Ethics and Bias in AI for Public Health
Expert-defined terms from the Advanced Skill Certificate in AI in Public Health and Epidemiology course at London School of International Business. Free to read, free to share, paired with a globally recognised certification pathway.
Ethics and Bias in AI for Public Health #
Ethics and Bias in AI for Public Health
Ethics #
Ethics
Ethics in AI for public health refers to the moral principles that govern the us… #
It involves considerations of fairness, transparency, accountability, and privacy in the development and deployment of AI systems to ensure that they align with societal values and do not cause harm.
Bias #
Bias
Bias in AI for public health refers to the systematic and unfair preferences or… #
Bias can arise from the data used to train AI models, the algorithms themselves, or the design and implementation of the systems.
Algorithmic Bias #
Algorithmic Bias
Algorithmic bias refers to the phenomenon where artificial intelligence algorith… #
This bias can result in unfair or harmful outcomes, especially in public health applications where decisions impact people's well-being.
Data Bias #
Data Bias
Data bias in AI for public health occurs when the training data used to develop… #
This can lead to skewed results and inaccurate predictions, potentially exacerbating health disparities and inequities.
Fairness #
Fairness
Fairness in AI for public health is the principle of ensuring that the benefits… #
Fairness involves addressing bias, promoting diversity, and considering the impact of AI interventions on vulnerable populations.
Transparency #
Transparency
Transparency in AI for public health refers to the practice of making the decisi… #
Transparent AI systems enable accountability, trust, and the identification of potential biases or errors.
Accountability #
Accountability
Accountability in AI for public health involves holding developers, users, and s… #
This includes ensuring that decisions made by AI systems are traceable, auditable, and subject to oversight to prevent harm and promote trust.
Privacy #
Privacy
Privacy in AI for public health encompasses the protection of individuals' perso… #
Respecting privacy rights is critical to maintaining trust and ensuring the ethical use of data in public health applications.
Ethical Principles #
Ethical Principles
Ethical principles in AI for public health provide a framework for guiding decis… #
Common ethical principles include beneficence, non-maleficence, autonomy, justice, and respect for persons.
Beneficence #
Beneficence
Beneficence is an ethical principle that requires promoting the well #
being and welfare of individuals and communities through the use of AI technologies in public health. AI systems should aim to maximize benefits while minimizing risks and harm to those they serve.
Non #
maleficence
Non #
maleficence is an ethical principle that emphasizes the obligation to do no harm when deploying AI technologies in public health. Developers and users of AI systems should strive to prevent negative consequences and mitigate risks to ensure the safety and well-being of individuals.
Autonomy #
Autonomy
Autonomy is an ethical principle that upholds individuals' rights to make inform… #
Respecting autonomy requires obtaining consent, maintaining confidentiality, and empowering individuals to make choices about their health.
Justice #
Justice
Justice is an ethical principle that pertains to the fair distribution of benefi… #
Ensuring justice involves addressing disparities, promoting equity, and considering the needs of marginalized or underserved populations.
Respect for Persons #
Respect for Persons
Respect for persons is an ethical principle that recognizes the inherent dignity… #
Respecting persons involves obtaining informed consent, protecting privacy, and upholding the confidentiality of personal health information.
AI Governance #
AI Governance
AI governance refers to the policies, regulations, and practices that guide the… #
Effective AI governance frameworks promote transparency, accountability, and fairness to ensure that AI systems serve the public interest.
AI Ethics Committees #
AI Ethics Committees
AI ethics committees are interdisciplinary groups tasked with evaluating and adv… #
These committees help identify and mitigate ethical risks, promote best practices, and ensure that AI systems align with societal values and norms.
Data Governance #
Data Governance
Data governance in AI for public health involves the management and stewardship… #
Effective data governance practices include data collection, storage, sharing, and analysis while upholding ethical and legal standards.
