While black box AI has become the leading technology in automatic decision-making, data-driven decisions might promote statistical biases, resulting in social biases. The lack of ethical and social considerations in AI development results in faulty software, causing the world to suffer from low-quality and unprofessional software products.
Explainable AI (XAI) is an emerging phenomenon shaping the way companies develop new machine learning algorithms. Basically, the company must be willing to reveal information, and it must be willing to reverse-engineer the root cause of an automatic decision.
More often than not, the industry offers uninformative explanations, but policymakers are debating the importance of offering explanations to customers, namely with the right to an explanation required by GDPR. In recent years, the field of XAI has seen a boost in interest from the community, with many new approaches and ideas. Most of the effort is being handled from the technological and algorithmic front, encouraging other domains such as statistics and game theory and striving to develop opaque models — or models that are more interpretable by nature.
One of the key components in XAI platforms or services is reporting feature importance in a clear, aggregated way. Let’s imagine that you serve as a consultant company, and you’re asked to provide an estimation of your customer cash flow for next year. Equipped with machine learning tools, you decide to analyze the provided data with practiced data science techniques. Next, you’re asked to explain to your customer, in simple words, how you’ve reached these conclusions and what factors you took into consideration.
Becoming an XAI company requires that every model that impacts an automatically made decision regarding customers can be explained internally first. In other words, data scientists who develop these AI models should know why and how these decisions have been made, what contributed to these predictions, and how redesigning the logic could impact the end result. With that said, this concept of awareness allows professionals to become ethically predispositioned and able to handle dilemmas with better tools.
As professional data scientists take on more global missions and contribute to new domains, the importance of validating statistical models from a quality aspect is a major task, and that’s where XAI serves as an ethical tool.
Let’s take AI biases as an example: Most biases happen as a result of statistical relations, creating classified groups based on common irrelevant factors in some cases. These segmentations end up promoting unconscious discrimination against or assumptions about certain social groups. Some of these relationships act as important contributors to the informed decisions made by machines and can be discovered prior to releasing new algorithms using XAI services as a validation phase. This helps professionals take advantage of the option to redesign these algorithms in order to mitigate and prevent potentially unethical results.
Ethical tools and considerations should still focus on the original idea or goal at hand, and they should not place limitations on the project. Rather, they should promote freedom and open dialogues among involved parties.
In order to implement these ethical standards, organizations can start by communicating company values and social commitments to their employees and consumers — all in the name of driving development standards. These standards can serve as key guidelines for launching professional software products and services, and they should be recognized as part of the development process. XAI achieves the initial step of bias awareness, but it should always be followed by conscious redesigns as needed to bolster the effectiveness of a solution overall.