Addressing Diversity, Non-Discrimination, and Fairness in AI to Promote Inclusive Healthcare Technologies and Reduce Bias Against Vulnerable Groups

Artificial Intelligence models, especially those based on machine learning, rely a lot on the data they are given to learn from. There is a saying, “garbage in, garbage out,” which means if the data used is poor, the results will also be poor. If the data shows past unfair treatment, lacks variety, or does not include all types of patients fairly, the AI will copy those problems. This can cause some groups to get wrong treatment, miss important diagnoses, or face errors more often.

Researchers Matthew G. Hanna and others, in a 2025 review published in Modern Pathology, found three main types of bias in medical AI models:

• Data Bias: This happens when the training data does not represent different groups well. For example, if an AI system learns mostly from data on middle-aged white men, it might not work well for women, older people, or minorities. This can cause unequal care.
• Development Bias: This occurs when building the AI. Some design choices may treat certain patient groups unfairly. For example, the AI might wrongly link a disease to a group because of mistakes in how the features were chosen, leading to biased results.
• Interaction Bias: Healthcare practices change from one hospital or region to another. AI trained in one place may not work right in a different hospital with different patients or treatments. Differences in staff or how reports are made can also confuse the AI, causing unfair outcomes.

These biases can harm patients, break rules about fairness, and even violate laws like the Civil Rights Act and HIPAA.

Ethical Frameworks to Guide Trustworthy AI in Healthcare

To handle these problems, clear ethical rules are needed. The European High-Level Expert Group on AI made Ethics Guidelines for Trustworthy Artificial Intelligence in 2019. These rules are used around the world, including in U.S. healthcare. They say trustworthy AI should have three main qualities:

• Lawfulness: Follow all laws and rules.
• Ethical soundness: Respect human rights and ethics.
• Robustness: Be reliable technically and consider social impacts.

Within this framework, seven important needs for AI are highlighted. They focus on fairness, openness, and responsibility:

• Human agency and oversight: AI should help people make decisions, not replace them. Humans must check AI results, especially for sensitive medical choices.
• Technical robustness and safety: AI must work well and handle errors without causing harm.
• Privacy and data governance: Patient data must be kept private and secure, and used only by those allowed. This protects vulnerable groups.
• Transparency: Doctors and patients should understand how AI makes decisions. Patients should know when AI is used and why.
• Diversity, non-discrimination, and fairness: AI should avoid unfair treatment of any group by using diverse data and including all stakeholders during development.
• Societal and environmental well-being: AI should serve society fairly and be kind to the environment.
• Accountability: Clear responsibility must be set for what AI does. There should be ways to check and fix harms caused by AI mistakes.

A tool called the Assessment List for Trustworthy AI (ALTAI) helps AI developers and healthcare groups use these rules well.

Challenges Specific to U.S. Healthcare Organizations

Healthcare in the U.S. faces some special problems with fair AI use:

• High diversity among patients: The U.S. has many ethnic groups, income levels, and living areas. AI must reflect this variety to treat everyone fairly.
• Fragmented healthcare delivery: Different states have different laws, and hospitals differ in quality and resources. An AI working well in one place may not work well in another because of interaction bias.
• Regulation compliance: Laws like HIPAA require strong privacy protection. AI must handle patient information lawfully.
• Social determinants of health: Things like poverty, education, and housing affect health but are not always in AI models. This can cause the AI to give wrong risk levels or unfair care plans.

Healthcare leaders must choose AI carefully, keeping in mind different laws, institutions, and patient groups.

Best Practices to Reduce Bias and Promote Fair AI in Healthcare

To lower bias and make AI fair, healthcare groups in the U.S. can use these methods:

• Use diverse and representative data: Train AI on data from many groups, places, and care settings. Hospitals working together can share data to improve fairness.
• Conduct thorough bias testing: Carefully test AI before use to check how well it works for all patient groups. Use tools that find bias and measure fairness.
• Regular updates and monitoring: AI can get outdated with new diseases or rules. Update it often with fresh data and watch its performance continuously.
• Multidisciplinary oversight: Include doctors, data experts, ethicists, and community members in teams to spot bias risks early.
• Transparent communication: Tell staff and patients about AI use, what it can and cannot do. This helps build trust and good oversight.
• Clear accountability mechanisms: Assign who is responsible for AI results and set rules to fix errors or harms. This supports legal compliance and ethical care.

AI and Workflow Automations for Inclusive Healthcare

AI is often used for front office tasks like scheduling, patient calls, and billing. One company, Simbo AI, offers phone automation using AI. For U.S. healthcare administrators and IT managers, AI tools can make things run smoother but must be designed fairly.

How can workflow automation respect diversity and fairness?

• Language accessibility: AI phone systems should support many languages and dialects of patients to avoid leaving out non-English speakers.
• Cultural sensitivity: Automated messages should be written to avoid confusion or offense.
• Bias in natural language processing (NLP): Voice recognition and language systems must be fair for different accents or speech patterns related to ethnicity or age.
• Data privacy: Patient data from automation must be strictly protected following HIPAA to keep trust, especially from vulnerable groups who may worry about sharing information.
• Human oversight: Although automation can reduce wait times, people must still be available for tough or sensitive questions. Combining AI with human checks gives balance.
• Continuous feedback loops: Regularly ask patients and staff for feedback on AI systems to find and fix fairness or access problems.

Using AI front-office automation in a responsible way can improve patient service without causing bias or unfairness. When done with ethical AI ideas, this helps make healthcare more inclusive.

Addressing Bias in AI: A Practical Example in Pathology and Diagnostic AI

Pathology labs use AI more and more for tasks like examining images and predicting outcomes. However, as research by Matthew G. Hanna shows, bias in these AI tools can impact diagnosis and treatment.

• Data bias might cause an AI to miss signs of skin cancer in patients with darker skin if it was mostly trained on lighter skin examples. This would lead to worse care for minorities.
• Development bias might cause models to fit rare cases from some areas too closely, making them less useful elsewhere.

Labs need to use diverse data, check AI results for different groups, and keep human experts involved in diagnosis. Managing bias well is very important for patient safety.

Future Directions for Fair AI in U.S. Healthcare

Since AI is playing a bigger role in healthcare, ongoing focus on diversity, fairness, and non-discrimination is needed. Medical leaders can:

• Use ethical AI rules like the European guidelines and tools like ALTAI adjusted for U.S. healthcare.
• Train staff about AI bias and ethical concerns.
• Work with AI companies that show commitment to openness, privacy, and fairness.
• Watch legal changes about AI rules in the U.S. and prepare for updates.

These steps help build healthcare tools that serve all people fairly, reduce gaps in care, and improve quality.

This article is meant to help healthcare administrators, IT managers, and practice owners understand the key issues about AI bias and fairness. Facing these challenges is important to use AI in a way that helps all patients in the United States and respects ethical healthcare values.