Privacy #
Preserving AI
Privacy #
preserving AI techniques are methods that enable the analysis and modeling of health data while protecting individuals' privacy and confidentiality. These techniques, such as differential privacy and homomorphic encryption, help mitigate the risks of data breaches and unauthorized access in public health applications.
Fair AI #
Fair AI
Fair AI refers to the design and implementation of artificial intelligence syste… #
Fair AI algorithms aim to reduce bias, promote diversity, and ensure that all individuals receive equal treatment and opportunities in public health settings.
Explainable AI #
Explainable AI
Explainable AI (XAI) is an approach that aims to make the decision #
making processes of AI systems transparent and interpretable to users and stakeholders. XAI techniques help explain how AI models arrive at specific predictions or recommendations, enabling accountability and trust in public health applications.
Model Bias #
Model Bias
Model bias refers to the inherent prejudices or inaccuracies present in machine… #
Model bias can result from biased training data, flawed algorithms, or inadequate validation procedures, leading to unreliable or discriminatory outcomes.
Ethical AI Design #
Ethical AI Design
Ethical AI design involves integrating ethical considerations and principles int… #
By incorporating ethics from the outset, AI designers can proactively address bias, fairness, transparency, and accountability to ensure ethical and responsible AI systems.
Responsible AI #
Responsible AI
Responsible AI refers to the ethical and accountable use of artificial intellige… #
Responsible AI practices encompass ethical design, data governance, transparency, and stakeholder engagement to ensure that AI systems serve the public good.
AI for Social Good #
AI for Social Good
AI for social good initiatives leverage artificial intelligence technologies to… #
These initiatives aim to create positive impact, promote equity, and improve the well-being of individuals and communities through innovative AI solutions.
Public Health Ethics #
Public Health Ethics
Public health ethics are the moral principles and values that guide decision #
making and actions in the field of public health. Public health ethics consider the impact of policies, interventions, and technologies on population health, equity, justice, and individual rights to promote the common good.
Public Health Surveillance #
Public Health Surveillance
Public health surveillance involves the systematic collection, analysis, and int… #
Surveillance data inform public health decision-making, response strategies, and interventions to prevent and control diseases, injuries, and other health threats.
Health Equity #
Health Equity
Health equity is the principle of ensuring that all individuals have the opportu… #
Addressing health equity involves reducing disparities, promoting access to healthcare, and addressing social determinants of health.
Health Disparities #
Health Disparities
Health disparities refer to differences in health outcomes and access to healthc… #
Addressing health disparities is essential for promoting health equity and reducing inequities in public health.
Social Determinants of Health #
Social Determinants of Health
Social determinants of health are the social, economic, and environmental factor… #
These determinants, such as income, education, housing, and access to healthcare, play a significant role in shaping health disparities and inequities in public health.
Algorithmic Accountability #
Algorithmic Accountability
Algorithmic accountability refers to the responsibility of developers, users, an… #
Promoting algorithmic accountability helps prevent harm, mitigate biases, and uphold ethical standards in public health applications.
Ethical Challenges in AI for Public Health #
Ethical Challenges in AI for Public Health
Bias Detection and Mitigation #
Bias Detection and Mitigation
Bias detection and mitigation techniques are methods used to identify and addres… #
These techniques include data preprocessing, algorithmic adjustments, fairness metrics, and bias audits to reduce the impact of bias on decision-making processes and outcomes.
Interpretable Machine Learning #
Interpretable Machine Learning
Interpretable machine learning techniques enable users to understand and interpr… #
By providing explanations and insights into model outputs, interpretable machine learning helps build trust, facilitate decision-making, and identify potential biases or errors.
AI Transparency Tools #
AI Transparency Tools
AI transparency tools are software applications and resources designed to enhanc… #
These tools provide visibility into AI processes, data sources, algorithms, and outcomes to help users understand, evaluate, and validate the performance of AI technologies.
