article explains the ethical concerns around generative AI, a case study, and best practices. You can read this article with another expert opinion in New-age predicaments to better understand all ethical concerns with generative AI.
Ethical concerns of generative AI
Here is a list of concerns about generative AI that the global community is actively monitoring –
Harmful content distribution
Generative content can produce valuable human-like and harmful/offensive content with equal ease and efficiency. The recent wave of deep fake videos, photos, and audio can propagate hate speech or even commit financial fraud.
It is impossible to distinguish between real and fake phone calls, making the scams ever more sophisticated.
Copyright and legal implications
Generative AI models use tons of data to train themselves to produce an outcome. In this process, the training team may accidentally infringe on the intellectual property rights or copyright data of another business.
This can lead to conflict, loss of reputation, and lawsuits by the company whose copyright data was compromised.
Data privacy concerns
The training data may contain personally identifiable information (PII). PII is any information that reveals a person’s name, address, telephone number, social security number, email address, etc. This breach can cause identity theft, manipulation, and fraud.
The data training models must adhere to the privacy guidelines and ensure they do not feed any PII data to generative AI.
Disclosure of sensitive information
The euphoria of AI-powered results can make employees overlook the inadvertent disclosure of sensitive information. To achieve faster results, they may upload sensitive information, such as legal contracts, pricing and costing sheets, source code, etc., into the generative AI model.
If this sensitive and confidential data goes online through generative AI, it could result in severe financial implications for the business.
Biased results
Generative AI models work on the data the annotators feed. The generative AI model could produce skewed output if this data is culturally, socially, economically, and politically biased. This racial, communal, financial, or political bias could be offensive.
Morale of the workforce
Hyper-productive generative AI may appear as a threat that results in job losses. According to a management consulting firm, AI can automate certain tasks, making 60 to 70 per cent of jobs redundant. This calls for upskilling or reskilling of employees to a new role in the advanced AI era.
AI-generated misinformation case study
United Nations (UN) has highlighted concern over AI-generated fake or biased content that can potentially lead to conflicts and crimes.
One well-known case study is that of Tay Chatbot by Microsoft, which learnt racist and xenophobic language from Twitter trolls and had to halt its activity soon after launching.
Best practices for ethical usage of generative AI
Generative AI augments humans and machines to execute business processes and activities.
Align with global standards
Several legislations give an ethical framework and policies for generative AI. Examples are –
- European Union
- United States
- United Kingdom
The AI Act in the EU would look to divide AI apps into low-to-no risk, high-risk, and unacceptable-risk categories with a special focus on copyright concerns and generative AI.
Some of the legislations in the US that could become a law in the future are the blueprint for an AI Bill of Rights, copyright registration guidance for AI-generated content, and NIST’s AI Risk Management Framework.
While the UK may be moving slower than the rest of the EU, it already has a policy called AI regulation, a pro-innovation approach that has the necessary plans for AI regulation.
Make yourself aware of UNESCO’s AI Ethics Guidelines that 193 member states adopted in 2021. Its four core values are –
- Peaceful and just societies
- Environmental flourishing
- Human rights and dignity
- Diversity and inclusiveness
Engage with ethical AI communities
Several communities work towards making generative AI an ethical space. Some are the Montreal AI Ethics Institute and AI Ethics Lab, which provide ethical AI literacy. Some of the tenets of ethical AI are data privacy, transparency, accountability, and robustness of the technology providers.
Stay aware of the key developments in ethical generative AI to keep the practices within your company safe.
Learn and foster an environment of awareness
Not all the information available online is true that you can take at face value. The same holds for generative AI. As end-users, we must challenge, question, and even fact-check information by verifying its origin.
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How can Infosys BPM help adopt ethical generative AI?
Infosys BPM Generative AI Business Operations platform is a set of services, solutions, and platforms that help enterprises in value creation, generative evolution, and automation.
Read more about Infosys BPM’s generative AI platform.
