This article was updated on March 10, 2026, to reflect the latest information.
TL;DR: AI ethics for leaders is about setting clear policies for how your organization uses AI — and holding your vendors accountable to those same standards. With over half of workers now using AI on the job and regulations like the EU AI Act taking effect, leaders need to address bias, transparency, and privacy head-on. This guide covers how AI bias works, what questions to ask your software vendors, and five concrete steps to build an ethical AI framework at your organization.
AI ethics for leaders doesn’t have to be scary or overly technical. We’re all navigating a workplace that’s changing fast because of emerging technology, but at its core, ethics are about people — and AI ethics is about how people use this new technology.
How do leaders build frameworks that reduce the chances of AI working against people instead of with them? Here's a practical guide with recommendations for policies and implementation.
How are people using AI in the workplace?
Developing good AI ethics for leaders starts with understanding how AI is already showing up at work.
Most employees interact with AI at work — including growing numbers who seek out tools like generative AI such as ChatGPT to improve their daily work. AI affects us in more passive ways, too. Ever clicked suggested posts on social media? Applied for a home loan? AI algorithms shape our decisions, and we don’t always realize it.
If you’ve applied to a job in the last five years, chances are AI made a decision about you somewhere along the pipeline. Applicant Tracking Systems (ATS) scan thousands of resumes and narrow candidates based on scoring metrics set by HR departments. A missing phrase could move you into the rejection pile, or the right keywords could catapult you into an interview.
Maybe you’ve worked with ChatGPT yourself. According to PwC’s 2025 Global Workforce Survey, 54% of workers have used AI for their role in the past 12 months — though only 14% use it daily. According to Harvard Business Review, employees use generative AI for two primary reasons: content creation and editing, and troubleshooting and technical assistance.
Doesn’t sound so bad, right? But how far can we trust AI? Half of Americans say they’re more concerned than excited about the increased use of AI in daily life — a figure that's been climbing steadily since 2021. Building trust is where AI ethics for leaders comes to the forefront.What AI ethics really means
Ethos is defined as the “guiding beliefs of a person, group, or institution.” That’s exactly what AI ethics for leaders aims to do — guide your organization in alignment with your business principles. AI ethics has to be a collaborative, team process. Open discussion across all levels should lead to concrete policies.Your AI ethics policies should shape two things. First, how AI is used in your organization. Second, your feedback to vendors about development preferences. Now let’s talk about the why.
How AI can help — and how it can hurt
AI technology can empower people, but it also has the potential to widen existing inequalities. Whether it’s generative AI platforms or SaaS programs like your ATS, you can’t assume software companies hold the same ethical considerations as your organization. AI is developed by humans, and not all technology is created in the same vacuum.
Fairness and bias
As you work on your AI ethics framework in 2026, start by addressing the bias baked into the technology.
How bias gets into AI. The dominant type of AI right now is machine learning (ML), where algorithms learn from massive amounts of existing data and predict outcomes based on that knowledge. ML is dependent on the data fed to it — and using real-world data means human bias is embedded in the data sets. If you use AI to determine what neighborhoods get policed for potential crime, it will be driven by biases that already exist in society.
Real life example. In January 2020, Detroit police arrested Robert Williams in his driveway based on AI-powered facial recognition identifying him as a match for security footage from a theft. Williams, a Black man, became history’s first person wrongfully arrested based on facial recognition. The technology wasn't adequately trained to accurately distinguish an innocent Black man from the suspect. Programs like these can carry intrinsic discriminatory biases from their developers or development process.Transparency, robustness, and privacy
Transparency is key to reducing or eliminating the possibility of negative incidents like this in the workplace. Leadership is responsible for tough decisions about organization-wide AI use and regulatory policies.
Interrogate your processes: Do you tell people — internally and externally — when AI is used? Do you tell them how it’s being used? Can they find out more after being made aware?
Be careful not to put the burden on the candidate or employee. Dense legal documentation — like most cookie consent policies — is not transparent when it comes to AI ethics for leaders.
Robustness. Can your AI technology be willfully manipulated? Can it be used to intentionally benefit one person or group of people over others? Should you limit the amount that technology replaces traditional human decision-making in the company?