AI Bias Assessment #
AI Bias Assessment
AI bias assessment involves evaluating the fairness, accuracy, and equity of art… #
Bias assessment tools, metrics, and frameworks help identify, quantify, and address biases in AI systems to ensure that they do not perpetuate discrimination or harm vulnerable populations.
Model Validation and Testing #
Model Validation and Testing
Model validation and testing are essential processes for evaluating the performa… #
By validating models against diverse datasets, testing for biases, and assessing predictive accuracy, developers can ensure that AI systems produce trustworthy and ethical results.
Explainability vs. Accuracy Trade #
off
The explainability vs. accuracy trade #
off in AI for public health refers to the challenge of balancing the interpretability of AI models with their predictive performance. While explainable AI techniques provide transparency and insights into model decisions, they may sacrifice some level of accuracy or complexity in exchange for clarity and trustworthiness.
AI Data Governance Framework #
AI Data Governance Framework
An AI data governance framework outlines the policies, procedures, and guideline… #
This framework includes data collection, storage, sharing, access controls, privacy protections, and ethical considerations to ensure the responsible and secure use of data.
Ethical AI Decision #
making
Ethical AI decision #
making involves applying ethical principles, values, and considerations to guide the development, deployment, and evaluation of artificial intelligence technologies in public health. By incorporating ethical decision-making processes, stakeholders can promote fairness, transparency, and accountability in AI systems to protect public health and well-being.
Health Data Privacy Regulations #
Health Data Privacy Regulations
Health data privacy regulations are laws and policies that govern the collection… #
These regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, set standards for data security, confidentiality, and consent to protect individuals' privacy rights.
AI Ethics Training #
AI Ethics Training
AI ethics training provides education and awareness on ethical principles, best… #
Training programs help stakeholders understand the ethical implications of AI, recognize biases, and make informed decisions to ensure ethical and accountable AI systems.
Ethical AI Impact Assessment #
Ethical AI Impact Assessment
Ethical AI impact assessment involves evaluating the potential ethical, social,… #
Impact assessments help identify risks, benefits, and unintended consequences of AI interventions to inform decision-making, policy development, and risk mitigation strategies.
AI Bias Reporting Mechanisms #
AI Bias Reporting Mechanisms
AI bias reporting mechanisms are channels and processes for stakeholders to repo… #
These mechanisms enable transparency, accountability, and oversight to monitor and mitigate biases, discrimination, and ethical violations in AI applications.
AI Accountability Framework #
AI Accountability Framework
An AI accountability framework outlines the responsibilities, obligations, and m… #
This framework includes governance structures, compliance measures, ethical guidelines, and enforcement mechanisms to ensure transparency, fairness, and trust in AI systems.
AI Governance Policies #
AI Governance Policies
AI governance policies are rules, procedures, and guidelines that govern the dev… #
These policies address issues such as data privacy, transparency, bias mitigation, accountability, and stakeholder engagement to ensure ethical and responsible AI practices.
AI Transparency Requirements #
AI Transparency Requirements
AI transparency requirements are standards and expectations for making artificia… #
These requirements include disclosing data sources, algorithms, decision-making processes, and outcomes to enable users and stakeholders to understand, evaluate, and trust AI technologies.
AI Fairness Metrics #
AI Fairness Metrics
AI fairness metrics are quantitative measures used to assess the fairness, equit… #
These metrics evaluate the distribution of outcomes, impacts on different demographic groups, and disparities in decision-making to identify and address biases, discrimination, and inequities in AI systems.
AI Bias Correction Techniques #
AI Bias Correction Techniques
AI bias correction techniques are methods used to mitigate biases in artificial… #
These techniques include data sampling, feature engineering, algorithm adjustments, bias-aware training, and post-processing methods to reduce the impact of bias on AI decision-making and outcomes.
Ethical AI Guidelines #
Ethical AI Guidelines
Ethical AI guidelines provide principles, recommendations, and best practices fo… #
These guidelines address issues such as bias mitigation, transparency, accountability, privacy, and fairness to promote responsible and ethical AI practices.
