What are the best ways to ensure impartiality in AI algorithms?

1 Define the problem

The first step to ensure impartiality in AI algorithms is to clearly define the problem that the algorithm is trying to solve, and the criteria that it will use to measure success. This requires understanding the context, the stakeholders, the data sources, and the potential impacts of the algorithm. For example, if the problem is to predict the risk of recidivism for criminal offenders, the criteria should not be based on factors that are irrelevant or discriminatory, such as race, gender, or income.

3 Design and test algorithms

The third step is to design and test algorithms that are transparent, explainable, and accountable. Transparency means that the algorithm's logic, assumptions, and limitations are visible and understandable to the users and the affected parties. Explainability means that the algorithm can provide reasons for its decisions and actions, and how they relate to the problem and the criteria. Accountability means that the algorithm can be monitored, evaluated, and corrected if it causes harm or injustice. For example, if the algorithm is used to recommend products or services, it should disclose how and why it makes those recommendations, and allow users to give feedback or opt out.

4 Monitor and audit outcomes

The fourth step is to monitor and audit the outcomes of the algorithm, and measure its performance and impact. This involves collecting and analyzing data on how the algorithm behaves in different scenarios, how it affects different groups or individuals, and how it aligns with the ethical and legal standards. For example, if the algorithm is used to diagnose diseases, it should be checked for accuracy, reliability, and fairness, and compared with human experts or other methods.

6 Learn and improve

The sixth step is to learn and improve from the experience and the evidence, and to adapt and update the algorithm accordingly. This involves reviewing and revising the problem definition, the data collection and analysis, the algorithm design and testing, the outcome monitoring and auditing, and the stakeholder engagement and collaboration. For example, if the algorithm is used to optimize a business process, it should be flexible, scalable, and responsive to changing needs and conditions.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?