Artificial intelligence (AI) has significantly transformed human life. As these systems become more robust, it helps to increase the organization’s efficiency.
A huge discussion exists on whether to use AI in their business processes. A few business leaders think businesses can use AI better to streamline business operations.
While a few industry veterans are against the use of AI across sectors. The reason behind resistance to AI adoption across industries is the ethical implication of AI.
In this article, let’s discuss some ethical implications of AI:
Since the inception of AI, there has been a discussion about AI taking over jobs.
AI capabilities have expanded and can accomplish much more than legacy tools. Generative AI can write, code, create content, summarize, and evaluate. Massive worker displacement and replacement have occurred since AI and automation tools were adopted. Since the inception of generative AI, the pace of AI adoption has accelerated.
Businesses must train resources to adapt to the latest paradigm shift in the marketplace. For instance, employers must help resources develop generative AI skills like prompt engineering.
One of AI’s most significant ethical implications is on the enterprise design, workflow, and individual resources.
There is a surge in the adoption of AI across industries because it saves much time. Despite the debates, there has been a paradigm shift in job roles. Instead, there is a skill gap in almost or industry. With the tremendous evolution in AI, the ethical concern remains, will AI take human jobs?
The answer is simple- businesses should use AI to augment human efforts, not replace them.
Generative AI tools can automatically generate content based on the user’s text prompts. These AI tools offer tremendous productivity enhancements.
However, malicious actors can also use it to harm, either intentionally or unintentionally. Criminals can generate an email through AI to share it in the organization, posing as the brand. It can have offensive language or harmful guidance for resources.
Businesses should ensure that their content developed through AI meets ethical expectations. Moreover, it is also crucial to align the content generated through AI with the brand goals.
Organizations need huge image and text databases from various sources to train generative AI models. When AI tools generate images or coding lines, the data sources are unknown. It can create challenges for businesses in financial services or pharmaceuticals.
Enterprises might witness reputational and financial risks if they base their products on another company’s intellectual property.
Business leaders should evaluate the AI models’ outputs before using them. Regulatory bodies should define strategies that offer more transparency regarding IP and copyright challenges.
Modern businesses struggle to maximize generative AI benefits because of the inherent ethical AI issues.
Data sets used to train Generative AI large language models (LLMs) may have access to users’ identifiable information (PII).
Malicious actors can extract this data with a simple text prompt. It will be very difficult for individuals to find and eliminate this information.
Enterprises that develop or modify LLMs must ensure they do not embed PII in the language models. Removing PII from the data sets used to train AI to comply with privacy laws is crucial.
Generative AI is democratizing AI tools by making them more accessible to users. The lethal combination of democratization and accessibility might expose sensitive data to an unauthorized resource. Implications of unexpected security incidents might lead to lost customer trust or legal litigations.
Enterprises should set clear guidelines and governance policies. Additionally, they also need to have transparent communication throughout the organization. This approach facilitates shared responsibility for securing sensitive data and IP.
The advent of generative AI might strengthen the current biasedness. For instance, data used for training LLMs can be biased. Companies have no control over this. But it will be used to run AI tools.
It is crucial for enterprises working on AI to hire and recruit experts to identify unconscious bias in data and models.
The majority of generative AI systems compile facts. This is from the point where AI learned to link relevant data with each other.
However, not all generative AI models reveal the details of the data sources. Hence trustworthiness is one of the most critical ethical implications of AI. While analyzing generative AI, industry veterans expect to come at causal response explanations.
However, machine learning (ML) models and generative AI tools look for correlations instead of causality.
Hence, businesses need to prioritize AI model interpretability. It is an effective way to conclude the reason why the AI framework gave the response. Decision-makers can determine if the answer is acceptable answer or not.
Until businesses achieve a level of trustworthiness with AI foundation models, they should not rely on them.
Also Read: How Artificial Intelligence and Automation Are Transforming Industries
Generative AI tools leverage large data volumes for training purposes. It may be difficult to govern these data sets or question the data sources. Additionally, there is a significant chance of using data without consent or containing bias.
However, social influences on AI systems can amplify inaccuracies. The AI system’s accuracy depends on the quality of data it stores and processes. A few generative AI foundation models mine internet data which cannot be reliable. Hence accuracy is another ethical implication that AI imposes.
Despite the immense benefit of Artificial intelligence (AI), it has a few inherent risks that it exposes business to. Hence before integrating generative AI into the enterprise tech stack, businesses need to be aware of the potential ethical implications of AI.
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Nikhil Sonawane is a Tech Journalist with OnDot Media. He has 4+ years of technical expertise in drafting content strategies for Blockchain, Supply Chain Management, Artificial Intelligence, and IoT. His Commitment to ongoing learning and improvement helps him to deliver thought-provoking insights and analysis on complex technologies and tools that are revolutionizing modern enterprises. He brings his eye for editorial detail and keen sense of language skills to every article he writes. If he is not working, he will be found on treks, walking in forests, or swimming in the ocean.
