The Ethical Implications of Generative A.I.

Reference

Generative AI is a rapidly growing field across a variety of industries, including technology, healthcare, entertainment, and finance. Microsoft, Google, Facebook, and other top technology companies are currently using generative AI to accelerate AI innovation. However, its use has also raised a number of ethical concerns that must be considered.

About Generative AI

• Generative AI is a broad term used to describe any form of artificial intelligence that uses machine learning algorithms to create new digital images, video, audio, text, or code.
• It works by training a model on a large dataset and then using that model to generate new content that is similar to the training data. This can be done through techniques such as neural machine translation, image generation, and music creation.

Ethical concerns associated with generative AI

• Accuracy: Companies often do not disclose the data used to train generative AI models. Because AI models sometimes provide incorrect or outdated information.
  • As such, the content of generative AI cannot be used as a reliable and trustworthy source of information.
  • For example, ChatGPT sometimes creates citations for sources that do not exist.
• Privacy and data security: Generative AI large language models (LLMs) are trained on data sets that sometimes contain personally identifiable information (PII) about individuals.
• Unauthorized use of this data or the creation of highly accurate synthetic profiles is a significant concern.
• Environmental impact: Creating, training, and using generative AI models requires a lot of energy, which increases carbon emissions. However, researchers and companies are looking at various ways to make generative AI more sustainable.
• Disclosure of sensitive information: Generative AI is making information more accessible. This brings up the possibility of a medical researcher inadvertently disclosing sensitive patient information, or a consumer brand inadvertently exposing its product strategy.
• Such unintended events can result in a loss of patient, customer and public trust, as well as legal implications.
• Distribution of harmful content: Generative AI systems can automatically create content based on text prompts provided by humans. These systems can also drastically alter productivity, which can sometimes be used to cause harm, either intentionally or unintentionally.
  • For example, an AI-generated email sent on behalf of a company may contain offensive language or issue incorrect guidance to employees.
• Deepfakes: Generative AI’s ability to create content blurs the distinction between real and artificial, which is a matter of concern. From fake news to offensive videos, these can distort public perception and promote disinformation.
• Biased Outputs: Generative AI models operate on fed data. If this data is culturally, socially, economically or politically biased, then the generative AI model can give incorrect outputs that can lead to racial, communal, financial or political bias.
• Creation of harmful content: Anything can be easily created if someone misuses it for unethical content. Such unethical content can harm the society.
• Misinformation: Generative AI models are trained on datasets from various sources in which errors are possible. In such cases, these models can generate factually incorrect information.
• Copyright Infringement: Generative AI is trained from many unknown sources, so the chances of data breach are high. This may eventually lead to copyright infringement which raises legal concerns. Also, generative AI presents complex challenges for various rights management.
  • For example, content of artists and authors has been used to train generative AI.
• Replacing human workforce: The growing capabilities of generative AI cannot be matched by any human. While this can be highly beneficial in terms of cost reduction and time management, it can impact the workforce demand.
• Regulatory compliance: Generative AI models sometimes do not comply with regulations such as the General Data Protection Regulation (GDPR). These tools may fail to maintain confidentiality about sensitive information which may be against individual and national interest.

Suggestions

• Establish clear guidelines, governance and effective communication to safeguard protected data and IP when sensitive information is involved
• Not including PII (Personally Identifiable Information) in language models for privacy and the ability to easily remove PII from these models in compliance with privacy laws
• Also emphasizing the need for a 'right to forget' feature
• Need to focus on quality data to increase the quality of output
• Emphasis on understanding the limitations of AI, unintended biases that result from skewed data, and the biases of those building these models
• Holding accountability for the organizations and technology teams that own, build, and maintain AI systems
• Standardization of practices around AI and knowledge management to ensure ethical and economic accountability
• Ensuring clear and transparent policies for adherence to data law principles such as privacy, consent and data quality