AI is now part of many organizations’ day-to-day operations, but using it responsibly is harder than many teams realize.
Three patterns consistently surface: bias buried inside the system, reasoning no one can fully reconstruct, and AI accountability that turns murky when something goes wrong.
As the founder and principal attorney at ZeroDay Law, I discuss what responsible AI looks like in the latest episode of my podcast, Decoding Cyber Law. Here are the highlights.
Prefer to listen to the 10-minute podcast? Tune in here.
AI Bias You Can’t See
AI bias comes in two forms that often get tangled together.
Algorithmic bias carries the baked-in assumptions of the people who built and trained the model. Those biases are often reflected in how systems are designed and trained, and eliminating them entirely is unrealistic.
Data bias occurs when the data used to train the AI algorithm is incomplete and unrepresentative of the real-world situations the system will encounter. AI can keep reinforcing patterns that no longer apply, including old prejudices, retired business models and policies that are no longer in use.
Here are some basic examples of how bias can appear in AI output:
-
AI engines can associate “nurse” with women and “doctor” with men.
- When doing industry research, your results unknowingly come from global companies based in a few countries, as opposed to worldwide.
The Importance of Bias Review
Bias doesn’t start with AI. It shows up in the data, assumptions and decisions that shape AI tools and systems. The more useful approach is to expect limitations and build in review processes that catch issues before they affect outcomes.
Bias screening on the output side is a practical way to address this. Assume the engine is imperfect. Assume the training data is incomplete. Then build mechanisms to confirm the output is accurate and legitimate before anyone acts on it.
Going deeper on choosing tools where these risks are easier to manage? Read the blog: Choosing Compliant AI Tools: What Legal and Privacy Teams Must Know.
The “Black Box” Problem in AI Transparency
In some cases, even the people who build and train AI systems can’t fully explain how a specific output was generated. The pressure to develop more rapidly than ever results in less rigorous software development lifecycles (SDLCs).
This lack of visibility is often referred to as the “black box” problem, and regulators have been paying attention. Users should be able to explain how a given system arrived at an output. If an explanation isn’t possible, it becomes your black box, along with the responsibility that comes with it.
Model cards are designed to close gaps between how a system works and what users can actually understand about it. A model card documents training and decision-making processes. A model card gives legal, privacy and security teams a basis for evaluation before approving an AI tool for company use.
Impunity and Why Accountability Has to Sit With a Person
In the first episode of Decoding Cyber Law, I talked about why AI makes people uneasy. Some worry that AI might act with impunity if it operates in ways that affect people’s rights or safety without anyone being clearly accountable for those outcomes.
But it’s critical to remember that the use of AI doesn’t change who is responsible. Organizations remain legally accountable for the output of any AI tool, even when it is embedded inside a workflow.
The working concept here is human in the loop (HITL). With HITL, automation is supported by human review, which helps ensure decisions are checked before they affect rights, safety or accuracy.
Pulling AI-based concerns into something usable is the next step. For most organizations, the starting point is looking deeper into existing procedures and policies.
Begin With Your Existing Policies
The good news is that you often don’t need to draft new policies from scratch.
The work is more about extending what you already have, so it covers the AI tools in active use across your organization, including the AI features quietly added to platforms you already license and how vendors are using AI in connection with your data.
Take a look at your existing policies and procedures covering data handling, vendor management, acceptable use, and confidentiality. It is within these documents that responsible AI use should get operationalized first.
We’ll get into policies in more depth in a future episode.
Our blog post Data Privacy Laws: 6 Best Practices Every Business Should Know can get you started.
Putting It Together
AI risk shows up across the full lifecycle of a system, from design to output. You can explore why oversight, transparency and accountability matter at every stage in Episodes 1 and 2 of Decoding Cyber Law.
If a regulator, board member or auditor asked tomorrow how your organization manages AI, would you have an answer? ZeroDay Law can help you prepare that answer — reach out today to learn more.