Other Areas of Bias in AI
Bias in AI remains a big issue in several areas. One concerning area is predictive policing. Algorithms used to predict where crimes might happen often rely on biased historical data, leading to over-policing in minority communities and worsening inequalities.
In healthcare, diagnostic AI systems can be biased too. They might misdiagnose or under-diagnose certain conditions, especially in marginalized groups, because they're trained on data that doesn't represent everyone equally. This can worsen health disparities.
Another problem is in hiring. AI tools used to screen resumes or select candidates can favor certain groups, like men or people from higher socio-economic backgrounds. This can make it harder for others to get fair opportunities in the job market.
These examples show how bias in AI can affect different aspects of life. It's important to keep working on fixing these biases to make sure AI treats everyone fairly.
Comparison/Synthesis of Methodologies
Methodology #1: AI+Ethics Curricula for Middle School Youth, Richard's pick
Methodology #2: Can Children Understand Machine Learning Concepts?, Cesar's pick
Both studies dive into understanding human interaction with artificial intelligence but target different populations and learning goals. Examining their methodologies reveals similarities and differences in approach, participant recruitment, assessment methods, and ethical considerations.
Similarities:
Pre-Test and Post-Test: Both studies employ pre-tests to gauge baseline knowledge and post-tests to assess learning outcomes. This allows for measuring the effectiveness of interventions.
Mixed Methods: Both utilize quantitative and qualitative data collection strategies for holistic analysis. This incorporates both numerical data and rich descriptive information.
Ethical Considerations: Both acknowledge ethical aspects of their research, ensuring informed consent and addressing potential risks of involving human subjects.
Differences:
Target Population: Methodology #1 targets teachers for professional development in AI education, while #2 focuses on children learning about machine learning concepts.
Learning Experience: #1 employs workshops with discussion and feedback, while #2 uses structured tasks and simulations.
Data Collection: #1 utilizes workshops assessments tailored to each session, while #2 combines pre-tests,post-tests, and task observations.
Research Goal: #1 aims to develop teachers as ambassadors for AI education, while #2 focuses on children's understanding of specific ML concepts.
Data on Demographics: #1 reports school types but not individual data, while #2 emphasizes inclusivity but doesn't mention socioeconomic information.
Methodology #1:
Prioritizes inclusive recruitment with Title 1 school involvement.
Provides limited information on participants' prior AI experience.
Emphasizes teacher ownership and feedback implementation.
Doesn't explicitly mention "Data Labeling Aspects" used in #2.
Methodology #2:
Clearly defines key terms like ML and AI for clarity.
Focuses on specific aspects of data labeling for ML understanding.
Includes questions about the ethical implications and real-world application of ML.
Doesn't report on participant demographics beyond prior ML knowledge.
While both studies use pre-tests, post-tests, and mixed methods, their goals, target populations, and learning experiences differ significantly. #1 focuses on empowering teachers to deliver AI education, while #2 concentrates on children's foundational understanding of ML concepts. Both methodologies exhibit ethical considerations and tailored data collection for their specific aims.
CITI Notes Chapters 3-6
The Federal Regulations - SBE
This article outlines federal regulations concerning the protection of human subjects in research, particularly under 45 CFR 46, known as the Common Rule.
In summary, these regulations aim to balance the advancement of research with ethical considerations, ensuring the protection of human subjects involved in research activities.
Federal regulations set a baseline for research ethics, with institutions having the option to implement additional procedures.
Assessing Risk
Identifying and evaluating risks in social and behavioral science research is challenging.
Risks include invasion of privacy, breach of confidentiality, and risks associated with study procedures.
Invasion of privacy can occur when personal information is accessed without consent.
Breach of confidentiality is a primary concern and can have adverse effects on subjects.
Study procedures, such as data collection methods, can also pose risks to subjects.
Assessing risk involves considering both the probability and magnitude of harm.
Risks can vary depending on factors like culture, subject population, and research context.
Common research methodologies like surveys and interviews can pose potential risks, requiring careful assessment of harm probability and magnitude.
Informed Consent
Gifts and Reimbursement: Gifts to subjects must be disclosed, and payment conditions explained.
Recruitment Strategies: Must be reviewed and approved by IRB, part of the consent process.
Ensuring Comprehension: Information should be presented clearly and at the appropriate reading level.
Ensuring Free Choice: Participation must be voluntary without coercion, and subjects can withdraw at any time.
Safeguards for Vulnerable Subjects: IRBs must ensure additional protections for vulnerable populations.
Informed consent is crucial in research to ensure subjects understand risks and benefits. The process involves providing information, ensuring comprehension, and documenting agreement. Subjects' rights, comprehension, and cultural factors must be considered.Â