AI ethics and innovation for product development

Ethics & Responsibility
Impact & Society

AI ethics are a factor in responsible product development, innovation, company growth, and customer satisfaction. However, the review cycles to assess ethical standards in an environment of rapid innovation creates friction among teams. Companies often err on getting their latest AI product in front of customers to get early feedback.

But what if that feedback is so great and users want more? Now.

In later iterations, your team discovers that the algorithms and use cases that have been perpetuated are enabling misinformation or harmful outcomes for customers. At this point, your product leaders also know that taking figurative candy away from a baby incites a tantrum. Even if they retract the product, customers will demand to know why your company didn't test harmful consequences before releasing the product. It is a scenario that puts the reputations of both you and your customers at stake.

Historically, your corporate ethics, standards, and practices have driven your approach to all parts of your organization, including products and the market. AI ethics must align with your corporate ethics. Further, development processes and systems need to have steps to examine and mitigate questionable outcomes from your AI. The following guidance can help you assess where to adjust your product development and design thinking to make ethical AI an enabler of awesome products your customers will trust and love.

The difference between ethical AI and responsible AI

It's important to understand that ethical AI and responsible AI are not the same thing. Although they are related, they have distinct meanings. Fortunately, grasping this concept is not overly complicated, despite the complexity of artificial intelligence.

Ethical AI refers to the standards, principles, and decisions behind the development of AI. It involves considering the purpose, impact, and alignment with corporate ethics and standards. Understanding the impact of AI goes beyond intentions and depends on how it is perceived by end users, industries, societies, and global interests. When developing a new AI product, it is crucial to assess potential risks, biases, and privacy concerns, while promoting fairness and inclusivity. By adhering to ethical principles from the start, organizations can build trust with users, stakeholders, and the community. Taking ethical considerations into account early on also contributes to the long-term sustainability and success of AI products and businesses.

Responsible AI, on the other hand, involves the implementation and adherence to practices, processes, and frameworks that uphold ethical AI principles. In other words, you’ve already considered the ethics, intentions, and risks of what you are building; and what it takes to align cross-functionally and mitigate them throughout the process. It means following through with what you say you will do and applying it throughout the organization, across all business functions, and involving every employee who plays a role in developing, implementing, and using AI.

However, many challenges faced by product owners in relation to AI arise during the ideation phase. If you find yourself questioning the reasons behind specific features or functions, as well as why AI is being used, you may be facing an ethical or directional dilemma, or both. The remainder of this article will focus on identifying ethical challenges, ideas for overcoming them, and getting to the ultimate goal: building AI responsibly.

Ethical AI principles in product design and development

Product teams can maximize the potential of AI and enhance the effectiveness of their products while also adhering to ethical AI principles. Ethical AI also promotes innovation in product development, and here are some examples of where you should be looking in your roadmap to see if your processes align with your strategy:

  • Developing AI models with representative and unbiased data leads to increased accuracy and fairness in predicting outcomes and making decisions, resulting in more effective products that meet the needs of a broader set of users. Consider appropriate use cases for data management or synthetic data.
  • Incorporating ethical AI practices into the development of AI models increases transparency and explainability, improving user trust and driving more use of products perceived as fair and understandable.
  • Ethical AI principles offer product teams the chance to explore novel opportunities and assess use cases for AI. By crafting AI models that are transparent, explainable, and fair, product teams can demonstrate the value of their AI before it impacts customers and society.

Adhering to ethical AI principles during early design and development phases allows for the creation of AI models that align with core societal values and fulfill business objectives. The effort to improve product accuracy, effectiveness, and user-friendliness for all stakeholders within an ethical framework enables product teams to leverage the potential of AI fully.

Also, if it sounds like ethical considerations might impact extended groups of stakeholders such as UX, data engineering, risk management, and even sales when developing AI, your hunch is correct. Cross-team visibility will become essential to upholding AI to you corporate ethics and standards.

Sounds good, right? Alas, let's explore where the challenges often occur.

Challenges for incorporating ethical AI principles into products

Incorporating ethical AI principles into product development is essential for responsible and trustworthy AI applications. However, the following challenges and objections might arise during the multiple stages of the roadmap and during development:

  • Data that accurately represents the population and isn't biased may not be available. Biased data can cause discriminatory and unjust outcomes when AI models perpetuate or amplify existing biases.
  • Transparency is key to ethical AI practices, but achieving alignment across teams can be tough. Without designing for interpretability, AI models will lack transparency, which can hinder understanding of decision-making processes when issues arise and time to correct model behavior is critical.
  • Likewise, a lack of transparency combined with disagreement on ethical policies can also slow down the speed of development. Early warning signs occur when stakeholders feel ethical principles are an unnecessary layer of planning not required during objective data-oriented model development.
  • AI models can pose challenges in identifying and addressing emergent ethical concerns, especially when product teams haven't received effective training on common ethical implications that many models face. Or, they understand the premise but don't have the information or visibility to be proactive about remedies.
  • The absence of authoritative ethical standards for AI and technology use more broadly within companies poses challenges for product teams in determining what practices are considered ethical and responsible. Conversely, this can also be a sign that your organization lacks the diversity of thought or experience to consider ethical policies and safeguards.

As ethical challenges are identified and resolved, teams can incorporate critical steps into responsible AI practices and processes that support ethical and trustworthy AI development. For many of the challenges, AI governance software advancements allow companies to govern, monitor, and audit models continuously, providing right-time evidence and documentation that demonstrates AI safety and compliance to various stakeholders.

Companies that prioritize ethical AI principles

Remembering our distinctions above between ethical AI and responsible AI, your AI ethics should be aligned with your corporate ethics, standards, and practices. If you have ESG policies, seek alignment between those and your AI. Do not view AI in isolation from broader societal values your organization has or is developing.

9 ethical AI principles for organizations to follow

Regulated industries such as banking and insurance are familiar with assessing the performance, robustness, and compliance of their algorithms and models against standards and controls. They have been doing it for decades. Rapid innovation and AI have forced these industries to streamline and automate these processes to explain their machine learning and AI continuously for compliance with industry standards.

Some Ai-led insurtechs are going as far as to publicly share their audit process and timing. This is a practice that will become increasingly important to discerning vendors, partners, and customers who choose 3rd-parties to incorporate Ai-assisted experiences in their products and want to do it ethically and responsibly.

Customers decide on ethics and trust

Your company and your customers have core business ethics to adhere to and uphold. With proper consideration, your ethics for developing and implementing AI will follow. 

By matriculating ethical AI principles into your core product strategy, your company can build immediate trust with end users and customers. Leading ethically with AI also ensures that you are building products that don’t become distrusted, misused, or worse, unsafe tools on a customer’s shelf.

Originally published in Dataversity, May 3, 2023. Updated here on February 23, 2024 for recent events.