Building an Ethical & Inclusive Product
Technology is spearheading a significant shift in human interaction and products are reaching an increasingly diverse audience. As our reliance on technology grows, the stakes are even higher for a fair and responsible product experience. Product managers and designers are the voice of the user and play a significant role to shape the future. While ethics and inclusion may be applied differently based on the product vision, target market, business goals, and technology strategy; the core principles remain the same. In every organization, trust and inclusiveness should be a core part of product and design principles and product managers should feel empowered to say NO to features that don’t align with these values. These are the 5 real-world approaches that can help you build an ethical and inclusive product
Design with accessibility & flexibility
Are you building a product for mass adoption or a product that is one-size-fits-all? Designing your products with user diversity in mind is the first step to build inclusive products and hence accessibility & flexibility is a crucial factor. Assuming that all the users access and use your product the same way leads designers to build features for a singular user experience. Ensure that the product works for older user groups or people with different physical, vision, or cognitive abilities. Some of the ways you can make your products more approachable are applying variable font sizes, color contrasts, captions & audio controls, and exploring different interaction modality options from touch, voice, keyboard et all. You can uncover a lot of accessibility & flexibility issues by researching user personas, monitoring usability metrics across different user groups, including people from different age groups, and physical abilities during user testing. Creating a design that is more inclusive leads to equitable access to technology and more innovative products.
Uncover and mitigate potential bias in your product
For the product folks building or experimenting with artificial intelligence – AI Bias is your day 1 problem. Machine learning platforms have biases just like humans. Bias with an accent, bias with language, bias with understanding cultures, et all. There are several recent press releases on AI algorithms going wrong: face recognition systems have higher inaccuracy for women or people with darker skin tone, financial AI systems show gender bias when approving loans, or deciding credit limit, and the list goes on. Biased data or biased algorithms that are not representative of diversity and cultures can lead your AI system to learn the wrong lessons, setup failed expectations, and even worse – make unfair decisions. I learned this the hard way. One of the very first AI products, I built was a voice assistant for conference room devices. While demoing the early prototype where you can use the assistant to call someone by name, we realized that the assistant failed miserably to recognize non-native English names. Product managers should start with a clear definition of user personas and use cases that help create a training data strategy, setup robust user testing for all use cases, and build awareness among data scientists to uncover algorithmic bias sooner than later. Since AI is not a magic bullet; the product should be designed with fallback options and human in the loop experiences to account for scenarios when AI fails.
Build a personalized experience with a privacy-first approach
Going hand in hand with AI is the next wave of engagement and connected customer experience – “hyper-personalization”. With real-time behavioral data and myriad touchpoints, brands are building connected and targeted experiences for customers so they can interact and transact where, when, and how they want. In pursuit of building an individualized experience sometimes privacy and data protection take a back seat. How do you balance user privacy vs differentiated user experience? Product managers can play a key role in bringing mindfulness into the product strategy by taking data privacy and security first approach. Make it transparent for users on what data is collected, how it will be used, get explicit user approval, and offer the option to opt-out. Contrary to what you may assume, over personalization can sometimes be annoying to users. Product managers and designers should be doing ample user testing before going overboard thus creating a responsive and responsible design. You can increase loyalty and build a competitive moat if you keep the end user’s trust in the forefront.
Don’t get obsessed with engagement metric and rather focus on user satisfaction
Product led growth strategy or growth product management is the new wave driving customer acquisition and adoption for subscription-based SaaS businesses. Unlike traditional software companies, product-led growth companies focus on frictionless sign-up/onboarding, virality/network effect & user journey/value creation. It’s a no brainer that user adoption and engagement metrics are crucial for the success of these products. There are several approaches that companies are applying to increase stickiness from virality, loyalty, gamification to hook model. Sometimes products out in the market get too obsessed about user engagement and encourage addictive behavior that can negatively impact user wellness. We have seen several such examples in video games, social media, and streaming platforms that have come under scrutiny. The impact is not trivial and you can see the example when in 2018, the World Health Organization recognized “gaming disorder” or “video game addiction as a disease. The best way to avoid such pitfalls is to focus on user satisfaction and to build a positive user experience that drives value vs blindly chasing specific business metrics. Additionally, track the edge cases, differentiate between highly engaged vs an addicted user, and help them overcome the addiction.
Practice and evangelize a code of ethics and fair standards for your users
Another business model that has evolved fast in the last few years is the gig economy platforms and the list of success stories is huge from Uber, Lyft, Airbnb, to Kaggle and Upwork. As per the ADP report, the gig economy has grown by 15% over the last decade. On one hand, the gig economy platforms are giving people the flexibility and access to work; the industry is plagued with challenges like lack of minimum wage, overtime guidelines, and employee benefits. For product folks who are out there building a marketplace and gig platforms, please establish and evangelize a code of ethics and fair standards for all your users. Understandably, this is a more nuanced issue with labor laws, regulatory standards, and other factors in the mix. I built a crowdsourced platform for AI training data and we actively promote fair pay, inclusiveness, and well-being for the global workforce. As an example, we built smart pricing algorithms that recommend and guide users to pay fair wages based on the location of the gig worker and the complexity of the task.
Ethics and inclusiveness can and should be applied in a variety of creative ways irrespective of the product’s business model, problem space, target users, and technology stack. Product managers and designers play a key role in bridging the gap.