AI Bias Detection Tools #
AI Bias Detection Tools
AI bias detection tools are software applications and resources designed to iden… #
These tools use statistical analyses, visualization techniques, and fairness metrics to detect and mitigate biases, discrimination, and inequities in AI systems.
AI Ethics Framework #
AI Ethics Framework
An AI ethics framework is a structured approach that outlines the ethical princi… #
This framework includes ethical guidelines, governance structures, accountability mechanisms, and stakeholder engagement strategies to ensure responsible and ethical AI practices.
AI Bias Prevention Strategies #
AI Bias Prevention Strategies
AI bias prevention strategies are proactive measures taken to reduce, prevent, a… #
These strategies include diversity in data collection, algorithmic transparency, bias-aware training, fairness constraints, and bias impact assessments to ensure that AI systems produce equitable and unbiased outcomes.
Ethical AI Use Cases #
Ethical AI Use Cases
Ethical AI use cases are examples of how artificial intelligence technologies ca… #
These use cases include disease surveillance, predictive modeling, personalized medicine, health interventions, and decision support systems that prioritize fairness, transparency, and accountability.
AI Governance Principles #
AI Governance Principles
AI governance principles are foundational values, standards, and guidelines that… #
These principles include transparency, accountability, fairness, privacy, and security to ensure that AI systems align with ethical standards, legal requirements, and societal expectations.
AI Ethics Compliance #
AI Ethics Compliance
AI ethics compliance involves adhering to ethical principles, guidelines, and re… #
Compliance measures ensure that AI systems meet ethical standards, promote transparency, mitigate biases, protect privacy, and uphold the rights and well-being of individuals and communities.
AI Bias Impact Assessment #
AI Bias Impact Assessment
AI bias impact assessment involves evaluating the potential effects, consequence… #
Impact assessments help quantify harms, identify vulnerable populations, and inform bias mitigation strategies to ensure that AI systems produce equitable, fair, and unbiased outcomes.
AI Transparency Mechanisms #
AI Transparency Mechanisms
AI transparency mechanisms are processes, tools, and practices that enhance the… #
These mechanisms include audit trails, model documentation, explanation interfaces, user interfaces, and interpretability tools to enable users and stakeholders to understand, trust, and validate AI technologies.
Ethical AI Decision Support #
Ethical AI Decision Support
Ethical AI decision support provides guidance, recommendations, and insights on… #
Decision support tools help stakeholders navigate ethical dilemmas, address biases, and make informed decisions to ensure that AI systems align with ethical principles and societal values.
AI Bias Awareness Training #
AI Bias Awareness Training
AI bias awareness training educates developers, users, and stakeholders on the r… #
Training programs raise awareness about bias detection, mitigation techniques, fairness metrics, and ethical considerations to promote responsible and ethical AI practices that prioritize equity, transparency, and accountability.
AI Privacy Protection Measures #
AI Privacy Protection Measures
AI privacy protection measures are safeguards, controls, and protocols implement… #
These measures include data encryption, access controls, consent mechanisms, data anonymization, and privacy-preserving techniques to protect individuals' privacy rights and prevent unauthorized access or disclosure of sensitive data.
AI Ethics Oversight Board #
AI Ethics Oversight Board
An AI ethics oversight board is a governing body responsible for evaluating, mon… #
Oversight boards review AI projects, assess compliance with ethical guidelines, address ethical concerns, and provide recommendations to ensure that AI systems align with ethical standards, legal requirements, and societal values.
AI Bias Detection Framework #
AI Bias Detection Framework
An AI bias detection framework is a structured approach that outlines the method… #
Bias detection frameworks include data preprocessing, bias metrics, fairness evaluations, and bias impact assessments to mitigate biases, discrimination, and inequities in AI systems.
Ethical AI Risk Assessment #
Ethical AI Risk Assessment
Ethical AI risk assessment involves evaluating the potential ethical, social, an… #
Risk assessments help