What are Ethical Frameworks?
The other day I read a blog by Lizzie Short at Harvard University about responsible AI and ethical AI. Responsible AI focuses on accountability, transparency and compliance with regulations, while ethical AI — sometimes referred to as AI ethics — emphasizes broader moral principles like fairness, privacy and societal impact. Responsible AI and ethical AI work hand in hand. By combining both approaches, “organizations can build and deploy AI systems in ways that are not only legally sound, but also aligned with human values and designed to minimize harm.”
Ethical AI refers to the values and moral expectations governing AI use. These principles may change over time so that what is acceptable at ay point of time can differ by culture and country. However, as Lizzie Short notes, “many ethical principles, such as fairness, transparency and avoidance of harm, tend to be consistent across regions and over time.”
Organizations have expressed interest in ethical AI by developing ethical frameworks to serve as a foundation for ethical principles. Ethical AI helps to define the contours of an AI social contract by establishing what is and isn’t acceptable. AI ethics frameworks often serve as a precursor to AI regulation, although some regulation is emerging alongside or even ahead of formal ethical frameworks.
What Is Responsible AI?
Responsible AI is a term used to describe how businesses deploy AI tactically. Businesses using AI responsibly are focused on fairness and mitigating any biases in the technology. As AI development accelerates, a clear framework to guide AI usage is essential.
Michael Impink, an instructor from Harvard Division of Continuing Education’s Professional and Executive Development division, explained it this way:
“Responsible AI means you’re paying attention to fairness outcomes, cutting biases, and going back and forth with the development team to remediate any issues to make sure the AI is appropriate for all groups,” he says.
Impink, who teaches A Ethics in Business says there is not yet a one-size-fits-all solution to adopting a responsible approach to AI.
“It depends what you’re doing with AI, how central it is to your business,” Impink says. “Banks, hospitals, or other organizations in regulated industries will need to make sure their AI works well for all groups — across race and gender, for example — whereas those concerns may not be top of mind for a company in the unregulated product market.”
What Is Ethical vs. Responsible AI?
The terms “ethical AI” and “responsible AI” are often used interchangeably, but there are key differences between the two, as explained by Short.
Ethical AI refers to an approach to AI that is philosophical and focused on abstract principles (like fairness and privacy) while also examining the broader societal implications of widespread AI usage. For example, researchers investigating AI’s impact on a community or its potential for workforce disruption are examining AI ethics.
Responsible AI is more narrowly focused on how AI is being used. AI responsibility deals with issues related to accountability, transparency, and regulatory compliance. For example, in a medical research setting, a responsible AI framework would ensure there was sufficient transparency into the AI algorithm to understand and eliminate any biases.
Organizations that use AI ethically follow five key principles: fairness, transparency, accountability, privacy, and security. These principles outline the best ways to limit an organization’s exposure to the risks associated with AI, as discussed n Short’s blog that follows.
Principle #1: Fairness
Fairness in AI relates to the output of the AI. To be “fair” in this sense means the outputs match a fairness criterion. Organizations that want to ensure their AI is delivering fair outcomes across protected classes will need to build models that appropriately weigh different criteria for different groups, which may include race, gender, or religion, making it more difficult to create fair outcomes across groups.
“There’s a trade-off between privacy and transparency,” Impink says. The more transparent the data, the easier it is to get a fair outcome — but this could infringe on an individual’s right to privacy. Organizations that want to ensure fairness in their AI algorithms need to develop a robust fairness criterion across protected classes and other social groups.
Principle #2: Transparency
If fairness relates to the outcomes of using AI, transparency is knowing what goes into an algorithm. Creating transparent AI tools helps ensure the tool is unbiased, which is critical for delivering accurate outcomes. The algorithm itself could be biased, overweighing a certain kind of data.
Organizations can ensure their AI framework is transparent by having programmers consider diverse perspectives when building the tool and conducting rigorous bias testing. Organizations would also benefit from having an AI bias expert on staff who can closely monitor outcomes against the algorithm to determine areas of bias. This expert could also examine training materials to make sure they draw from broad, unbiased sources.
“There’s a trade-off between privacy and transparency. The more transparent the data, the easier it is to get a fair outcome — but this could infringe on an individual’s right to privacy., says Impink.
Principle #3: Accountability
Accountability in AI means someone needs to be held accountable for the outcomes of AI produces. AI itself cannot experience consequences, so organizations need to build a solid framework defining who will be held responsible for the AI. As an IBM training manual from 1979 puts it: “A computer can never be held accountable. Therefore, a computer must never make a management decision.”
Organizations can establish clear hierarchies outlining responsibilities for each AI element. A clearly delineated structure will help determine who will be held responsible if something goes wrong.
Principle #4: Privacy
Privacy in AI relates to keeping the data AI uses secure. Personally Identifiable Information (PII), such as someone’s name, Social Security number, address, or phone number, must be kept private to protect individuals from fraud and identity theft. When it comes to AI, privacy and security are closely linked, and organizations must be in compliance with data privacy laws
“Security is what makes privacy work,” Impink says. “Without it, people would just steal your data.” Organizations can ensure their AI framework protects user privacy by establishing a strong security system to keep PII safe within their AI tool.
Principle #5: Security
To maintain privacy, organizations have a responsibility to keep user data secure. When using AI, that security means protecting internal, private data from external attacks or internal corruption.
“If systems are designed without ethical constraints, they can harm humans and reproduce or amplify biases that already exist in society,” she said.
The Pursuit of Ethical AI
Early discussions of ethical AI were driven less by compliance requirements and more by a sense of responsibility, including how AI systems could reinforce existing inequities or produce unintended outcomes. Regulation later formalized these expectations, providing businesses with a standardized framework as AI moved into real-world applications, according to Patrizia Bertini, founder of Euler Associates, a UK consultancy specializing in operationalizing compliance for digital products.
Many organizations pursue ethical AI primarily due to regulation, legal risk or reputational pressure. However, when core ethical principles are embedded across the AI lifecycle, ethics has the power to guide everyday decisions and culture. Yet, too often organizations treat an ethical approach as a checkbox , doing the minimum rather than fully embedding it in their processes, workflows and culture.
“What it takes to achieve ethical AI isn’t a mission statement,” Bertini said. “It’s design choices, governance choices, operational practices and core ethical AI principles that prevent harm before systems are deployed and keep preventing harm as systems evolve.”
AI operating without ethical oversight can result in wrongful denials of service, surveillance overreach and other harms, triggering public backlash and regulatory scrutiny. Leaders often underestimate how difficult it is to undo AI mistakes. “Unlike traditional software,” according to Jill Knesek CISO at BlackLine, “systems trained on flawed or biased data can’t simply be fixed after deployment, raising the cost of ethical failures.”
This isn’t just a technical issue, it’s also a leadership one, she added. “When AI risk isn’t integrated into enterprise risk management, ethical failures become business failures,” she said. “Boards need to treat AI oversight with the same seriousness as cybersecurity or financial controls.”
The pursuit of AI is an ongoing endeavor because challenges arise depending on the culture of an organization and whether top management sets an ethical tone at the top.
Blog posted by Dr. Steven Mintz, professor emeritus from Cal Poly San Luis Obispo, on September 15, 2026. You can communicate with Steve at: smintz@calpoly.edu. Visit his website to find out more about his activities.