Are you practicing ethical AI? What to know before you scale

AI brings unprecedented opportunities to businesses but also an incredible responsibility. The output from AI systems has a tangible impact on people’s lives, raising questions around AI ethics, trust and legality. The more decisions a business puts into the hands of AI, the more they accept significant risks. These can include a host of reputational, employment/HR, data privacy, health and safety issues. 

Awareness of the potential hazards seems to have given organizational leaders cold feet when it comes to making the leap to AI. According to an Accenture global research study, 88% of respondents do not have confidence in AI-based decisions. 

So how do we create trust in AI?

Practicing responsible AI is the answer. Responsible AI is fundamentally human-centered. And, it’s carefully created with privacy, security, inclusion and fairness interwoven deeply throughout all algorithms. 

It’s not enough to just pay lip service to the idea of Responsible AI. We must live it in our business practices. For example, our research shows that 78% of executives want to use AI solutions to address barriers to disability inclusion over the next three years. Yet, only 32% say they’re embracing inclusive design principles that support fair and unbiased AI solutions. Executives must recognize the significance of inclusive design to AI to make real progress in improving disability inclusion. 

Designing, developing and deploying AI to empower employees and businesses—and to positively impact customers and society—allows companies to create trust and scale AI with confidence.  

Four keys to designing trust into your AI  

An interdisciplinary, innovation-friendly approach can help you weave responsibility directly into your AI from the start and tailor it to your business needs. 

Here are the four pillars of responsible AI: 

• Operational: Set up governance and systems that will enable AI to flourish. 
• Technical: Ensure systems, platforms and AI models are trustworthy and easy for all to understand by design. 
• Organizational: Democratize the new way of working and facilitate human + machine collaboration. Identify new and changing roles and see where you need to upskill, re-skill or hire employees. 
• Reputational: Articulate the responsible AI mission and ensure it’s anchored to your company’s values, ethical guardrails and accountability structure. 

Identify AI bias before you scale 

Central to implementing the pillars above is being sure to run an Algorithmic Assessment. This essential step comprises a multi-phase technical evaluation that identifies and addresses potential risks and unintended ramifications of AI systems across businesses to arouse trust and build support systems around AI decision-making. 

To prepare for the assessment, prioritize your use cases to ensure you are evaluating those that have the highest risk and impact. 

The assessment itself involves four key steps: 

1. Set goals around your fairness objectives for the system, considering different end-users. 
2. Measure & discover disparities in potential outcomes and sources of bias across various users or groups. 
3. Mitigate any undesired outcomes using proposed remediation strategies. 
4. Monitor & control systems with processes that flag and resolve future disparities as the AI system evolves. 

To confidently scale market-shaping AI, generate trust and steer clear of any unwanted consequences along the way, we all should embrace responsible AI from the first moment and every moment after that. 

Explore our AI ethics & governance insights to learn more.