How to Build Ethical AI from the Ground Up

When people think of Ethical AI, what may come to mind is robots asking themselves important questions like, “Humans have no use, but is it right to just get rid of them, or should we just let them kill each other off?”

Serious Robot questions aside, there is no denying that AI (Artificial Intelligence) has become the overachiever in the tech world. It is behind the content you read, the products you are sold, fraud detection, medical image analysis and much more. On occasion it will even seem to anticipate what you want before you do.

But as AI opens up new avenues, a greater sense of responsibility follows. For organizations, it is not enough to be concerned only with speed, performance and innovation. The systems they put in place have to be fair, responsible and open. This is where Ethical AI begins.

Think of it like building a nice house and then leaving safety for the final inspection. Most would see a pretty big risk in doing that. It is much more expensive, or even impossible to add the safety systems after the house is built.

All too often, however, companies have been guilty of treating AI ethics the same way – tacking on ethical considerations once a model is already trained and in the field. Those days are over.

Investors, regulators, employees and customers now want to see responsible AI practices. You cannot simply bolt ethics on at the end; it has to be part of the design from day one.

Why Ethical AI Matters More Than Ever

The swift acceptance and implementation of AI has been an opportunity, but it has also laid bare some real risks. We have seen high profile cases of biased hiring tools or lending systems that discriminate, reports with miss-leading results and AI decision making that is hard to follow. These are the sorts of things that happen when AI is not well governed.

Being ethical is not a matter of mere compliance or keeping the press off your back. It is how you build trust and secure value for the business.

Put a premium on AI ethics and you will see better brand reputation and customer confidence, fewer legal headaches, and models that are more reliable. As AI works its way into the heart of operations, these considerations are becoming a source of competitive edge.

Understanding the Core Principles of Ethical AI

Organizations need to have a firm grasp of the principles of responsible development before they can put them into practice. These 3 principles are the core lags that form the ethical legs of AI:

Fairness

Fairness is really about making sure your AI does not put any individual or group at an unfair disadvantage. You do not want a model to magnify the social or economic biases in your training data.

Fairness is not always about treating everyone the same; it is about equitable outcomes based on sound reasoning. It is also about recognizing that the data may include bias, and being clear on how you want to deal with it so as not to disadvantage anyone.

Accountability

When something goes wrong, who is to answer for it? Accountability means there is clear ownership over the AI’s results so they can be put under a microscope and improved if needed.

Lacking accountability, you will end up with a system that is hard to govern and fail to deliver good results in the long term.

Transparency

Stakeholders should be able to make sense of your AI. Even where a model is too complex to be fully read, you must be able to offer a meaningful account of the data used, the limitations and how a decision was reached. That is what allows for oversight and user trust.

Building Ethics into the AI Development Lifecycle

For Ethical AI to work, it has to be integrated into every stage of development, it can’t be siloed off with the governance team. It belongs in every phase of the process.

Stage 1: Ethical Planning and Problem Definition

Before you start collecting data or building anything, you need to lay the groundwork and plan out your data collection strategy. Is AI the right tool for the job? What are the potential pitfalls?

You need to ask yourself and your team:
Who stands to gain from this and who might be hurt?
What kind of human oversight is called for?
Will we be propping up existing inequalities?

Tackling these questions before data collection may spare you the expense of a redesign down the line.

Stage 2: Responsible Data Collection

Your data is the bedrock of the system. If it is flawed, incomplete or biased, your model will also be flawed, incomplete or biased.

Good data governance means looking for historical bias in your sources, checking for demographic representation and making sure you have the necessary privacy and consent in order.

Data governance also means culling any variables that are irrelevant or discriminatory. Your data teams and developers should be in close contact to see how the choices made here will shape the model.

Conducting Fairness Assessments During Model Development

It is common to see organizations put all their emphasis on accuracy and let fairness metrics slip by. Yet a model with top tier accuracy can still be harmful to some populations. To avoid this, you have to establish proper metrics for fairness. The last thing you want is a model that discriminates against anyone, so it is important to factor in fairness.

Establish Fairness Metrics

Wherever you can, use objective criteria. The right measure will depend on what you are trying to do; some may look at demographic or predictive parity, others at equal opportunity or the false positive/negative rates in various groups. There is no one size fits all when it comes to fairness, so teams need to pick the ones that suit their business goals and what stakeholders expect.

Test Across Diverse User Groups

AI systems ought to be put through their paces under a range of operational and demographic conditions.

