80% of internet users have interacted with a chatbot at some point.
By the end of 2023, chatbots are expected to generate $100B transactions in the eCommerce industry.
Chatbots are primarily using Generative AI behind the scene. However, the downside of Generative AI is significant, as it introduces a myriad of ethical issues. Examples include plagiarism, the creation of hurtful and disrespectful content, and the potential displacement of jobs.
Companies face the risk of reputational damage and regulatory consequences if they fail to implement \”ethical policies\” for the use of generative AI.
A survey conducted by Deloitte revealed compelling statistics:
74% have initiated testing Gen AI technology.
65% have commenced internal usage of Gen AI.
31% are leveraging Gen AI externally.
These figures underscore the significant adoption and potential impact of Generative AI, prompting high expectations from those associated with AI technology. Consequently, there is a substantial responsibility to ensure the ethical use of Gen AI.
Several concerns arise, and future posts will explore possible solutions. Some of the key concerns include:
Harmful Content:
Examples include Deepfake videos and instances where criminals have exploited AI-generated cloned voices for nefarious activities.
Sensitive Information Leak:
The rapid democratization of AI technology increases the risk of inadvertently leaking sensitive information during processes like fine-tuning of a Language Model (LLM) when proper policies are lacking.
Data Privacy:
Inadvertent disclosure of Personally Identifiable Information (PII) is a plausible risk, particularly during the training of an LLM without the implementation of appropriate policies.
Copyright Violations:
LLMs trained on internet data may inadvertently use copyrighted material, constituting a breach of trust.
Bias, Transparency, and Data Provenance Issues:
Achieving unbiased data is an elusive goal, given the inherent biases present in the data that AI is trained on. Consequently, the outcomes derived from AI models are also predisposed to bias.
Furthermore, deep learning models pose challenges in terms of explainability, as the results emanating from these models resemble outputs from a black box. Without robust data governance policies in place, certainty about the source of data becomes uncertain. Given the substantial amount of data required for training models, overlooking proper control measures becomes a prevalent risk.
Data Poisoning:
Deliberate contamination of datasets with false information, akin to a virus attack, poses a threat to AI outcomes.
While challenges are inherent in the introduction of any new technology, solutions are always within reach. Stay tuned for the next post, where I’ll discuss a few solutions implemented in the field.
#generativeai #artificialintelligence
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