Safe and Ethical AI: A simple approach for healthcare AI product development

Updated on April 25, 2024
Artificial intelligence, Healthcare, Robots in Healthcare, Healthcare Technology

Artificial intelligence (AI) is everywhere these days, changing how we live and work. It’s incredibly powerful, but with that power comes a big responsibility to use it wisely. That’s why we need a strong framework to make sure AI is developed and used in ways that are safe, fair, transparent, and protect our privacy. Here’s a look at the six principles to keep in mind when designing and developing a healthcare AI product:

Transparency: Building Trust Through Understanding

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Think of transparency as the key to trusting AI. If you don’t understand how something works, how can you have confidence in it? Here’s how we can make AI easier to understand:

  • Explainable AI: When AI gives us an answer or recommendation, it should also be able to explain how it got there. Think of it like your friend explaining why they like a particular movie – they might mention the actors, the storyline, or the cool special effects.
  • Clear and Simple: Explanations need to be easy for everyone to understand, not just the tech experts. Imagine trying to explain complex technology to your grandparents – you need to break it down into simple terms.
  • The “How” and “Why:” Don’t just tell us what the AI decided, tell us how it reached that decision and what information it used to get there.

Below table presents different explanation types that could be used in the development of healthcare AI products.

Explanation TypeDescription
Feature ImportanceHighlights the most influential input variables
Decision TreesVisualizes the decision-making process as a series of branches
CounterfactualsShows how changing an input variable would impact the outcome

Fairness: Making Sure AI Plays by the Rules

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Just like humans, AI can sometimes have biases built into it. We need to make sure our healthcare AI product treats everyone fairly, regardless of things like race, gender, or background. Here’s how:

  • Fighting Hidden Bias: AI learns from data, and if that data is biased, then the AI might be too. We need to constantly check for bias and find ways to root it out.
  • Level Playing Field: AI should give everyone the same opportunities, no matter who they are. It shouldn’t hold anyone back or unfairly favor one group over another.
  • Humans in the Loop: Especially for important decisions, having a person double-check the AI’s work helps prevent accidental unfairness. Plus, people can provide feedback so we know when the AI might be slipping up.

Establishing Fairness Metrics is essential when evaluating AI. The below table outlines a few different fairness metrics that can be used to evaluate AI models:

Disparate ImpactMeasures the ratio of positive outcomes between protected and unprotected groupsMay not detect bias if overall accuracy is low
Equalized OddsEnsures equal probability of true positives and false positives across groupsRequires careful selection of a target outcome variable
Demographic ParityEnforces equal proportions of positive outcomes across groupsMay lead to less accurate models in some cases

Accountability: Taking Responsibility

The people and companies behind the healthcare AI products need to be accountable – that means owning up to mistakes and working to fix them. This is how we maintain trust:

  • An Ethical Compass: Clear rules and guidelines are crucial for everyone involved in creating and using AI. Think of this as a shared code of conduct that keeps us on the right path.
  • Who’s in Charge?: We need to know who’s responsible for AI systems, especially if something goes wrong. This isn’t about blaming individuals, but making sure there’s someone looking out for potential problems and finding solutions.
  • Learning from Mistakes: Things won’t always be perfect. We need to be able to investigate when AI makes mistakes, understand what went wrong, and make sure it doesn’t happen again.

Establishing clear roles and responsibilities can ensure accounatblity and trust. The below table gives an example of the roles and responsibilities of key stakeholders:

AI Ethics CommitteeOversees ethical AI development, reviews policy, resolves issues
Data ScientistsImplements bias mitigation, ensures explainability, documents processes
Compliance TeamConducts regular audits, tracks regulatory changes, enforces guidelines
Business LeadersEnsures alignment with ethical principles, provides resources, champions responsible AI

Privacy and Security: Protecting What’s Yours

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Personal information is precious. Our healthcare AI product shouldn’t overstep its bounds and collect more data than it needs, or use it in ways we don’t agree to. Here’s how we protect privacy:

  • Privacy Up Front: Privacy can’t be an afterthought – it needs to be baked into how AI is designed from the beginning.
  • Need-to-Know Basis: AI should only collect the absolute minimum data necessary to do its job. It doesn’t need to know your whole life story to recommend a new pair of shoes!
  • You’re in Control: We need to have a meaningful say in how our data is collected, used, and shared. Opt-in options, clear explanations, and the ability to change your preferences are key.

The below table summarizes a few techniques that could be deployed to protect privacy at different stages of the AI lifecycle:

Data CollectionInformed consent, anonymization, differential privacy
Model DevelopmentFederated learning, synthetic data generation
DeploymentAccess controls, encryption, regular security audits

Sustainability: Thinking About the Long Run

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AI uses a lot of energy and resources. We need to think about how it affects the environment, both now and in the future:

  • Energy Smart: Training giant AI models takes massive amounts of power. Let’s figure out how to create efficient AI systems, use renewable energy, and find ways to reduce the footprint.
  • Resource Conscious: From the hardware it runs on to the materials they’re made of, AI’s impact on the planet goes beyond just energy use.

Teamwork Makes the Dream Work

One of the most important things is that AI creators need diverse teams. People from all different backgrounds and perspectives bring unique insights that help us build AI that’s truly fair and inclusive for everyone. Adopting a Responsible AI framework is just a starting point and as technology improves and society changes, these guidelines will need to evolve as well. Building ethical AI requires everyone to work together with the commitment to make AI that is fair, equitable and safe for the world.

Ramakrishnan Neelakandan
Ramakrishnan Neelakandan
AI Quality and Safety at Google

Ramakrishnan Neelakandan is a seasoned professional in healthcare product Quality and Safety Engineering focused on AI for Healthcare. Ramakrishnan currently works for Google supporting healthcare technology development. More details about Ramakrishnan can be found in his profile.