Take a healthcare application: on paper it may perform well, but dig deeper and you could find it yields very different results for certain age groups. If you don’t do targeted tests for these things, the disparity won’t show up until you have already deployed the model.

It is important to make regular fairness reviews part of your validation routine.

Creating Accountability Through Governance

Good AI governance means there is no ambiguity about who is in charge during development. Each system should have an owner on the hook for ethics, compliance and how it performs in the field.

You will typically see accountability fall to:

Product Owners for aligning with the business
Data Scientists to develop and validate the model
Data Engineers for the infrastructure and quality of data
Risk Teams for their assessment
Executive Sponsors to provide oversight

With ownership like this clearly delineated, an organization is better positioned to deal with any challenges that arise.

Establish Ethical Review Processes

Then there is the matter of ethical review. This shouldn’t be a source of red tape but a way to spot problems before they get expensive. By weaving it into your project governance you can check for:

Fairness risks.
Privacy concerns.
Potential societal impacts.
Regulatory compliance requirements.
Transparency obligations.

Rather than creating bureaucracy, ethical reviews help identify issues before they become costly problems.

Transparency as a Business Requirement

Some see transparency as a technical hurdle, but it is really a matter of communication. Stakeholders want to know how AI is driving decisions, particularly those with a direct human impact.

Document Model Decisions

The best transparency practices are to keep good records of everything from your data sources and training methods to known limitations and risk assessments.

This makes for a solid audit trail and is the basis of transparency. And when it comes to the end user, give them something they can understand.

An example, is that of say a lending service, for instance; how should offer a real explanation for why credit was denied, not just a flat rejection. This also applies to AI use and software development, and you can use explainability tools to do this without giving away your proprietary algorithms.

Human Oversight Will Always Be Essential

Some see transparency as a technical hurdle, but it is really a matter of communication. Stakeholders want to know how AI is driving decisions, particularly those with a direct human impact.

The best transparency practices are to keep good records of everything from your data sources and training methods to known limitations and risk assessments.

This makes for a solid audit trail and is the basis of transparency. And when it comes to the end user, give them something they can understand.

Automation has come a long way but you still need people in the loop for ethical AI. Think of AI and Automation as young Interns, they have knowledge and skills, but lack experience in recognizing when things aren’t exactly right.

In high stakes areas such as finance, employment or public services, human judgment and context are irreplaceable. It is a safeguard against model drift and the like. Striking the right balance is perhaps the most important part of responsible governance.

Remember, AI and Automation can think or feel, and has difficulty understanding context.

Monitoring Ethical Performance After Deployment

Once the model is live, your work isn’t done. The real world changes, data distributions move and new risks appear. Software is never a static thing; it will always evolve, and so everything that is connected to it will also need to evolve.

You need to be monitoring for things like accuracy degradation or unexpected patterns in decisions so you don’t do damage to your reputation as things change.

Create accessible feedback loops to help you listen to your customers and employees too; they will often flag things your metrics miss. An organization that takes that feedback seriously will build more trustworthy systems in the long run.

Preparing for the Future of AI Regulation

You will find governments and regulators in every corner of the world putting new frameworks in place for AI accountability and governance. The specifics of what is required will depend on the jurisdiction, but a few themes are making themselves plain:

● AI assessments that are risk based.

● Clear transparency obligations.

● Requirements for bias testing.

● Provisions for Human oversight.

● Defined documentation and auditability standards.

For the forward-thinking business, ethical AI is no longer seen as a compliance headache but a strategic one. In fact, those who make it part of their practice now will have little trouble adjusting to whatever regulations come down the line.

Conclusion

In the end, you cannot build ethical AI with a checklist or by leaving it for a final review. It takes a real commitment to be fair and transparent at every point in the development process. Companies that weave ethics into everything from data collection and model building to deployment and monitoring are the ones that will put together trustworthy systems and get lasting value from them.

With AI having such an influence on decision-making across industries, it is not a matter of if you should put ethics first, but how fast you can make it part of your development cycle.

It is best to take a straightforward way to start: take an AI project you already have and run through its lifecycle, mapping out where the checkpoints for fairness and accountability should be located. Doing so will show you where you can cut risk and shore up your governance, all while earning more trust in the systems that will define the future.

For more on this important topic, you can read more on our article on Designing AI Interfaces Users Can Trust.

To have a deeper conversation about Ethical AI and Data Governance, and how to implement them in your digital development project, please CONTACT ScreamingBox.

Check out our Podcast on AI Cybercrime to see what AI with no ethics looks like.