Privacy. What happens to the information you feed ChatGPT? Or the information candidates feed your ATS? Where is it stored, and who has access? Publish a privacy disclosure on your candidate portal and make sure everyone with access to that data has been trained on proper privacy techniques.5 steps to build your AI ethics framework
Organization-wide value alignment is the goal when it comes to AI ethics for leaders. Here are concrete steps you can take.
1. Bring in an AI ethics expert
Train your teams about AI by bringing in an expert. Your staff has to understand how AI and ML work — this is basic risk management. An educated employee can interrogate the system and help identify gaps and risks.
2. Start a dialogue
Encourage open debates, questions, and feedback about AI in your workplace. Establish a cross-functional work group that represents diverse people, ideas, teams, and levels of your organization — or call up an existing employee resource group (ERG). These conversations are invaluable for building the foundation for the principles you establish about AI ethics.
3. Ask your vendors the right questions
If your company has purchased or will purchase software featuring AI, everyone needs to understand exactly how it works. Bring in the software vendor to explain the technology. Start by asking:
- How can you ensure that this AI is fair to historically underrepresented groups?
- Does the data represent all possible scenarios and users without bias? How?
- Can you tell us what methods and data sets were used to train the model?
- Who is on your AI development team? Is the team diverse or disproportionately White and male like the majority of the tech industry?
- Can your AI be manipulated to favor one group or profile?
Software vendors must be able to answer these concerns clearly. Give them time to reach out to the developers if necessary. You can also get ahead of it by seeking out AI-powered technologies developed with diversity and inclusion at the forefront.
4. Conduct an AI audit
Take stock of every AI tool your organization uses — from your ATS to generative AI platforms. For each tool, document what data it collects, how it makes decisions, and who reviews its outputs. Regular audits help you catch bias or privacy issues before they become incidents.
5. Stay ahead of regulation
AI regulation is picking up speed globally. The EU AI Act — the world’s first comprehensive AI law — classifies AI used in employment and hiring as high-risk, with full requirements enforceable by August 2026. These rules apply to any AI system whose output is used in the EU, regardless of where your company is based. U.S. states are also introducing their own AI transparency laws. Staying informed now saves you from scrambling later.
Leadership is built on trust
AI ethics and policies shouldn’t be decided from the top down. It requires collaboration from the diverse people at your organization. As a leader, it’s your job to understand how AI is being used, share that information at all levels, and hold AI software companies accountable to your values around inclusion. If AI is going to be trustworthy tech, you hold the power to push for those products to be developed ethically.
Accountability starts with leaders like you. The AI software marketplace is vast, and you can afford to use tech that complies with your organizational AI ethics policies and aligns with your company values.
Frequently asked questions
What are AI ethics?
AI ethics is a set of guiding principles that shape how an organization develops, deploys, and monitors artificial intelligence. It covers fairness, bias, transparency, privacy, and accountability — making sure AI tools work with people, not against them.
Why should leaders care about AI ethics?
Without clear AI ethics policies, companies risk reinforcing bias in hiring, violating employee privacy, and losing trust with candidates and customers. Clear policies also help organizations stay ahead of emerging regulations like the EU AI Act.
How can I tell if my company's AI tools are biased?
Start with an AI audit. Review what data each tool was trained on, how it makes decisions, and whether its outcomes disproportionately affect certain groups. Ask your vendors directly about their testing for bias, and establish regular reviews — especially in hiring and performance evaluation.
What is the EU AI Act, and does it affect my company?
The EU AI Act is the world’s first comprehensive AI regulation. It categorizes AI systems by risk level, and AI used in employment and hiring is classified as high-risk. If your AI system's output is used in the EU, these rules apply regardless of where your company is headquartered.
Where do I start with AI ethics at my organization?
Bring in an AI ethics expert to educate your team, then conduct an audit of your current AI tools. Start a cross-functional dialogue with employees at every level, and develop clear policies for AI use, vendor accountability, and ongoing monitoring.
Looking for more information about AI and inclusion working together? Check out this PowerUp course!
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