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Rider University Library

AI tools for Research

This guide includes information and suggested Generative AI tools to bolster your research productivity. Generative AI is one type of artificial intelligence (AI) system that generates text, images, and other media in response to user prompts.

Ethics of AI

As AI becomes increasingly integrated into everyday life, its use brings forth various ethical and legal challenges. Some of the key concerns include bias and fairness, accuracy and misinformation, data privacy, job automation, and safety.

To make sure AI is used in an ethical way, experts recommend following certain guidelines. While these may vary, the most common ones include:

  • Fairness: AI should produce fair results that don’t discriminate against anyone based on their race, gender, or background.

  • Explainability:  AI researchers should be able to explain why their algorithm made the decisions that it did.

  • Data Privacy: AI should respect data privacy laws by only using data when people have given consent. Personal information should be protected from misuse.

  • Robustness: AI systems should work reliably and provide accurate results. Ensuring that AI models are resilient to errors, bias, and manipulation by bad actors is essential for maintaining trust in their use.

  • Transparency: The source of data for, and development process of, AI algorithms should be open and known by users.

  • Social Responsibility: The broader impact of an AI algorithm on society and the environment should be considered before it is implemented.

Addressing Concerns

AI systems are prone to biases, which can arise when algorithms rely on incomplete, unrepresentative, or skewed data. Bias can influence AI outputs in unfair ways, favoring certain groups over others or reinforcing societal inequalities. To mitigate bias, it is essential to:

  • Carefully examine the data used to train AI systems, ensuring it is representative of the population or problem at hand.
  • Regularly audit AI models to identify and correct biases as they emerge.
  • Consider the ethical implications of using AI in sensitive areas, such as hiring, criminal justice, or healthcare, where biased decisions could have significant consequences.

Many AI models require users to create accounts and provide personal information, raising concerns about data privacy and security. It's crucial that AI systems comply with data protection regulations and safeguard user information from breaches or misuse.

To protect yourself while using AI tools:

  • Use strong passwords and avoid reusing them across multiple platforms.
  • Consider using a VPN to add an extra layer of security when accessing AI tools or downloading data.
  • Be aware of what data you are sharing with AI platforms and read their privacy policies carefully.

AI models are not foolproof and may generate incorrect or misleading information, known as "hallucinations." These inaccuracies can lead to misinformation or flawed research outcomes, especially if not properly verified.

To ensure the reliability of AI systems:

  • Verify AI-generated outputs by cross-referencing with trusted sources, such as peer-reviewed journals or authoritative databases.
  • Stay informed about the limitations and potential biases of the AI tools you use, as many companies offer guidance on how to avoid common pitfalls.