AI Broader Impact Statements
Example Inputs
Example
Outputs
Bias and Fairness
Criteria: Is bias or fairness in machine learning models or data a focus of this text example?
Summary: Addressing biases in machine learning models is crucial to prevent perpetuating social biases, discrimination, and unfairness in decision-making processes.
Security and Attacks
Criteria: Does the text example address security vulnerabilities or adversarial attacks against systems?
Summary: Addressing security concerns in machine learning is crucial for ensuring system safety and reliability against potential attacks.
Environmental Impact
Criteria: Does this text example highlight concerns about the environmental impact of technology?
Summary: Addressing environmental impact in AI research is crucial due to high energy consumption and carbon footprint of training large models.
Job Displacement
Criteria: Does this text example discuss the risk of job displacement due to automation or AI?
Summary: Advancements in technology, particularly artificial intelligence and automation, pose a threat to various job sectors, potentially leading to widespread unemployment.
Analysis
➡️ Try out analyzing this data with LLooM on this Colab notebook.
Task: Investigate anticipated consequences of AI research
Advances in AI are driven by research labs, so to avoid future harms, today's researchers must be equipped to grapple with AI ethics, including the ability to anticipate risks and mitigate potentially harmful downstream impacts of their work. How do AI researchers assess the consequences of their work? LLooM can help us to understand how AI researchers discuss downstream outcomes, ethical issues, and potential mitigations. Such an analysis could help us to uncover gaps in understanding that could be addressed with guidelines and AI ethics curricula.
Dataset: NeurIPS Broader Impact Statements, 2020
In 2020, NeurIPS, a premier machine learning research conference, required authors to include a broader impact statement in their submission in an effort to encourage researchers to consider negative consequences of their work. These statements provide a window into the ethical thought processes of a broad swath of AI researchers, and prior work from Nanayakkara et al. has performed a qualitative thematic analysis on a sample of 300 statements.