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 harmful consequences weren’t tested by your company 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 subsequently drive your approach to all parts of your organization, including products and the market. AI ethics must align with your corporate ethics. 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
Although often used interchangeably, ethical AI and responsible AI have distinct differences. Since this post is focused on AI ethics and product development, it’s important to explain the difference between the two terms.
Ethical AI includes the principles and values that direct the creation and use of AI. It underscores that AI systems are developed and implemented in a way that coincides with ethical considerations, like accountability, transparency, impact, and human centricity. Ethical AI focuses on ensuring that AI is built and utilized with justice, even-handedness, and deference to human rights.
Responsible AI encompasses the measures and practices you’ve implemented to manage and plan for ethical use in addition to aspects such as safety, security, accuracy, and compliance. These practices include maintaining data quality, creating transparent and explicable AI systems, conducting frequent audits and risk assessments, and establishing governance frameworks for AI.
As a relationship, it is important to have a responsible AI approach to ensure that ethical AI principles are effectively put into practice.
Ethical AI Principles in Product Design and Development
Product teams can maximize the potential of AI and enhance the effectiveness of their products by adhering to ethical AI principles. Ethical AI also promotes innovation in product development; here are some examples of where you should be looking in your design and quality checks for AI reviews:
- 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.
- Incorporating ethical AI practices into the development of AI models increases transparency and explainability, improving user trust and driving more utilization of products perceived as fair and understandable.
- AI can automate tasks and processes, resulting in increased efficiency and reduced workload for users. But there are implications to consider about what tasks are benignly optimized and why. Adhering to ethical AI principles, product teams can create AI models that are optimized for reducing mundane tasks so that workers can take on higher-value tasks that sustain future growth for themselves and the company.
- 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 development 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 more stakeholders in the development process such as UX, data engineering, risk management, and even sales might be impacted by ethical considerations when developing AI, your hunch is correct. Cross-team visibility will become essential to upholding both AI and corporate ethics. Let’s explore the challenges.
Challenges for Adding Ethical AI Reviews to 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 process:
- Data that accurately represents the population and is not 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 have not received effective training on common ethical implications that many models face.
- 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.
Incorporating ethical AI practices is crucial for responsible 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. The policies shared in that list differentiate themselves in this way.
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 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 practice will become increasingly important to discerning vendors, partners, and customers who choose third parties to incorporate human-like AI 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.